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Data Analysis AI Tools
Discover and compare the best data analysis AI tools and software. Browse 60+ curated tools with reviews and rankings.
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Voxel51 is an end-to-end multimodal data platform built to accelerate robot learning. The real world is messy, and robots encounter unexpected situations and behave in ways their builders never anticipated; Voxel51 is designed for the teams behind those robots and for the video and sensor data those machines produce. Its stated purpose is to help teams find the failures that matter, understand why they happened, and fill gaps in their training data so models can be improved faster. Rather than treating robot data as an undifferentiated archive, the platform is positioned as the place where recorded robot behavior becomes usable evidence for improving the robot itself. The thinking behind Voxel51 starts with a simple premise: every failure is a chance to teach a robot something new. What stands in the way is not the failure itself but the effort required to learn from it. Robots generate hours of video and sensor data, and when a robot does something unexpected, the hard part is sifting through all of that material to figure out why the failure occurred. Without a way to locate the relevant moments and interpret them, the lesson stays buried inside long recordings, and the gap in the robot's understanding never gets closed. Voxel51 addresses that bottleneck by organizing the work around three connected steps that the company repeats in its own tagline framing: find the failures that matter, understand why they happened, and fill the gaps in training data. The first capability Voxel51 emphasizes is finding the failures that matter. Because robots produce hours of video and sensor data, the challenge is not a shortage of material but the difficulty of locating the specific moments that explain a problem. Voxel51 is positioned as the layer that lets teams move through that volume of recorded behavior and isolate the failures worth acting on, instead of treating every minute of footage as equally relevant. For a robotics team, that means the difference between reviewing blind and starting from the episodes that actually explain why the robot behaved in a way they never anticipated. The second capability is understanding why a failure happened. Voxel51 describes itself as a multimodal data platform, and it explicitly works across video and sensor data rather than a single stream. That matters because a robot's behavior is only fully explained when the visual record and the sensor readings are considered together. Once a failure has been located, the platform gives teams the material to examine the situation and work out the cause, so that the response is based on what actually happened during the episode rather than on guesswork about what the robot might have encountered in the messy real world. The third capability is filling gaps in training data. Once a team understands why a robot failed, the next step is to close the gap that the failure exposed, so that the model has the examples it needs to handle the situation next time. Voxel51 frames this as filling gaps in training data and improving models faster. The three capabilities are intentionally connected: the failure points to the gap, the diagnosis explains the gap, and the training data fills it. Because all three steps live inside one end-to-end platform, teams do not have to move data between disconnected stages to get from a recorded failure to a better model. Taken together, Voxel51 describes an end-to-end approach: the platform starts with the raw video and sensor data a robot already produces, helps teams surface the failures that matter, supports understanding why those failures occurred, and then supports filling the corresponding gaps in training data so the model can be improved. The product is delivered as a web application. The Voxel51 app presents dataset views, such as a robotics-droid samples dataset, and the application exposes prompt-style queries over those datasets, as illustrated by an example request for all episodes where the robot is folding clothes. Access to the app is passwordless: users enter an email address, receive a one-time verification code, and sign in with that code instead of choosing a password. Trying Voxel51 is free, and no credit card is required. The outcomes Voxel51 presents are practical and stated directly. Teams can find the failures that matter rather than sifting through every hour of video and sensor data by hand. They can understand why those failures happened, which is the step the company identifies as the hard part of the process. They can fill gaps in their training data, and in doing so improve models faster. The repeated framing, "Better data. Smarter robots. Repeat.", captures the loop the platform is meant to support: the robot's real-world experience becomes better data, better data produces a smarter robot, and the cycle runs again as the robot encounters new and unexpected situations. The concrete scenarios in the content center on robot learning from real-world behavior. A team discovers that a robot behaved in a way it never anticipated and needs to work out why, so it turns to the recorded video and sensor data rather than relying on assumptions. A team notices a pattern of unexpected behavior and needs to locate the specific episodes that explain it within hours of footage. A team identifies a gap in its training data and needs to fill it so the model can be improved faster. And a team working with a robotics dataset, such as the robotics-droid samples, can query episodes directly, for example asking to show all episodes where the robot is folding clothes. Voxel51 is built for the people who train and improve robots: the description speaks directly to teams whose robots encounter unexpected situations and behave in ways they never anticipated, and who must sift through hours of video and sensor data to understand failures. The platform is multimodal, working with video and sensor data, and it is delivered as a web application available through sign-in. On pricing, the content states only that Voxel51 can be tried for free and that no credit card is required to do so; no further plan details are given. The primary value proposition is straightforward: Voxel51 turns the messy, unexpected behavior of real-world robots into an opportunity to teach them something new, by providing one end-to-end multimodal data platform where teams find the failures that matter, understand why they happened, and fill the gaps in their training data, improving models faster and repeating the cycle.
Overmind describes itself as the training platform for specialized agents and, more specifically, the model training platform for AI teams. Its stated purpose is to turn an agent's production data into specialized models that the team owns, with data preparation, training, evaluation and deployment handled inside one platform. The website frames the product as an AI lab for your engineering team, built around the observation that you do not need a frontier model for every task: teams can train smaller models for the work their agents actually do, from labeling emails to calling APIs, and measure those models against the model they use today. Overmind provides a Console for getting started and is open source, with its code available on GitHub. The site positions Overmind against a concrete set of problems. First, agent failures are hard to understand: Overmind's observability section promises to help teams find the cause of agent failures by looking at both code and production context. Second, production work itself is a source of knowledge — real traces contain the knowledge specific to a business, but that knowledge has to be cleaned, filtered and prepared before it can be used as training and evaluation data. Third, improvements need to be proven rather than assumed, which is why the platform includes evaluation against real tasks from production data and regression checks before deployment. Overmind presents its foundations as engineered rather than fragile wrappers, noting that it was founded by security engineers with combined decades of experience across defense intelligence, ML infrastructure and fintech hyper-scaling. The first stage in the platform is code and production context. Overmind lets you connect your repository and your production traces so you can see how your agent makes decisions. When an agent fails, you can inspect the failing step, the tool call, and the context behind it instead of guessing from logs alone. The website illustrates this with an example flow: a repository scan expands into the agent page, discovers capabilities, and opens a capability's full decision graph. This decision-level view is the starting point for everything else in the platform, because the same traces that explain a failure also become the raw material for datasets, evaluations and training runs. Data preparation happens in the Data Workshop, which the site describes as turning real work into training data. You build datasets from your agents' production traces, then clean, filter and prepare examples for training and evaluation, capturing the knowledge specific to your business. The illustrated flow shows production traces opening in Data Workshop, with notebook cells appearing as they clean and prepare training examples, suggesting a notebook-style, step-by-step workspace for shaping data. The purpose is to convert everyday operational activity into curated examples that reflect how the business actually works, rather than relying on generic data that does not capture the team's own workflows. Evaluation and optimization is the third stage, summarized on the site as proving improvements before you ship. Overmind lets teams test prompts and models against real tasks drawn from production data, compare results with the current model, inspect failures, and catch regressions before deployment. The site's illustration zooms from a capability page into a model call and its evaluation gates and judges, then returns to the full decision graph. In practice this means an evaluation is tied to the same agent capability being improved, so a team can see whether a candidate prompt or model actually performs better on the work that matters, and can see where it still fails. The fourth stage is training and deployment. Overmind lets you fine-tune an open model on your own data, handles training and deployment for you, and leaves you owning the weights, with the choice to host the model on Overmind or run it yourself. Taken together, the four stages describe a single loop: production traffic is captured and inspected, prepared into datasets, used to evaluate candidate prompts and models, and finally used to train and serve a specialized model. The training setup is presented as opening into a live run dashboard that follows training progress through to a deployed model, so the same platform carries a team from a raw trace to a model running in production. Overmind emphasizes enterprise-grade security and describes three foundations. It is open source and self-hostable: you can run the platform on your own infrastructure with Docker Compose, and the code is AGPL-3.0 on GitHub. It uses zero third-party training, meaning proprietary operational data never leaves your pipeline to train public foundation models. And it provides deterministic validation, where advanced verification harnesses ensure model outputs are empirically tested before deployment. These points matter for teams whose agent traces contain sensitive operational detail, and they explain why self-hosting is presented as a first-class option rather than an afterthought. The stated benefits follow directly from the workflow. Teams can find the cause of agent failures by inspecting the failing step, the tool call and the context behind it. They can turn real work into training data that captures business-specific knowledge. They can prove improvements before they ship, comparing results against the model in use today and catching regressions early. And they can end up owning the weights of a model trained for their tasks, hosted on Overmind or run themselves — together with the claim that a frontier model is not required for every task, which is the argument the site makes for training smaller, specialized models. The site names concrete kinds of work as examples: labeling emails and calling APIs are the tasks it uses to describe what agents do and what smaller models can be trained for. Beyond that, the described workflows map to common engineering scenarios — investigating why an agent took a wrong step in production, assembling a dataset from live traces, evaluating a prompt change against real tasks before release, catching a regression introduced by a model or prompt update, and fine-tuning an open model on business-specific examples for deployment. Each of these uses the same production traces as the underlying source. Overmind is aimed at AI teams and engineering teams building agents, and its security posture speaks to organizations with sensitive operational data — the founders' backgrounds are given as defense intelligence, ML infrastructure and fintech hyper-scaling. Access starts through the Console, the site invites visitors to create a free account and start for free, and offers a talk-to-the-team path for those who want to engage directly. Teams that need to keep data in-house can self-host with Docker Compose under AGPL-3.0 instead of using the hosted console. In short, Overmind's value proposition is ownership: a single platform where an AI team turns its agents' production data into specialized models it owns, prepared, trained, evaluated and deployed in one place, with open-source, self-hostable foundations and deterministic validation behind it.
Tapetide is an AI-first stock research platform built for Indian equities. Instead of clicking through a dozen screens, you ask TapeAi in plain English (for example: "compare HDFC Bank and ICICI Bank on ROE and valuation") and it pulls the numbers, reads the filings and lays out the answer with charts and tables.What you get:Every NSE and BSE stock (8,000+): price history since 2000, 12 years of financial statements, quarterly results, shareholding patterns, key ratios, technical indicators, analyst ratings and forecastsA 326-ratio stock screener with AND/OR filters and live technical signalsMarket dashboards: FII/DII flows since 2014, IPOs, bulk and block deals, F&O ban list, MTF, market valuation, mutual fund holdings, heatmapsParsed corporate filings: annual reports, concall transcripts, credit ratings, promoter pledgesTapetide MCP server: one remote URL (https://mcp.tapetide.com/mcp) gives Claude, ChatGPT, Cursor, VS Code, Windsurf, Codex, Gemini CLI and other MCP clients 55 tools for Indian market data. Google sign-in or API token, nothing to install.Free plan forever (50 MCP tool calls/day, screener, FII/DII data). Paid plans start at ₹299/month with a 7-day free trial.
Spinachi is a personal health manager that runs on WhatsApp. Rather than asking you to install and learn yet another app, it meets you in the messaging app you already open every day: you forward your lab reports to Spinachi on WhatsApp, and it reads them, sorts them, and keeps your health records — and your family's — in one place. From there you can see where you stand, how things are changing over time, and when your next test is due. Spinachi describes itself simply as "your health, finally in one place," and it is explicit that it is built for organising and tracking health information, never for issuing a diagnosis. The problem Spinachi addresses is a familiar one: health records do not stay in one place. A blood test arrives as a PDF in a family WhatsApp group, a thyroid panel is emailed by a lab, a knee MRI lives on a hospital portal, and vaccination slips sit in a drawer. When you want to know what your last reading was, whether things have changed since the previous test, or when you were last screened, you have to hunt through messages, downloads and paper folders. Multiply that by every member of a household and the picture gets messier still. Spinachi's premise is that the answer should live somewhere you already are, and that the record-keeping should happen for you rather than being another chore you have to maintain yourself. The delivery channel is the product's defining choice. Spinachi works through WhatsApp, so there is no app to download, no account to keep logging into, and no new interface to learn. You sign up for Spinachi and then simply forward your reports as you receive them. Because the entry point is a messaging thread, the workflow fits the way people already share medical documents with their families — the same PDF that lands in your chat can be passed straight to Spinachi instead of being lost in a scroll of messages. Once a report arrives, Spinachi reads it and sorts it. The website shows this organisation in action, with health records gathered together and individual files filed alongside labels such as Lab report, Prescription, Scan report, and Full body checkup. Example documents used on the site include Blood Test 2025.pdf, Knee MRI.pdf, Thyroid Panel.pdf, and Chest X-ray.pdf, sitting under folder-style headings for Health Records, Prescriptions, Lab Results, and Vaccinations. The point is not just storage but structure: instead of a pile of attachments, you get categorised records that can be found again when you need them. Beyond filing, Spinachi is designed to help you understand where you stand. It keeps your records in a form that shows how things are changing over time, so that a single reading stops being an isolated data point and becomes part of a pattern. It also tracks timing: Spinachi reminds you when a retest is due, so follow-up tests do not quietly slip past. This combination — a stored history, a view of change, and a nudge for the next test — is the core of what the product offers. The overall approach is deliberately minimal. You forward a lab report on WhatsApp; Spinachi reads it; Spinachi sorts it for you and your family; the record is kept in one place. There is no app to install, and there is no diagnosis. Spinachi states plainly that it never provides a diagnosis, which keeps its role clearly defined: it is a manager and organiser of the health information you already have, not a source of medical opinion. That boundary matters, because it means the product can be useful for storing and tracking records without making clinical claims. For users, the benefits follow directly from those choices. Records for the whole family end up in a single place rather than being distributed across chats, inboxes and drawers. You can see where you stand and how things are changing, which makes it easier to arrive at an appointment with context rather than guesswork. Retest reminders reduce the chance that a recommended follow-up is forgotten. And because everything happens inside WhatsApp with no app, the effort required to keep the record up to date is close to zero — the act of forwarding a file is the act of filing it. Concrete scenarios follow directly from the document types shown on the site. After a full body checkup, you forward the lab report and Spinachi files it so the results are not lost among other messages. If a household member has a thyroid panel done, the record joins a lab results history that can be compared against earlier tests. Scan reports such as a knee MRI or a chest X-ray are stored alongside the rest of the family's records instead of sitting in a hospital portal. Prescriptions are kept in their own place, and vaccination records are collected together so the household's paperwork is complete. Spinachi is aimed at individuals and families who want their health records organised in one place. Access is through WhatsApp, and getting started means signing up at spinachi.in/start. The service is presented without a download, which makes it approachable for people who do not want to manage another piece of software, and for households where one person ends up holding everyone's paperwork. The takeaway: Spinachi turns WhatsApp into a personal health manager. Forward your lab reports, and they are read, sorted and stored with your family's records in one place. You see where you stand, how it is changing, and when your next test is due — with no app to install and never a diagnosis. The value proposition is straightforward: your health, finally in one place, maintained with the smallest possible amount of effort.
The Drive AI is an AI file assistant that organizes, searches, and creates documents in plain English, in its own workspace or in connected Google Drive, OneDrive, SharePoint, and Dropbox accounts. The website describes it as the AI assistant that organizes and searches your team's files: you connect the drives your company already uses, and the assistant reads and organizes them in place, so nothing has to move. It is built for teams and individuals who want to stop filing by hand and instead ask for what they need in a single sentence, whether that is sorting a folder, finding a clause, sharing a report, or asking a question across thousands of files. The problem it addresses is one almost every team recognizes. Files are usually named by whoever happened to save them, which is why the homepage points at collections full of scan_0412.pdf and Final FINAL v3.docx, where the filename tells you nothing about what the document actually contains. Once a drive reaches that state, search stops being reliable, duplicates accumulate, and the only dependable way to find anything is to open files one at a time. The Drive AI's answer is to read every file by what it says rather than by what it is called. As the Product Hunt description puts it, most AI tools can read your files; The Drive AI does the work after reading. Rather than stopping at summarizing or chatting about a document, it takes the next step and reorganizes, renames, merges, shares, and sends files on your behalf. Organizing is the first job the assistant takes on. You can ask it to sort every invoice by client and year, to sort a year's client documents by entity and fiscal year, or to rename every invoice that lands in Finance to vendor, date, and amount. It also finds duplicates and can organize thousands of files in a single prompt. Crucially, it proposes the work as a plan rather than mutating a drive silently: a sample request to sort the Clients folder by client and year produces a proposal showing 1,264 changes and 24 new folders, flagged as needing your OK. For accounting firms the same plan covers every file at once, and an undo puts it all back. This plan-first approach is what makes a large-scale reorganization of thousands of files practical rather than terrifying. Alongside organizing, the assistant answers questions across the entire collection rather than one document at a time. You can ask which vendors raised prices this year, and it reads the files to find out. In the real estate example, asking which invoices for 14 Elm Street came in over $2,000 this year makes it read every invoice for the property and answer with a table. For law firms, asking which NDAs renew in the next 90 days makes it read each agreement and list them with their notice periods. That turns a folder of unstructured PDFs into something you can interrogate directly, without opening anything yourself. The assistant also creates and acts on documents, not just sorts them. It can turn the Q3 board notes into a two-page PDF summary, merge three contracts into one PDF, and build a new document such as a closing binder from 20 PDFs. In the human resources example, asking it to split Priya's onboarding packet makes it split the PDF, name each form, and file them under the employee. It handles sharing too: you can ask it to share the Q4 report with Sarah, view-only, or send the Acme NDA to both signers. The workspace that ships on every plan adds version history, permissions by file and folder, file requests from anyone, and e-signature, so document workflows that normally span several tools can happen in one place. The website summarizes the workflow in three verbs: connect it, ask it, approve it. In the connect step, you link Google Drive, SharePoint, OneDrive, Dropbox, or upload files directly, and every file is read by what it says rather than what it is called. In the ask step, one sentence is enough to sort a folder, find a clause, share a report, or ask a question across thousands of files. In the approve step, it shows the plan: small changes apply at once, big ones wait for your OK, and everything can be undone. Three explicit rules govern this. Small changes apply at once and can be undone. Large changes, and anything sent to another person, wait for your OK. Admins decide where that line sits for the whole workspace. On top of that, workflows let you describe a job once and have it run on every file that arrives afterward, wherever it arrives from; you see every step before it runs, and any run can be replayed. An activity log records every change, who made it, and who it was for, including restores, moves, shares, and renames. The benefits follow directly from that design. Work that used to mean hours of dragging, renaming, and cross-referencing becomes a single sentence and an approval. Renames like scan_0412.pdf becoming Acme MSA 2024.pdf mean files are findable by the next person, not just by the person who saved them. Questions that previously required opening dozens of PDFs are answered with a table. Because the assistant reads and organizes files in place, nothing has to move between systems. And because every change is visible, approvable, and reversible, teams can let an agent act on real business documents without losing control of them. The product is explicitly framed around document-heavy industries, with concrete scenarios for each. Real estate teams use it to read every invoice for a property and answer spend questions with a table, and to file documents from different senders under one address in the order the deal happened, so a listing agreement, purchase contract, and inspection report all land in the right place. Law firms use it to read each agreement and surface NDAs renewing in the next 90 days with their notice periods. Accounting firms use it to sort a year's client documents by entity and fiscal year in one approved plan. Property management teams use it to put every lease under its property and unit, named with tenant and start date, proposing the moves and renames as a single change. HR teams use it to split onboarding packets and file each form in the right employee folder. Finance teams use it to rename every invoice that lands in Finance to vendor, date, and amount, and to organize invoices by client and year. New files can arrive from uploads, Gmail, Outlook, Slack, and Microsoft Teams, while the connected drives it works on are Google Drive, OneDrive, SharePoint, and Dropbox. Files can alternatively live in The Drive AI's own workspace, which is on every plan, while connected drives require the Max or Team plan. The product is free to start, with a book-a-demo option through Cal.com. On trust, the site states that data is encrypted, scanned for malware, and never used to train AI, and lists CASA Tier 2 certification, Microsoft Verified Partner status, AES-256 encryption, real-time malware detection, and enterprise infrastructure. A Chrome extension lets you use it right inside your existing drive. The takeaway is simple: The Drive AI positions itself as the assistant that does the work on your files rather than just reading them. Find anything, file nothing by hand — connect a drive or upload a folder, ask in plain English, approve the plan, and let every new file be read and filed where it belongs.
EmbeddingGemma 2 is an open, lightweight multimodal embedding model from Google DeepMind. It natively maps combinations of text, code, images, audio, and video into a unified embedding space, so a single query can search across all of them. The model is intended for developers building on-device applications: apps that organize, search, and connect information directly on consumer hardware. Released under the commercially permissive Apache 2.0 license, EmbeddingGemma 2 has 740 million parameters, making it optimal for on-device inference, and its model weights are available on Hugging Face and Kaggle. The original EmbeddingGemma was introduced to give developers a lightweight option for high-quality text embeddings. That release was used to power smarter on-device search tools and privacy-first retrieval augmented generation (RAG) pipelines, and it surpassed 20 million downloads as the developer community's response blew past expectations. EmbeddingGemma 2 addresses the next step: content is no longer only text. Users have voice memos, image libraries, video archives, and code repositories, and cloud embedding services can add latency and raise privacy concerns because data has to leave the device. EmbeddingGemma 2 unifies those modalities in a shared space so developers can build cross-modal search and retrieval that works entirely offline. EmbeddingGemma 2 is described as the most capable model for on-device multimodal embeddings. It is built from the same technology as Gemini Embedding models and is positioned as best-in-class for its size, achieving leading scores among sub-1B multimodal embedders on benchmarks such as MTEB (Massive Text Embedding Benchmark) Code and MAEB (Massive Audio Embedding Benchmark). According to Google, it matches or outperforms many larger models across text, vision, and audio tasks, and across image, video, documents, and audio it sets a new standard in quality-per-parameter for sub-1B models, even outperforming some specialist models more than twice its size. It also matches the strong multilingual text performance of the original EmbeddingGemma while improving code performance significantly: the MTEB Code score rises from 68.76 to 78.68, a 9.92-point improvement. That makes the model well suited to local codebase indexing, semantic code search, and coding agent retrieval. Modular by design, EmbeddingGemma 2 requires as little as 270M parameters for text-only workloads, with optional vision (170M) and audio (300M) encoders available for full multimodal support. Developers can therefore scale the deployed footprint to the workload instead of always shipping the largest configuration. Storage efficiency is handled through Matryoshka Representation Learning (MRL): output vectors can be dynamically truncated from 768 dimensions down to 512, 256, or 128 dimensions, which provides up to 6x storage reduction for local vector databases and reduces memory usage. For apps that keep embeddings on the device, this directly affects how much media and content can be indexed and searched locally. The model is optimized for on-device performance and runs efficiently within tight resource constraints. With quantization, on a Google Pixel 11 Pro, EmbeddingGemma 2 requires as little as approximately 191MB of active RAM for text-only weights and roughly 567MB for the full multimodal model. It also features an 8K token context window, four times larger than EmbeddingGemma 1, which allows it to process up to 5.5 minutes of audio, 29 images, 58 video frames, or interleaved combinations of those inputs directly on local hardware. That extended context lets a single embedding cover a substantial piece of real content, such as a recorded clip, a set of images, or a sequence of video frames, rather than only a short text snippet. Overall, EmbeddingGemma 2 brings robust embedding capabilities directly to edge hardware. Generating embeddings locally helps ensure data privacy, reduces pipeline latency, and empowers developers to build cross-modal search and retrieval that works entirely offline. Because the model is built on the Gemma 4 architecture and shares its text tokenizer and audio encoder, developers can run EmbeddingGemma 2 together with generative models such as Gemma 4 in a unified pipeline with a lower combined total memory footprint. That pairing enables on-device RAG pipelines that understand complex multimodal data, for example using EmbeddingGemma 2 for local file retrieval and Gemma 4 for contextual reasoning. The benefits described by Google follow directly from running embeddings on the device: data privacy, since content does not need to be sent to a cloud service; reduced pipeline latency; and offline operation, so search and retrieval keep working without a network connection. The modular parameter counts and MRL truncation reduce memory and storage requirements, letting developers index more content within a device's constraints. The quality gains in code, vision, and audio mean cross-modal search is practical at this size, and the extended context window means embeddings can represent longer stretches of audio, images, or video rather than only short text. Google demonstrates several concrete use cases. In Google AI Edge Gallery's Instant Media Search, users find the top matches in a media library using text or an image, ranked by semantic similarity. Video Moments Finder locates specific moments in video using text or audio queries. The Google AI Edge Foresight app pairs EmbeddingGemma 2 for local file retrieval with Gemma 4 for contextual reasoning. Developers can also create real-time decision engines that leverage multimodal context for classification, routing, and predictive capabilities via the MediaPipe Decision Task API. Because of the code improvement, the model is well suited for local codebase indexing, semantic code search, and coding agent retrieval. The launch example also describes finding a specific video clip from a voice memo, or searching through hours of audio recordings based on a text query, all handled by a single natively multimodal model. EmbeddingGemma 2 is aimed at developers building on-device and edge applications, including those working on search, retrieval, and RAG experiences. Getting started is supported by model weights on Hugging Face and Kaggle, with Gemini Enterprise Agent Platform Model Garden availability coming soon, and LiteRT Community on Hugging Face for models optimized for on-device use. For on-device deployment, developers can use Google AI Edge MediaPipe for turnkey embedding, retrieval, and decision tasks, or LiteRT for custom model integration; browser builds are possible with transformers.js or WebGPU. The model can be served with transformers, sentence-transformers, MLX, vLLM, llama.cpp, SGLang, Ollama, and LMStudio, embedding vectors can be stored with Qdrant, and Unsloth provides guidance on fine-tuning. Google also points to a developer guide, documentation, and specific inference and fine-tuning guides. The model is released under the commercially permissive Apache 2.0 license. EmbeddingGemma 2 continues the EmbeddingGemma line by moving lightweight embeddings beyond text. It unifies text, code, images, video, and audio in one shared embedding space, stays small enough to run on-device, and stays flexible through modular encoders and truncatable vectors. For developers who need privacy-first, low-latency search and retrieval that works offline, it provides a single multimodal model for cross-modal search, on-device RAG, and real-time decision tasks.
Claude for Google Workspace is an add-on that puts Claude to work directly inside Google Docs, Google Sheets and Google Slides. Rather than leaving a file to ask an assistant for help, users open Claude from the Extensions menu and work with it in a sidebar beside the document, spreadsheet or slide deck they have open. In Docs, Claude drafts from scratch, tightens wording and structure, and delivers changes as suggestions the author applies or dismisses. In Sheets, it builds financial models, analyzes data and pulls in live numbers from data sources. In Slides, it builds slides from a deck's layout, edits whatever is selected and flags overlapping elements. The product is in beta and is aimed at individuals and teams who already run their writing, modelling and presenting inside Google Workspace. Most AI assistance today lives in a separate window. A user copies text or numbers out of a document, pastes them into a chat interface, prompts for a result, and then copies the answer back into the file. That round trip breaks concentration, risks losing formatting, and makes it hard to see exactly what the assistant changed once the text is back in place. Claude for Google Workspace is designed to remove that loop, so that there is no more copying and pasting between apps. By working inside the file, the assistant can operate on the real content, respect the document's existing styles, and present its output as something the user reviews and approves rather than something that simply appears in the middle of shared work. Claude for Google Docs covers three kinds of work. It can draft a document from scratch when there is nothing on the page yet; it can tighten wording and structure in something that already exists; and it can present its changes as a suggestion that the author either applies or dismisses. That last behaviour matters because it keeps editorial judgement with the writer instead of silently rewriting a shared file. In Google Sheets, the same sidebar can build financial models, analyze the data already in the spreadsheet, and pull in live numbers from your data sources. Suggested edits are visible in Docs and changed cells are highlighted in Sheets, so every modification is legible before it becomes permanent. Claude for Google Slides builds slides from your slides layout rather than inventing a look of its own. It edits whatever you have selected, which keeps changes scoped to the element under discussion, and it flags overlapping elements so layout problems are surfaced rather than left for a reader to notice later. New slides are visible before you accept them. Formatting is preserved throughout: Claude builds from your deck's theme, follows your heading styles, and leaves surrounding formatting intact. The result is that generated content looks like it belongs in the file, instead of needing a second pass to repair fonts, colours and heading levels. Beyond the three apps, a set of core capabilities carries across them. Connectors bring in your tools, letting Claude pull context from other systems into the sidebar so answers can account for information that is not in the open file. When a process works, it can be saved as a skill, and a team then runs that same process the same way in Docs, Sheets and Slides. Preferences let you tell Claude how you like a model built or a memo drafted, and it works that way next time. Reference files extend the same idea: you can drop a PDF, a CSV or an Office file into the sidebar and Claude reads it alongside your open file, which is useful when the source material lives outside Google Workspace. The mechanics are deliberately narrow. Installation happens from the Google Workspace Marketplace, or a workspace admin can deploy the add-on to a domain or to selected groups; after that, Claude is opened from the Extensions menu and runs as a sidebar. Inside the add-on, Claude can access only its sidebar and the file you opened it in, plus any connectors you turn on, and Google lists both permissions before you allow access. Workspace admins control who installs it. Editing also runs in the other direction: you can ask Claude to grab a Google file, or paste a Google Docs, Sheets or Slides link into Claude on web or desktop, and on supported setups the file opens beside the chat so you and Claude edit together. That path uses the Google Docs, Sheets and Slides connectors, currently in beta, and access follows your Google Drive permissions. The practical benefit is that assistance arrives where the work already is. Writers keep their heading styles and review changes before they land. Analysts can build and adjust models without exporting data. Presenters get slides that match an existing theme and are warned about overlapping elements. Teams get consistency, because a saved skill runs the same process across three apps. And administrators get visibility, because permission scopes are shown up front and installation is centrally controlled. Underneath it all, the user remains in control of what changes, since suggested edits, highlighted cells and new slides are all presented before acceptance. Concrete scenarios follow the three apps. A marketing team drafts a brief in Docs, asks Claude to tighten the wording, and applies only the suggestions it likes. A finance analyst builds a financial model in Sheets, has Claude analyze the underlying data and pull in live numbers from a connected data source, then checks the highlighted cells. A consultant assembles a client deck in Slides, letting Claude build slides from the existing layout while flagging overlapping elements. Elsewhere, someone drops a PDF or CSV into the sidebar to have Claude read it next to an open sheet, and a team turns a repeated reporting process into a skill so everyone produces the same output. Claude for Google Workspace is in beta on all paid Claude plans and runs in Chrome, Edge and Safari. Installation can be self-service from the Google Workspace Marketplace or managed by an admin who deploys it to a domain or selected groups. Because the add-on surfaces permission details before access is granted, it suits organisations that need to know what an assistant can see. Data handling depends on the plan: for consumer plans, data is handled according to Anthropic's Privacy Policy, while for teams and enterprise plans it is handled according to the Commercial Terms and the Data Processing Addendum. Support is available through the Help Center article on using Claude in Google Docs, Sheets and Slides, or from an Anthropic account team. The takeaway is that Claude for Google Workspace keeps the assistant inside the file instead of beside it. Drafting, spreadsheet analysis and slide building all happen in a sidebar in Docs, Sheets and Slides; connectors, skills, preferences and reference files extend what Claude can draw on; and suggested edits, highlighted cells and previewed slides keep the user in control of what actually changes. It is a beta add-on for paid Claude plans, delivered through the Google Workspace Marketplace, and built for people whose working day already happens in Google Docs, Google Sheets and Google Slides.
KloudMate is an AI-powered, full-stack observability and SRE-ops platform that brings logs, metrics, and traces together in a single place so engineers can find and fix production issues without jumping between tools. Its headline promise is unified observability with an SRE Copilot built in: rather than treating telemetry as separate silos, KloudMate connects alerts, signals, incidents, and infrastructure context into one investigation flow. The platform pairs a complete observability stack — log management, infrastructure monitoring, APM and distributed tracing, alerting, incident and on-call management, synthetic monitoring, and Kubernetes and infrastructure monitoring — with KloudMate Assistant, an AI module that summarizes, correlates, and guides response workflows. KloudMate is described as built for modern, distributed systems and is aimed at SRE and platform teams who need production visibility from telemetry collection through to incident response. The investigation problem KloudMate targets is simple to state and painful to live with: every signal lives in a different tool, so every incident becomes a manual hunt. Alerts tell you something is wrong, while logs, metrics, traces, incidents, and infrastructure events tell you why — but only when a team can connect them quickly. KloudMate describes three concrete symptoms of this fragmentation. First, signals are scattered: teams jump between dashboards, alert channels, logs, traces, and infrastructure views just to understand what changed. Second, triage takes too long: every incident begins with manual correlation, noisy alerts, and repeated context gathering across tools. Third, costs keep growing: as telemetry volume increases, fragmented observability stacks become harder to manage and more expensive to operate. KloudMate's answer is to bring these signals together and use KloudMate Assistant to surface context, correlations, and next steps during investigation. KloudMate Assistant is the platform's SRE Copilot, designed to move teams from alert to evidence faster. It correlates telemetry, summarizes incident context, highlights likely causes, and guides engineers toward the next useful investigation step. The Assistant's documented capabilities include automatic correlation — connecting alerts with related logs, traces, metrics, infrastructure signals, deployments, and incident activity — and AI-assisted triage, which summarizes what happened, what changed, and which signals are most relevant before engineers start digging. Guided investigation then helps teams identify where to look next using telemetry-backed context instead of guesswork, and the module also works to reduce alert noise by grouping related signals and incidents so teams can focus on the underlying issue rather than every symptom. A representative Assistant output shows an incident summary for a Payment API latency increase after a deployment, listing correlated signals such as an error-rate spike, slow database queries, trace timeouts propagating from the database query layer, and Kubernetes restart events, followed by a suggested next step to review the deployment change and inspect database saturation. The telemetry layer underneath the Copilot covers the three pillars of observability. Logs can be searched, filtered, and investigated with context from services, traces, infrastructure, and incidents, so a log line is never examined in isolation. Metrics monitor service health, infrastructure performance, SLOs, and custom metrics at scale. Traces, delivered through KloudMate's APM offering, follow requests across distributed systems to identify latency, errors, and dependency issues, and let engineers move between related telemetry signals during an investigation without losing service, request, or incident context. The product illustrates this with a checkout trace showing a total duration of 1.84 seconds across 27 spans with one error, breaking down time across a frontend proxy, a checkout API handler, a Redis cart lookup, an inventory API call, a PostgreSQL SELECT for items, a payments API charge, and a Kafka publish — exactly the kind of end-to-end view needed to see where latency actually lives. Beyond raw telemetry, KloudMate covers the operational workflows around it. Alerting lets teams build workflows that connect symptoms to context and route them to the right responder. Incident management and on-call handles routing alerts to whoever is on call, paging them by phone until someone acknowledges, escalating through further steps when there is no response, and keeping customers posted with a status page. The site illustrates this with an incident timeline: an alert page goes to the primary on-call engineer, goes unanswered, is re-escalated to the second step with additional engineers rung by phone, and is finally acknowledged when one of them presses a key on the call. Synthetic monitoring tracks user-facing availability and performance before customers report issues, adding an outside-in check on top of internal telemetry. Kubernetes and infrastructure monitoring gives teams a view of cluster, node, pod, and workload health alongside application telemetry, so infrastructure events — such as pod restarts and OOM-kill spikes — can be read in the same context as service errors. The overall investigation workflow ties the pieces together in five steps: an alert is triggered when KloudMate detects abnormal latency, error rate, resource saturation, or availability impact; signals are correlated as KloudMate links the alert with related logs, traces, metrics, infrastructure events, and incident activity; the Assistant summarizes context by highlighting what changed, what is affected, and which evidence matters most; the team investigates faster starting from a focused investigation path instead of manually searching across disconnected tools; and the response stays connected, with findings, ownership, timelines, and follow-up actions remaining tied to the incident context. The stated benefits centre on consolidation and predictability. KloudMate helps teams consolidate telemetry, alerting, incidents, and investigation workflows into one platform, reducing tool sprawl and lowering operational overhead while keeping observability costs predictable as telemetry volume grows. Being OpenTelemetry native means teams collect telemetry using open standards and avoid lock-in to proprietary agents. Cost efficiency is described as a platform design principle rather than an afterthought, and the platform's production-readiness is framed around real-time signal collection, on-call paging and re-escalation, and a workflow tuned for live incident response. Together these translate into less manual investigation time, one place to look during an incident, and a stack that scales economically with telemetry growth. Concrete scenarios described in the content include investigating a latency regression: an engineer asks why p99 latency on checkout jumped after a specific time, and the Assistant points to the inventory API deployment that landed minutes earlier, notes that the added latency sits on PostgreSQL SELECT spans inside a stock-lookup call, and suggests opening the relevant trace cluster and comparing database statements across versions. Another scenario is a payment API incident where an alert fires on p95 latency breaching its SLO and error rates climbing, and the Assistant assembles an incident timeline covering the deployment, the alert, and the opened incident along with correlated signals and a suggested next step. Other workflows include paging and re-escalating to on-call engineers until an incident is acknowledged, monitoring Kubernetes workloads for restarts and OOM kills, and tracking user-facing availability with synthetic checks. Teams also use KloudMate to consolidate fragmented logging, metrics, tracing, and incident tooling. KloudMate is built for modern SRE and platform teams operating distributed systems in production; the site shows engineers from companies including SprintMoney, Rocketium, Codeifai, Ostrum, Soffit, Microsoft, WeCheer, HealthifyMe, and Smartbox. On the technology side, the platform is OpenTelemetry native for telemetry collection and designed for Kubernetes, covering services, pods, nodes, clusters, workloads, and application telemetry together. The Product Hunt listing notes that KloudMate originally launched three years earlier as an AWS serverless monitoring tool and returns as a full-stack, AI-powered observability and agentic SRE-ops platform, with AI modules comprising Assistant (Answers), Builder (Dashboards, Alarms), Investigator (RCA), and Docs (Documentation). Access to the product is offered through a demo booking and an exploratory demo environment rather than a published self-serve pricing page. KloudMate's value proposition is straightforward: unify the signals that describe production, put an AI SRE Copilot inside the investigation workflow, and let teams move from alert to root cause without switching tools. By connecting logs, metrics, traces, alerts, incidents, synthetics, and Kubernetes and infrastructure context in one platform — and by using KloudMate Assistant to correlate evidence, summarize context, and suggest next steps — it aims to shorten incident triage, cut manual correlation work, and keep observability costs predictable as telemetry grows.
Ana is an AI negotiation agent for software purchases, built by Vertice. It runs software negotiations on your behalf: it reads your requirements, builds the strategy, drafts every email and tracks every round, updating the plan the moment a vendor responds. Ana is aimed at procurement buyers and sourcing teams who purchase and renew software, and it specialises in long tail spend — the many smaller renewals that consume time but rarely receive expert attention. By bringing negotiation expertise to every purchase, Ana is built to deliver negotiations at scale and gives buyers hours back on every renewal, while keeping the human team in control of the outcome. Most teams overpay on SaaS renewals because they go in without knowing what a competitive price looks like. Negotiation is a chore rather than the core of the job, and it is the non-value-add back and forth that eats the time procurement teams would rather spend tapping into commercial value. Ana exists to close that gap: it replaces the chore, not the core. Because it handles the data-heavy parts of negotiation — analysing the contract, benchmarking the price, building a strategy and managing the back and forth — buyers no longer need to be expert negotiators to get a good outcome, and they can pursue negotiations at a scale that manual effort would not allow, especially across long tail spend. Ana is trained on the world's largest software pricing dataset. It has been built on thousands of comparable live negotiations, so it knows how vendors price, when they concede, and what it takes to secure the best terms. The dataset behind every negotiation spans 32k+ vendors benchmarked across every category, 2M+ vendor price points powering every negotiation, and $75bn+ of real vendor spend analysed and applied. Vertice states that Ana has negotiated $500 million in spend over more than 4,000 negotiations. Every insight Ana produces is backed by data, which means the buyer can always see the evidence behind a recommendation rather than relying on intuition. When a negotiation begins, Ana analyses your deal and benchmarks it against thousands of comparable contracts across 32,000+ vendors and 2M+ price points. It then builds a negotiation strategy specific to your vendor and your requirements, and drafts the outbound emails with the reasoning explained so you understand what is being asked and why. As your vendor responds, Ana adapts its approach and updates the plan, tracking every round of the negotiation. This is how it helps buyers negotiate a better price on SaaS renewals: it tells you what to ask for and how to ask for it, based on what a competitive price actually looks like. Control stays with the buyer. Ana is designed as a co-pilot, not an autopilot. It drafts every email, includes the reasoning behind it, and waits for your approval before anything is sent. Tone settings and constraints are configured upfront so Ana operates within boundaries you define, and you stay in control of every communication that goes to your vendor. If you disagree with its strategy, you can review, edit or ignore any recommendation before it goes anywhere. Tone, timing and escalation decisions are always yours; Ana handles the data-heavy parts of the negotiation. Ana also provides a deal overview and analysis, so you can always go back and see the evidence for why a purchase was approved — which matters from an audit trail perspective. Overall, the approach is a repeatable loop: analyse the deal, benchmark it against comparable contracts, build a vendor-specific strategy, draft the outbound emails with reasoning, send nothing without approval, then adapt as the vendor responds — with the team reviewing and approving every step while Ana carries the expertise and the execution. Everything lives in one place rather than a tab for email, another for an AI assistant and another for an Excel comparison, so a negotiation and its evidence stay together on the same page. Procurement buyers using Ana achieve measurable outcomes: 18% savings on average and 15 days cut from renewal cycles. Beyond the numbers, users describe it as scalable and as a way to remove the non-value-add back and forth, freeing up time to tap into commercial value they would not otherwise have had time for. The natural, professional wording Ana produces in its emails — described as looking like a human wrote it — means vendor communication does not read as automated, which protects the buyer's relationship with the vendor while the negotiation progresses. Concrete uses include negotiating SaaS renewals, where Ana benchmarks your contract, builds a strategy and drafts the emails so you know what to ask for on every renewal; managing long tail spend, the many smaller purchases that would otherwise go unnegotiated; benchmarking a software purchase before it is signed so the buyer knows what a competitive price looks like; and maintaining an audit trail, using the deal overview and analysis to evidence why a purchase was approved. It is also used by teams that want to run many negotiations at once, where manual back and forth across a large portfolio simply would not fit into the working week. Ana is used by procurement professionals — procurement leads, heads of strategic sourcing, procurement experts and global IT sourcing managers — across companies such as Baringa, Bloomberg, Lenqi and a global security services company. On confidentiality, Vertice states that Ana is trained on Vertice's aggregate dataset, built from years of vendor negotiations across thousands of contracts, and that your individual contract data is not shared with other customers or used to train against your interests. There is no free tier or published pricing on the site; buyers are invited to book a demo or get started directly. In short, Ana's value proposition is that it brings negotiation expertise to every software purchase. It combines the world's largest software pricing dataset with a strategy-and-drafting workflow that keeps the buyer in control, so procurement teams can secure better terms, cut days from renewal cycles and reclaim the hours previously spent on non-value-add back and forth.
Alkera is an agentic data platform that brings data engineering, analysis, and science into collaborative multiplayer workspaces shared by both humans and agents. The product is also known through Databench by Alkera, described on Product Hunt as the open-source, multiplayer workspace for data science, analytics, and engineering. Its stated purpose is to cover an entire data stack within one agentic platform, letting data teams collaborate live alongside teammates and agents in notebooks and chats, run any cell or agent on a laptop, another computer, or a GPU node, launch many agents in parallel to explore ideas, and trace every result back to the data and code behind it. Alkera presents itself with a single headline: 'One agentic platform. Your entire data stack.' The three disciplines it names — data engineering, analysis, and science — have historically been handled in separate tools and by separate specialists. Alkera's stated approach is to place all three in shared, multiplayer workspaces where humans and agents work together rather than in isolation. The platform leans heavily on two related concerns. The first is trust: the Product Hunt description states that every result traces back to the data and code behind it, and the site demonstrates column-level lineage across warehouse, transformation, and analysis layers, plus knowledge entries that display their sources and whether they are human-verified. The second is safety: Alkera demonstrates testing changes safely in sandbox environments, so edits to pipelines can be examined before they are relied upon. The marketing language around the product frames these qualities as confidence and speed for an agentic data stack. The core surface for this collaboration is the notebook and the chat. In the demonstration shown on the Alkera homepage, a user named Priya asks a Signals agent, 'Can you chart monthly revenue by segment for this year?' The agent reports that it used two notebook tools and ran q3-revenue.alknb.py, three cells, finished. A second teammate, Marcus, then asks whether the analysis can be split by region as well; the dbt agent replies that it is adding a region facet to the trend chart and reports editing q3-revenue.alknb.py, one cell. The resulting chart is titled 'Monthly revenue by segment,' uses month, revenue, and segment fields, includes a tooltip and a facet, and renders enterprise, mid-market, and SMB series across the months of the year. Notebooks therefore appear as ordinary files in the workspace with an .alknb.py extension, and both humans and agents can read and modify them in the same live session. Alkera maintains a dedicated features page for notebooks and dashboards, indicating that dashboards are a first-class part of the same workspace. Agents in Alkera are not confined to a hosted environment. The Product Hunt description states that a user can run any cell or agent on their laptop, another computer, or a GPU node, and launch many agents in parallel to explore ideas. The homepage illustrates this with a training notebook that builds a Llama-style model configuration — hidden size 2048, 24 hidden layers, 16 attention heads, and a maximum position embedding of 4096 — wraps it in FSDP with a bf16 mixed-precision policy, and runs a training loop with gradient clipping and a scheduler, charting pretraining loss against tokens for train and validation splits on 8x NVIDIA B200 hardware. The same interface shows which model powers an agent: the chat panel displays Claude Opus with a 'High' setting and an 'Ask first' permission mode, and agent messages carry small indicators of what the agent did, such as using two notebook tools, running three cells, or editing one cell. Trust in results is a recurring theme. Alkera's stated position is that every result traces back to the data and code behind it. The site demonstrates column-level lineage across warehouse, transformation, and analysis, which lets a reader follow a column from where it is stored, through the transformation that produced it, into the analysis that consumes it. The knowledge base behaves similarly: each knowledge entry shows its sources and whether it is human-verified, so a reader can see not just the answer but where it came from and whether a person has vouched for it. Alongside these, Alkera demonstrates testing changes safely in sandbox environments, giving teams a way to try modifications without committing them to the live stack. Together these features form a provenance story in which code, data, and knowledge all carry visible evidence of their origin. Alkera's distinguishing approach is to treat agents as first-class participants in the data workspace rather than as a separate assistant window. Agents are given notebook tools, so they can run cells, edit files, and generate charts directly inside the same document a human is working in. The charting interface shown on the homepage, alkera.chart(revenue).line(x='yearmonth(month)', y='sum(revenue)', color='segment').title('Monthly revenue by segment').tooltip().facet('region'), illustrates the style: concise, chainable methods for line charts, titles, tooltips, and faceting. Because agents act on the notebook itself, their work is visible and reviewable in the same place as a teammate's. The platform is also designed to sit on top of the tools a team already uses. Alkera publishes a plugins and connections reference and lists supported systems spanning orchestration, transformation, analytics databases, lakehouses, data warehouses, query engines, business intelligence, knowledge sources, issue tracking, observability, data ingestion, code and CI/CD, communication, and object storage. The benefits Alkera describes center on confidence and speed. Speed comes from parallel exploration: many agents can be launched at once to investigate ideas, and individual cells or whole agents can be dispatched to a laptop, another machine, or a GPU node, so heavy work does not block the interactive session. Speed also comes from having teammates and agents in the same notebook and chat, which removes the need to hand results between separate tools. Confidence comes from traceability. Because every result links back to the data and code behind it, and because lineage is exposed at the column level, a reviewer can check how a number was produced rather than accepting it on faith. Knowledge entries that display their sources and verification status serve the same purpose for documentation, and sandbox environments allow changes to be validated before they matter. Concrete scenarios are visible throughout the material. A data team can ask an agent to chart monthly revenue by segment for a year and then extend the same chart with a regional break, which is exactly the sequence demonstrated on the homepage. An engineer can run a distributed training job — the FSDP and B200 example — and watch pretraining loss as training progresses. An analyst investigating a surprising figure can follow column-level lineage back through the transformation layer into the warehouse to find where the value originated. A team planning a pipeline change can rehearse it in a sandbox environment first. Anyone maintaining internal documentation can build a knowledge base whose entries show their sources and whether they have been human-verified. And a team with an existing stack can bring Alkera in alongside the orchestration, warehouse, transformation, and business intelligence tools already in use. Alkera is aimed at data teams: data scientists, analytics and data engineers, and the broader group of people who do data engineering, analysis, and science. Its Product Hunt topics are Open Source, Artificial Intelligence, and Data Science, and because Databench is open source, teams can either use Alkera's hosted offering or host Databench themselves from its GitHub repository. Pricing starts free: the site offers a 'Start for free' call to action, the Product Hunt listing mentions a generous free tier, and there is also an option to book a demo with the founders. The platform runs on the web and is designed to connect to the tools a team already uses, with a published list that includes Airflow, dbt, ClickHouse, Databricks, DuckDB, generic SQL, Google Docs, Linear, MySQL, PostgreSQL, Sigma, Snowflake, Tableau, AWS, BigQuery, Confluence, Datadog, Fivetran, GitHub, Hex, Looker, Notion, Redshift, Slack, SQLite, and Trino. Security, privacy, and terms documentation are published at dedicated links. Alkera's proposition is straightforward: one agentic platform covering an entire data stack, with collaborative multiplayer workspaces where humans and agents share notebooks and chats, agents that can run anywhere from a laptop to a GPU node and in parallel, and results that always trace back to the data and code behind them. For data teams that want the speed of agent-assisted exploration without giving up visibility into how results were produced, that combination of multiplayer collaboration and end-to-end traceability is the core value.