Category Focus/categories/customer-service
Customer Service AI Tools
Discover and compare the best customer service AI tools and software. Browse 37+ curated tools with reviews and rankings.
Projects tracked
37
Sort mode
RECENT
Page
1
Customer Service
TraceX is an AI engineer for production issues, built to take a customer-reported problem from the first support message all the way to a proven root cause and a fix. When a support email, a Slack message, a Jira ticket or a question in the dashboard arrives, TraceX reads it, plans what to check, and then investigates the systems behind the issue: it queries the database read-only, reads the code and recent changes, and checks error and log tools such as Sentry, Datadog and CloudWatch. It then returns the root cause with a confidence level, what should have happened versus what actually happened, and the evidence behind every claim. From there it writes the fix as a pull request and drafts a reply for the customer. It is aimed at engineering and support teams running production software, and it is free while in private beta. The hard part of a production issue is usually not the fix but finding the cause. A customer writes that a refund has not arrived, and the explanation can be buried across a database state, a migration that renamed something weeks earlier, a code path that still filters on the old value, and a background worker that now finds nothing to process. In the example shown on the TraceX site, 34 refunds sit in a pending state created after October 21, migration 0142 renamed pending to awaiting_payout, and the refund worker still filters on pending, so it silently finds nothing to process. Sentry reports no errors at all, because nothing crashes; the worker simply has nothing to do. Tracing that by hand means jumping between a database console, a repository, a commit history and log tools, and then explaining the conclusion to a support team and to a customer. TraceX is built to do that work in one place, the way your best engineer would, and to show its work at every step so the answer can be trusted rather than guessed at. Every investigation moves through six visible steps: intake, triage, evidence, root cause, action and resolve. Intake accepts an issue from a support email, a Slack message, a Jira ticket or a question in the dashboard. TraceX then plans its checks and follows the data and the code until the cause is clear, querying PostgreSQL, MySQL or MongoDB through read-only credentials, reading the code and recent commits through the GitHub App, and checking Sentry, Datadog and CloudWatch. The result is a root cause presented with its confidence level, an expected-versus-actual comparison, and a citation for every finding pointing to the record, code or log line behind it. A second model checks the answer against that evidence, which is how TraceX keeps answers grounded in what it actually looked up rather than in plausible-sounding assumptions. From the root cause, TraceX moves to action. It writes the smallest change that fixes the cause and opens it as a pull request, with the diff reviewed and its risk level shown, along with validation steps written into the description. In the refund example, the fix changes a single filter from state="pending" to state="awaiting_payout" and is presented as low risk, with the validation step of running the refund worker against staging so that 34 refunds move to payout. TraceX also reviews your team's pull requests, giving reviews on every PR with one-click suggestions. Alongside the code fix, it drafts a reply for the customer, so the support conversation can close with an accurate answer instead of an apology and a promise to look into it. TraceX also answers questions about your systems in plain English: how many refunds are stuck, which customers were charged twice, or what changed in checkout since Monday. Answers come from your databases, code and logs rather than guesswork, each step is shown as it happens, and each claim is cited. When a longer answer is needed, TraceX produces spreadsheets, CSV and PDF exports. In the example conversation on the site, asking how many refunds are stuck returns 34 rows in the pending state, all created after migration 0142 shipped, with the query, the commits read and the verification against the evidence all displayed. This turns ad hoc production questions into documented, checkable answers that a support lead or engineer can act on immediately. For developers who prefer the terminal, the TraceX CLI brings the same capability into your repo. You sign in through your browser, link a repository with one command, and ask TraceX from your terminal with tracex -c for answers carrying the same evidence as the dashboard. It installs with one line on macOS and Linux. In a linked repository it also works in a coding mode that reads, edits and runs tests, asking for your approval before every change and command. The site shows the flow in practice: it edits app/workers/refunds.py, shows the removed and added filter lines, asks whether to apply the change, then asks whether to run pytest, and reports 12 tests passed. In a repository that is not linked, TraceX works as a coding agent that asks before every change. The design of the product is centred on trust. TraceX connects to your databases with read-only access and runs only read-only queries, so nothing in production is changed, and the site lists production writes at zero. Access is read-only and can be revoked at any time. Your records are not kept: there is no index of your code or data, answers retain what was looked up and where rather than the rows themselves, and exported files are deleted after 24 hours. You approve every change, because fixes arrive as pull requests for your review and the CLI asks before every edit and command. Enterprise sign-in covers SSO, two-factor sign-in, roles for admins, investigators and viewers, revocable sessions and a full audit log. You can also run TraceX on your own Anthropic or Gemini account, so AI usage is billed to you and never through TraceX. The outcome is that every customer issue can be traced to its root cause with evidence attached. Instead of an engineer spending time reproducing a report and correlating tools, TraceX returns a root cause, a confidence level and an expected-versus-actual comparison that a team can check line by line. Support gets a drafted reply built from the actual finding, engineering gets a reviewed pull request with validation steps, and the answer can be verified against the cited records, code and log lines. Because every finding cites its source and a second model checks the answer, the conclusion stands on evidence rather than on a model's best guess, which matters when the output is going to a customer and into production code. The product fits concrete day-to-day workflows. A support email saying a refund has not arrived starts an investigation that ends with a root cause and a reply ready to send. A teammate mentions TraceX in a Slack support thread asking why an order was charged twice, and gets the answer, with its evidence, right there in the thread. A Jira ticket about checkout timing out on Safari comes back with a comment explaining the root cause and a linked pull request. A support lead asks in plain English how many refunds are stuck and since when, and receives the rows, the commits read and the verification behind the number. A developer uses the CLI to fix the refund worker filter and run the tests. Teams also use TraceX to review the pull requests they open themselves. TraceX is in private beta with early engineering teams and is free while in beta, with 50 investigations a month during the private beta. It connects to 10 integrations — PostgreSQL, MySQL, MongoDB, GitHub, Sentry, Datadog, CloudWatch, Gmail, Slack and Jira — covering databases, code, logs and inboxes, all connected read-only. Setup is described as connecting a database and a repository and asking your first question in minutes, with an early access request and a sandbox available to try. The combination of read-only access, cited evidence, human-approved changes and the option to bring your own model keys makes it usable for teams that need to see exactly what the system looked at before they act on its conclusions. In short, TraceX's value proposition is simple: every customer issue, traced to the root cause. It reads the support email, investigates the database, logs and code read-only, proves the cause with cited evidence and confidence, writes the fix as a pull request and drafts the customer reply, while keeping production untouched and every change under your approval.
TopAgent is the AI messaging agent inside TopMessage, an SMS and WhatsApp platform for campaigns, reminders, verification, and customer conversations. The platform brings three capabilities into one account: marketing campaigns, a messaging inbox, and messaging APIs. TopAgent sits on top of that platform and runs SMS and WhatsApp messaging from start to finish — according to the site, you say what you need and TopAgent finds the right customers, writes the message, sends it, and suggests your next move. It is built for teams that reach customers by text: sales and marketing, customer service, developers, and operations. The site positions the platform as ready to use in minutes, with SMS and WhatsApp in one place and a fully agentic assistant that helps teams get the work done and improve results. The problem the product addresses is the fragmentation of business messaging work. Campaigns, two-way customer conversations, and verification codes are typically different jobs with different tools, and the site notes that developers otherwise have to manage multiple providers to send messages worldwide, verify users, and handle replies. Messages also need to be relevant, timely, and compliant, which is why the platform pairs sending with segmentation, analytics, opt-out management, and approval steps. TopMessage's answer is to keep campaigns, an inbox, and APIs in a single business messaging platform, then layer an AI agent on top so that routine setup — choosing contacts, drafting copy, building a campaign — can be described in plain language rather than configured through settings. The stated purpose is to help teams reach customers and hit their goals without technical overhead. TopAgent is the core agentic capability, and the site describes it in three steps. In Tell, you set your goal by describing it in plain language, like you would brief a colleague; no settings are needed, and TopAgent asks if anything is missing. In Send, TopAgent segments contacts, personalizes messaging for each audience, and drafts campaigns. In Improve, TopAgent suggests the next best actions to help you capture opportunities and grow revenue. The agent does not talk to customers on its own: the FAQ states that it selects contacts, writes messages, builds campaigns, and suggests replies inside TopMessage for your team, and that nothing is sent until you approve it. Campaign summaries include approve and request-changes actions. Marketing campaigns let teams run personalized SMS and WhatsApp campaigns and track performance from delivery to click. The site lists advanced segmentation, automation, and engagement analytics as the supporting capabilities, and describes the workflow as creating segments, launching campaigns, and tracking results with AI-powered automation. A campaigns dashboard shows delivery, click, and opt-out statistics per campaign. For sales and marketing teams, the platform supports interactive WhatsApp messages for driving sales, and a retail customer story reports a 3.4x return on campaign spend and an 18% average click rate from weekly product drops announced over WhatsApp. The messaging inbox is a shared space for two-way SMS and WhatsApp conversations. Customer service teams keep messages from both channels in one shared inbox so the team never misses a customer, and the inbox includes templates and suggestions. The site's example is a number customers can text, with your team answering from the inbox, and a dental clinic story reports 45% fewer inbound calls and a 4-minute average reply time after moving patient communication to text. For developers, TopMessage offers a messaging API, a verify API, and webhooks, so teams can add SMS and WhatsApp to an app without managing multiple providers. The site says developers can send messages worldwide, verify users, and handle replies, and the Verify API sends one-time passcodes by SMS so every account belongs to a real person. Transactional use cases such as order and delivery alerts can be sent from your own system through the API. A delivery company story reports 27% fewer support calls and a 98.6% delivery rate, while a fintech story reports 4x faster OTP arrival and 61% fewer fake sign-ups. Integrations connect TopMessage to a CRM, online store, and automation tools so customer data stays in sync and the right message goes out at the right moment. The site names direct CRM integrations with HubSpot and Salesforce, along with Pipedrive, Zoho, Slack, Google Sheets, and Shopify in its integrations illustration, plus no-code automation with Zapier, Make, and Power Automate. For custom builds there is a REST API and webhooks. The FAQ notes that a CRM can be connected three ways: direct integrations, Zapier for no-code connections to 1,000+ apps, or the API with webhooks. The product's unique approach is the combination of a self-service platform and a natural-language agent with an approval gate. Onboarding is described in three steps: connect, where you create your account and start right away with no waiting for setup; engage, where you segment audiences, manage replies, and send campaigns using natural language with TopAgent; and analyze, where you see what worked and what to improve next with AI-powered insights. No technical skills are required because everything works in the web app, and if a step feels technical you describe what you want to TopAgent and it sets it up, while developers can still use the API for custom flows. TopAgent works with the contacts, templates, and campaigns in your workspace, within the permissions of the person asking, and every action it takes is logged. TopMessage is GDPR-compliant and includes built-in opt-out management, so contacts who unsubscribe are excluded automatically. Benefits described on the site are practical and measurable. Automated SMS reminders cut no-shows for a beauty salon by 30% and saved 6 hours every week while keeping every chair booked. A dental clinic cut inbound calls by 45% and answered in 4 minutes on average. A retail brand turned weekly product drops into a new revenue channel with WhatsApp announcements. A delivery company reduced where-is-my-order calls by 27%. A financial company sped up sign-up verification and blocked fake accounts. Across these stories the through-line is faster replies, fewer inbound calls, fewer no-shows, and campaigns that convert. The site lists six best ways to use TopMessage. Sales teams grow revenue with campaigns, driving sales with interactive WhatsApp messages. Support teams reduce calls with texting, offering a number customers can text while the team answers from the inbox. Reminders cut no-shows by connecting TopMessage to your calendar and automatically sending reminders. Internal messaging reaches staff who are not at a desk, such as shift changes and urgent notices read in minutes. Transactional messages keep customers updated automatically with order and delivery alerts from your own system. Security verification uses one-time passcodes by SMS with the Verify API, so every account belongs to a real person. Product Hunt adds a B2B event use case: filling a conference, summit, or workshop with SMS invitations and relevant follow-ups. Pricing works on a top-up balance: you add credit and pay for the messages you send, with prices depending on the channel and the destination country. Setup is self-service and most teams send their first message the same day they sign up, though verifying a phone number or sender can take a little longer. The platform is aimed at every team — marketing, sales, support, and operations — plus developers who need messaging APIs. Channels are SMS and WhatsApp, usable for campaigns, two-way conversations in the inbox, and through the API, all from one account. The overall takeaway is that TopMessage pairs a complete business messaging platform with TopAgent, an AI agent that segments, drafts, and suggests next actions while leaving the final approval to your team.
Free Instagram Reel downloader. Paste a public Reel link to save the available video in HD MP4 for personal offline viewing—no login or app required. Works seamlessly on iPhone, Android, and PC.
Download Spotify songs, playlists, and albums for free with Spotidown – Free Spotify Downloader. Fast, high-quality Spotify downloader with no login required.
Tori is an AI support agent built by TalkJS for customer success teams. It learns a product in depth from content the company already has, including help centers, documentation, product pages, and company knowledge, and then delivers accurate answers to even the most complex customer questions. Tori runs in a help center, inside an app, or on a website through a chat interface that can be fully customized. Setup is presented as a matter of minutes: connect a URL, let Tori learn the product content, and embed the chat UI. Rather than replacing the support team, Tori works alongside it, answering what it knows and escalating what it does not. Customer success and support teams often hold product knowledge across scattered support content, while customers expect clear answers in the moment. A support queue makes customers wait, and when a conversation finally reaches a human, customers may have to repeat information they already shared. Tori is designed around a different approach: bring scattered support content into one dependable knowledge layer, use it to answer questions instantly, and hand off to a person only when human help is needed. The product explicitly positions accuracy as a priority, flagging uncertainty instead of filling gaps when confidence runs low. That matters because an answer that sounds confident but is wrong can damage customer trust. The first part of Tori's approach is knowledge. Teams connect their help center, docs, or product site by pasting a URL. From there, Tori learns the product, turning existing product content into grounded knowledge. The setup flow shows help content, website pages, guides, and updates being learned, along with a sitemap that stays up to date. The result is described as one source of truth: scattered support content is brought into a single dependable knowledge layer for every conversation. Because answers are grounded in the content the company already has, Tori can address complex product questions using the same material the support team relies on. Tori also stays in sync and can run in a help center, app, or website, so the knowledge behind the conversation is connected to the places customers already look for help. Tori does not simply return raw documentation. It first identifies what each customer needs and brings the right product context into the conversation. It then responds in your voice, using your tone, guidance, and rules so that every answer stays aligned with how your team supports customers. Customers receive clear, sourced answers in the moment without waiting in a support queue. Accuracy is handled with explicit boundaries: Tori knows its limits, flags uncertainty instead of filling gaps, and brings in your team when confidence runs low. This combination of grounded knowledge, brand-aligned responses, and visible uncertainty is central to the product's promise of accurate AI support rather than plausible-sounding guesses. When a customer needs a human, Tori escalates seamlessly. The conversation is passed to the team with context intact, and agents get the conversation and sources ready to continue without making customers repeat themselves. That handoff is a core part of the design: Tori answers what it knows and escalates what it does not. The experience is also customizable. Built on TalkJS, Tori lets teams shape the interface and behavior to match their product. They can use the ready-made chat UI, customize the experience, or connect Tori through the API, and they can embed the chat UI in docs, an app, or a website. This flexibility means the support agent can fit the surfaces where customers already interact with the product. Under the hood, Tori is built on TalkJS, which provides production-grade messaging, real-time delivery, and full conversation history as standard. TalkJS is described as the same infrastructure powering millions of real conversations across the web. That foundation supports the overall workflow: Tori understands the question, responds in your voice, answers instantly with sourced information, and escalates seamlessly when human help is needed. In practical terms, the product combines a grounded knowledge layer learned from existing content with a customizable chat interface and a context-preserving handoff to the support team. The stated result is support that never clocks out, delivered through infrastructure built for real conversations rather than a standalone answer box. For customers, the benefit is immediate help: clear, sourced answers in the moment instead of waiting in a support queue. For support teams, Tori keeps responses aligned with their tone, guidance, and rules, and it avoids filling gaps when it is uncertain, reducing the risk of inaccurate answers. When escalation is necessary, agents receive the conversation and its sources, so they can continue without asking customers to repeat themselves. Teams also gain control over the experience: they can customize the interface and behavior, embed it in their docs, app, or website, or connect through the API. Security features such as SSO, data controls, encrypted data, GDPR-ready controls, and ISO 27001 certified TalkJS infrastructure support the trust side of customer support. Tori fits several concrete support workflows. In a help center, it can answer complex questions using the documentation and guides the team has already published, giving customers a self-service path before they open a ticket. Inside an app, the embedded chat UI can identify what a customer needs, bring in the right product context, and answer instantly. On a website, Tori can use product pages as grounded knowledge and respond in the company's voice. For complex product questions, it can provide sourced answers and flag uncertainty when confidence is low. When a conversation requires a person, Tori hands it to the team with the conversation and sources attached, so the human agent can continue without a restart. This makes Tori relevant for teams that want instant first-line support while keeping human help available for the cases that need it. Tori is aimed at customer success and support teams that need accurate answers without compromising customer trust. It is built for teams that already have help docs, product pages, and company knowledge to draw from, and for organizations that want the agent to run in a help center, app, or website. The product is made by TalkJS and uses TalkJS infrastructure, with SSO, data controls, encryption, GDPR-ready controls, and ISO 27001 certification. Pricing starts with a free Trial plan with no time limit, up to 50 AI responses per month, one seat, and one agent. Basic is $35 per month for 300 AI responses, two seats, and one agent. Standard is $140 per month for 1,400 AI responses, three seats, and three agents, marked as best value. Growth is $550 per month for 6,000 AI responses, seven seats, and five agents. Enterprise is a custom plan based on team and support needs. Customers choose a plan when they sign up. Tori's core value proposition is straightforward: learn the product from the content a team already has, answer complex customer questions accurately and in the team's voice, and escalate to a human with full context when needed. By combining grounded knowledge, customizable chat, TalkJS messaging infrastructure, and enterprise-grade security controls, Tori offers customer success teams an AI support agent designed for accurate, trustworthy conversations rather than generic automated replies.
Lookout is a Slack app that watches Slack for time-sensitive messages and takes the action you choose. Powered by Jev from TypeSafe, it is built for teams whose Slack channels function as the live feed of their company — the place where a customer reports being locked out, where a production error surfaces, or where a large signup arrives. Rather than relying on someone to notice an urgent message among everyday chatter, Lookout monitors the channels you select and responds the moment a matching message appears. You describe what matters in plain words, choose where Lookout should look, and decide what should happen when it finds a match. The product is free during beta and is installed directly into Slack. The problem Lookout addresses is the mismatch between how quickly urgent requests arrive and how slowly a team can notice and act on them. The site's own illustration makes this concrete: Priya from Acme writes that the team is locked out after a password reset and has a demo in an hour, while other messages in the same #support channel ask about adding a second admin and thank the team because a CSV export is working again. Only one of those messages is time-sensitive, and missing it has real consequences. Slack is described as "the live feed of your company," which means urgent signals arrive continuously but are mixed with routine conversation. Lookout is designed to act on that feed as it happens, instead of depending on a person to watch it. Setting up a lookout starts with describing, in plain words, what Lookout should watch for. The site's example is a sentence rather than a query: "A customer is blocked from logging in or using the product." In the described flow there is no rule syntax to learn; you state the condition in ordinary language and Lookout uses it to check incoming messages. Next, you pick the channels where Lookout should look, with #support, #help-desk, and #customer-success shown as examples and an option to add more. Scope is deliberately under your control: Lookout only looks at the channels you pick. That keeps both noise and exposure down, because channels unrelated to support, engineering alerts, or sales never enter the picture. The third step is choosing what happens when Lookout finds a match. The setup flow shows two action types — a reaction (👀) and a DM — and the examples show how these play out. When Lookout spots an urgent support message such as a customer saying nobody on their team can log in since the morning and they present to their board at 2, the chosen action is to DM the on-call person. When it spots a specific error message, such as a TypeError in checkout-api reporting 142 events in the last five minutes in #alerts-prod, the chosen action is to create a Linear issue. When it spots a signup over $100 — Northwind Co. on the Team plan at $480/month in #signups — the action is to add a 💰 reaction. In every case, you choose the action and Lookout takes it instantly. The examples section shows that the same mechanism spans very different kinds of time-sensitive events. For engineering, Lookout can watch for specific error messages; the sample is a TypeError in checkout-api with a count of 142 events in the last five minutes, surfaced in #alerts-prod and turned into a Linear issue. For customer support, it can watch for urgent support messages; the sample is a customer locked out ahead of a board presentation, escalated by DM to the on-call person. For sales, it can watch for signups over $100; the sample is a $480/month Team plan subscription, acknowledged with a 💰 reaction. Because the condition is described in plain words, the same product covers monitoring, triage, and notification without a separate tool for each job. In operation, Lookout checks messages as they arrive: message text is sent to Jev (TypeSafe) to check each message, under TypeSafe's terms. The site states that Lookout never trains on your messages and that messages are cleared from Lookout after processing. Two controls reinforce this — you choose every action, and only the channels you pick are monitored. The site also discloses that visible app content may appear in session recordings retained for 30 days. The positioning is "real-time intelligence": the system acts on Slack as it happens rather than through a periodic digest or a manual review queue. The practical benefit is that urgent messages get handled instantly instead of waiting for someone to be at their desk, watching the right channel, and recognizing the signal. A lockout with a demo in an hour gets escalated to the on-call person right away; a production error becomes a Linear issue while the alert is still fresh; a significant signup gets acknowledged with a reaction. Because the action is chosen in advance, the response is consistent and does not depend on who happens to see the message. And because Lookout only looks at the channels you pick and clears messages after processing, teams can automate vigilance in sensitive channels without opening up every conversation in the workspace. The described scenarios map to concrete workflows. A support team could run a lookout over #support, #help-desk, and #customer-success for messages describing a customer blocked from logging in or using the product, with the action set to DM the on-call person. An engineering team could watch #alerts-prod for specific error messages and create a Linear issue automatically, as in the checkout-api TypeError example. A sales or growth team could watch #signups for signups over $100 and add a 💰 reaction to flag the subscription. The same pattern extends to any channel where a described condition should trigger a reaction or a DM. Lookout is aimed at teams that live in Slack and depend on it for time-sensitive signals: support organizations triaging customer issues, developers and on-call engineers watching production alerts, and sales teams tracking signups. Product Hunt lists it under Slack, Sales, and Developer Tools. Setup happens through Slack itself — "Add to Slack" installs it, and existing users sign in — and the product is free during beta. The three configuration steps and the resulting actions all live in Slack, where the messages themselves arrive. Lookout's value proposition is narrow and clear: Slack is the live feed of your company, and Lookout acts on it as it happens. By letting you describe what matters in plain words, limiting monitoring to the channels you choose, and taking the action you pick — a reaction, a DM, or a Linear issue — it turns passive monitoring into an instant, consistent response. It is free during beta and installs directly into Slack.
Kai Resolve is an AI support engineer from Gleap built for second-level, or tier 2, support. It handles the hard support tickets that Kai Support, Gleap's first-level AI agent, cannot answer. Rather than replying from documentation alone, Kai Resolve investigates: it reads the screen recording, console and network logs, the click trail, device and SDK versions, screenshots, account data from connected sources and read-only code access, then answers the customer or escalates with a diagnosis. It is built for SaaS and software support teams whose difficult tickets otherwise end up in an engineering queue, and it runs inside Gleap, the support platform that states it is trusted by more than 4,500 teams worldwide. The problem Kai Resolve targets is the ticket that an answer cannot settle. First-level support answers known questions around the clock, but a report like a checkout failing for some EU customers, or exported invoice PDFs coming back empty, needs someone to read logs, check configuration, trace a bug through source code and involve engineering. That investigation work is what tier 2 support does, and it is slow because the evidence is scattered and the context is rarely handed over cleanly. Kai Resolve is positioned as the second level that investigates those tickets, answers what the evidence settles and escalates the rest with a diagnosis, so engineers see the tickets that need them with the evidence already attached instead of as a bare handover. Every investigation starts from the evidence. Kai Resolve works from the ticket and thread, the device and SDK versions, console and network logs, the click trail, the screen recording and screenshots, plus repositories and data sources when the team allows them. On the website this is presented as an investigation brief with a set of sources, for example a ticket with three messages, two console errors and one failed request, a two-minute recording, read-only repositories and a read-only Postgres data source reached through MCP. Tickets that originate from the Gleap SDK carry the most evidence because console and network logs are captured automatically alongside the screenshot and any screen recording. Code investigation is explicitly read-only. Repositories are cloned into a sandbox and the report is traced through the source, and findings cite the files it read. Kai Resolve never commits or pushes. Access is listed per investigation, for example a read-only API repository and a read-only web repository, a data source queried through MCP, and any write, delete or billing tool blocked. A code change is a separate step performed by a coding task the team picks up, so the investigator itself does not modify the source code. The investigation produces three outcomes. If a setting or expected behavior explains the report, Kai Resolve sends a proposed customer reply with the cause, and the ticket can be answered without a teammate. If the report is a confirmed bug, it becomes a coding task in the ticket with the evidence attached. If a human decision is needed, the question arrives as a card with options, and the team's answer resumes the investigation. Tickets are classified as answerable, confirmed bug, suspected bug, feature request, needs more info or inconclusive, and a bug is marked confirmed or suspected with a confidence level and evidence citing the files it read. Feature requests are matched to the existing request on the public roadmap and the customer is subscribed to updates. When the customer's report is missing details, Kai Resolve asks the customer one focused question. When it is not sure, it says so: inconclusive means unknown, never no issue, and it re-investigates once when a new angle exists before asking a teammate. There are three ways an investigation starts. With "Analyze bug reports first" turned on, Kai Resolve investigates new bug reports from the Gleap widget before they reach the inbox, so they arrive classified with the evidence attached. In the chat widget, Kai Resolve takes over when Kai Support cannot answer or a customer asks for a person. And a teammate can start an investigation from any ticket, any channel, including email, Slack and integrations. The team sets the policy per project, from full second level to safety net only. Confirmed bugs become coding tasks that land in the ticket with the evidence attached. From there the team can fix the issue with Kai Code, open it in Claude Code, Codex or Cursor, or send it back with notes. Kai Code plans the change, edits the code, runs the tests and opens a pull request linked to the ticket, and engineers review and merge. With the default follow-up setting, a teammate confirms the fix is live before Gleap sends the customer reply. The company describes this chain as closing the loop from report to shipped fix, with Kai Support on first level, Kai Resolve on investigation and Kai Code on the code change. For users, the stated benefit is that hard tickets get a clear next step without the team sorting every report by hand. Customers can be answered directly when the evidence settles the ticket, engineers receive investigated escalations with a diagnosis instead of an unqualified handover, and the context travels with the work through to the pull request. The control claims are explicit: read-only code access, findings with evidence, and decisions made by the team. Inconclusive results are never silently turned into a no-issue verdict, and destructive database and billing operations are blocked. Concrete scenarios in the content include an EU checkout failing with a 500 from the tax endpoint after a deploy, where Kai Resolve reports a likely bug citing the deploy, surface survey notifications never arriving for visitors who are not logged in while chat notifications work, where the investigation finds that survey alerts skip visitors without an email, and empty exported invoice PDFs where the preview still shows every line. Others include a roadmap request for working hours per teammate that gets matched to the existing feature request and subscribes the customer, and technical questions in the chat widget that Kai Support hands over to Kai Resolve with session logs carried over. On plans, Kai Resolve is included in Gleap Team at $149 per month billed annually ($179 billed monthly), and in Pro and Enterprise. Each investigation is billed by the work it does, about $0.49 on average, a code investigation bills as Kai Code usage, and a run that fails to start is not charged. A 7-day free trial is offered with cancellation anytime. Gleap advertises a 4.6/5 rating on G2, and named customers in the content include Squarespace, Papa Johns, UNICEF, Microsoft, Universal Music, Tridonic, Commonplace, Partfox and Salesforge. In summary, Kai Resolve's value proposition is that it turns the hardest support tickets into investigated outcomes: an evidence-grounded answer, a coding task with the evidence attached, or an escalation with a diagnosis, all with read-only access and the team in control.
Actually Asked is an AI FAQ app for websites and Shopify stores. Rather than adding a floating chat bubble, it places a FAQ section directly inside your page, styled in your own fonts and colours, so it looks like part of the site. Visitors ask their question in their own words and receive answers taken only from your own pages, your help center and files you upload. The questions visitors ask most then join your FAQ on their own. The problem it addresses is a familiar one: businesses answer the same questions again and again by email. Sizing, shipping times, caffeine content, whether a jacket is truly waterproof, what a cancellation policy covers, what to bring to a first appointment. The answers usually already exist somewhere on a product page, in a help centre article or inside a PDF, but visitors either cannot find them or do not want to dig. Actually Asked is aimed at any site where people ask before they buy, book or sign up. The company puts the filter simply: if people email you the same questions again and again, it is for you; if nobody asks you anything yet, it is not. Three features carry the core experience. The first is an inline FAQ: a FAQ section that lives in your page rather than a chat bubble, and that is styled like your site. The marketing content shows the same widget rendered in the look of three different shops, a linen shirt store, a coffee roaster and an outdoor gear shop, each with its own typography and colours. The second is highlight to ask. Visitors select any sentence on the page and ask a question about it, which turns a specific line of copy, such as a sizing note or a care instruction, into an entry point for an answer. The third is the self-improving FAQ. The questions visitors ask most join your FAQ on their own, with nothing for you to approve. You can still edit, pin or remove any entry, and the product states that your edits are never overwritten. Answers are restricted to your own sources and nothing else. On the Sources page you connect your site, its pages, and on Shopify your products and store policies, which are read again every 7 days. You can add the address of a help center or knowledge base, and you can upload PDF or Word files such as size charts, care guides, manuals and price lists, including files that are not public. Answers can quote a file but never link to it. The onboarding view shows a site of 38 pages, a Shopify catalogue of 212 pages and four policy pages, all read at 04:00 with the next read scheduled for 04:00 the following day. The weekly gap report arrives as one email every Monday. It lists the questions asked most, the ones your pages do not answer, and what was added to your FAQ. A sample report shows 214 questions asked in the week, 87% of them answered and nine with no answer to write about. A separate leads feature captures visitors who leave an email address, so you have someone ready to reply to. Finally, an MCP integration lets you manage your FAQ by asking Claude, ChatGPT or another assistant, for example publishing FAQ entries through a tool call. Setup is presented as three steps. First, you add your domain, plus your help center or PDF and Word files if you have them; those sources are read, private files included, and your first FAQ entries are written from them. Second, you paste two lines, a div carrying your site key and an async script tag from the Actually Asked CDN, and the FAQ appears where you put it; on Shopify you add it as a block in the theme editor instead. Third, visitors ask, and your FAQ grows. The distinguishing choices are the absence of a chat bubble, the tight sourcing rule that answers come only from your pages, and the fact that popular questions are promoted into the FAQ automatically rather than waiting on a human editor. The outcome the product describes is a FAQ that looks like yours and learns from visitors. Popular questions are answered once and added permanently, so the same email does not need to be written twice. Because answers are grounded only in your own material, the reply a visitor sees reflects what you already publish. The Monday report gives a regular view of the questions your pages fail to answer, which points at content worth writing. Leads are collected for follow-up, and the whole FAQ can be maintained from a dashboard, the Shopify admin or an AI assistant. Four use cases are documented. Online stores, including Shopify, WooCommerce, Squarespace and custom shops, use it for product and policy questions such as whether a size runs small. SaaS and apps use it on pricing pages and documentation for plan and "does it do X" questions, illustrated by a per-seat pricing example where an annual discount question is answered from the /pricing page. Travel and hospitality businesses such as hotels, rentals and tours handle check-in, cancellations and what is included, illustrated by a room booking page answering whether breakfast is included from the /rooms page. Services and clinics, including agencies, clinics, studios and coaches, cover booking, pricing and what to expect, illustrated by a first-visit page that answers what to bring. Actually Asked was built first for Shopify stores but works on any website whose visitors have questions before they buy, book or sign up. Integration paths include a Shopify theme block, a WordPress Custom HTML block or footer script, a Webflow Code Embed, a Framer Marketplace plugin or pasted component, a Squarespace Code block, and static HTML, React, Next.js or custom templates. There is also a free gap-finder tool to find FAQ gaps and an MCP route for managing the FAQ through Claude or ChatGPT. Pricing starts free with 50 AI answers a month on one site, then $12 a month; every plan includes unlimited team members, and you pay for the answers you use and the sites you run. Actually Asked replaces the chat bubble with something more durable: a branded, inline FAQ that answers from your own sources and quietly grows with the questions visitors actually ask. It is free to start, live on your site in about ten minutes, and aimed at any business that keeps answering the same question by email.
SalesRep is an all-in-one omnichannel customer communication platform built around a single, AI-powered team inbox. It unifies every customer channel, CRM and AI provider in one place so teams can capture leads, automate conversations, broadcast campaigns and close deals faster from a single platform. The product also acts as a virtual sales rep that sells for you 24/7: it pulls live prices and stock from Shopify, WooCommerce, PrestaShop, Meta Catalog or your own database and API, answers customers on WhatsApp, Instagram, Messenger, email, SMS and other channels, and sends product cards and quotes on the fly in any channel. SalesRep follows up automatically until the deal closes, allows human takeover at any time, and works in more than 30 languages that are auto-detected. It is used by more than 1,000 businesses worldwide. As chats and calls multiply, old inboxes and workflows break. Teams that sell and support customers across WhatsApp, Instagram, Messenger, email, SMS, voice and social media end up juggling multiple platforms, losing context and leaving conversations unanswered. SalesRep addresses this fragmentation by giving every customer one reliable record and every conversation one thread, no matter which channel it came from. Instead of silos, the platform offers a unified conversation history, automatic contact merging and deduplication, real-time sync with your CRM, and smart routing based on channel and context. The stated goal is to help businesses manage the entire customer journey in one place, even at high volume — a framework SalesRep calls Conversation-Led Growth. At the centre of SalesRep is the Omnichannel Team Inbox. It unifies contacts from voice calls, email, WhatsApp, TikTok, Facebook Messenger, Instagram, Telegram, SMS, WeChat, MS Teams, Slack, iMessage, Google Business and more, including Asterisk. Every conversation appears in a single thread and every customer in one place, so agents no longer switch between apps or maintain separate inboxes. The inbox includes unified conversation history across all channels, automatic contact merging and deduplication, real-time sync with your CRM (or SalesRep's own CRM), and smart routing based on channel and context. SalesRep states that chat channels deliver 92%+ open rates, and that messaging channels support product catalogs and in-chat payments. The inbox also supports team and custom inboxes, live chat and popup widgets, a knowledge base and canned responses, mobile app access with 2FA, and AI call and chat summaries. SalesRep's AI Agents are AI that sells, supports and scales. Ready-made or custom agents qualify leads, answer FAQs, recommend products, book appointments, route enquiries and handle support requests around the clock, across every messaging channel. The site reports that 75% of chats are handled entirely by AI and 80% of conversations are resolved by AI. Agents perform multimodal processing — text, images, audio and files — so they can send documents and images and process screenshots, audio and files for full context understanding. AI sales automation sends product offers with photos, prices and live stock in real time, answers voice messages with its own voice replies, auto-detects each customer's language and replies in it, generates and sends quotes instantly without human intervention, follows up automatically on every quote until it closes, and hands the conversation to your team whenever needed. Additional AI Agent capabilities round out the offering. Guardrails define what AI Agents say and do so they stay on-brand and compliant, and humans can take over active conversations instantly. A RAG-powered knowledge base lets agents pull verified answers from knowledge sources such as PDFs, doc files, catalogs, images and websites, continuously updated for accuracy. Voice AI provides natural, human-like speech. Agent specialization lets you orchestrate teams of micro-agents focused on tasks like retrieval, routing and escalation for precision results at scale. Teams can start quickly with ready-made AI Agent templates or build a fully customized agent tailored to their business needs and workflows. Agents also collect data, update lifecycle stages, resolve support issues, recommend products, share images and trigger workflows automatically. Beyond the inbox and agents, SalesRep provides AI Workflows and Broadcast campaigns. Workflows let teams build powerful automations with a visual drag-and-drop builder to automate repetitive tasks, sync data across platforms and keep the team focused on closing deals rather than manual busywork, with automatic lead routing and assignment, real-time CRM sync and custom field mapping. Broadcast lets you send targeted messages across WhatsApp, SMS, Facebook, Instagram, Telegram and Email in one click, sharing promotions, product updates and newsletters directly to the channels your customers use most. Broadcast features include multi-channel campaigns, audience segmentation and targeting, delivery and engagement analytics, and automated opt-in/opt-out management. SalesRep connects to HubSpot, Salesforce, Jira, Zapier and 40+ tools, plus Shopify, WooCommerce and PrestaShop. SalesRep's overall approach is organised around its Conversation-Led Growth framework, which is designed to help businesses manage the entire customer journey across every channel in one place, even at high volume. The platform describes three pillars. The first is the Omnichannel Team Inbox, which unifies contacts from voice, email, WhatsApp, TikTok, Facebook Messenger, Instagram and more into one thread per customer. The second is AI Agents, which answer FAQs, qualify leads, route enquiries, book appointments and handle support requests around the clock. The third is Automate & Integrate, which connects SalesRep to HubSpot, Salesforce, Jira, Zapier and 40+ tools to automate workflows, sync data and eliminate manual busywork. A published sample conversation shows an AI agent greeting a customer, quoting an IP phone with a reference, price and live stock, preparing a quote for four units, sending a PDF quote by WhatsApp and email, and scheduling an automatic follow-up. The site publishes a range of outcome metrics. SalesRep reports 42.5x average ROI, 500K+ conversations per month, 99.9% uptime and 1,000+ businesses across 30+ countries. It also cites a 60% faster sales cycle, an 81% conversion rate with AI Agents and 3x more leads from ads. Customer stories describe AirCharter.com cutting its sales cycle by 60% with the omnichannel inbox for private jet charter bookings, Maroc Ventes eCommerce handling support and sales across WhatsApp, email and live chat from one inbox with 3x faster quotes, Sport Center reaching an 81% conversion rate with AI Agents on SalesRep, and Sip.ma tripling leads and streamlining customer communication. The stated promise is to transform customer conversations into revenue. SalesRep is marketed as built for a range of business types, each with a concrete workflow. Clinics and healthcare use it for bookings, confirmations, reminders and patient follow-ups right inside WhatsApp. Sales teams use it to capture, qualify and close leads from ads and chats in one visual pipeline. E-commerce and retail teams use it for order updates, abandoned-cart recovery and product campaigns that convert. Salons and local services use it for appointment scheduling, deposits and no-show reduction on autopilot. Customer support teams use one shared omnichannel inbox, with AI resolving the routine majority. Real estate and agencies route property and project inquiries to the right agent in seconds. A separate workflow example shows quote requests for hardware being received via WhatsApp, assigned to an agent, tracked through lifecycle stages such as Proposal, Warm Lead and Purchased, and answered with a quote attached. SalesRep targets businesses that sell and support customers through messaging apps — sales teams, support teams, e-commerce and retail operations, clinics, salons and local services, and real estate agencies — and reports being used by 1,000+ businesses worldwide. Integrations named in the content include HubSpot, Salesforce, Jira and Zapier, plus Shopify, WooCommerce and PrestaShop, and the product states it connects to 40+ tools; it also supports Zapier, Make and n8n, API access and webhooks, and Meta & TikTok Ads and Catalog. Pricing starts at $39/mo for the Starter plan with 3 users included and +$12/seat, then $20 per extra 1,000 conversations that never expire, with conversation-per-month tiers of 1000, 2500, 5000 and 10000+. The Growth plan is $79/mo with 5 users, the Advanced plan $139/mo with 10 users, and Enterprise $249/mo with 20 users. A 21-day free trial requires no credit card. SalesRep emphasises safety and onboarding. It uses the official WhatsApp Business API and enforces Meta's messaging policies — opt-ins, approved templates and sending limits — so your number stays safe, and the team helps resolve any issues with Meta. The WhatsApp Business API requires its own dedicated number, which can be new or migrated, and SalesRep guides users through every step. Teams can migrate contacts, templates and automations from other tools or from the WhatsApp Business app without interrupting ongoing conversations. The free trial lasts 21 days with no credit card required, including help connecting channels and launching first automations, with evaluation extensions available on request. Most teams launch within a few days, and guided onboarding is free on every plan. The platform is available in 30 languages and AI agents converse naturally in Arabic (including Darija), French, English, Spanish and more, with a tone you control. AI agents do not replace teams; they take over repetitive work such as answering common questions, qualifying leads and routing conversations, handing anything complex to the right human agent with full conversation context. As a summary, SalesRep combines an omnichannel team inbox, AI sales agents, workflow automation and broadcast marketing into one platform for businesses that sell and support customers across messaging channels. Its central value proposition is that a virtual sales rep can pull live prices and stock, answer in the customer's own language, generate and send quotes, follow up until the deal closes, and hand off to a human whenever needed — turning everyday customer conversations into revenue while reducing response times and manual workload.
Sellio is an AI customer support platform built around one shared inbox. It collects website live chat, WhatsApp, Instagram, Telegram, and email conversations into a single place so a team can reply, take notes, and hand off work without losing context. Beyond the inbox, Sellio adds tickets, automations, an AI agent, and analytics. The AI agent is trained on your own knowledge — your site, docs, and FAQs — so its answers stay grounded in what you actually ship. Sellio is positioned for stores, hotels, SaaS teams, local businesses, help desks, and agencies: organizations that answer customer questions across several channels and want one shared inbox for every conversation, with AI added only when they are ready. The problem Sellio addresses is fragmented, easily missed customer conversations. Customer messages arrive on website chat, WhatsApp, Instagram, Telegram, and email, and when those conversations live in separate tools it becomes hard to keep track of the next step. Sellio's site puts it plainly: missed replies look the same in every industry. By keeping every reply, note, and handoff in one thread, the product aims to make the next step always clear, whether the conversation was raised by a live chat visitor, a WhatsApp message, or an email. It also raises work from a conversation as a ticket when a single reply is not enough, so nothing falls through the cracks as a thread turns into ongoing work. At the center of Sellio is the shared inbox. Every conversation lives in a clear thread that holds every reply, note, and handoff, so whoever picks it up next can see the full history and the clear next steps. The inbox is designed for teams: multiple people can work the same queue together rather than trading messages in private tools, and every channel lands in one place instead of being scattered across apps. When a conversation turns into work that continues beyond a single reply, Sellio raises a ticket from it, letting the team assign it and keep the full history attached. This means a support question can move from a live chat greeting to an assigned ticket without ever leaving the platform. The AI agent is optional and arrives when you decide you are ready. It is trained on your knowledge by pointing it at your site, docs, and FAQs, which keeps its answers grounded in what you actually ship rather than generic responses. It is designed to cover the first reply on website chat, so common questions get an immediate answer. When the AI cannot finish the job, it hands off to a person without losing context — the human agent inherits the same thread and history. Sellio also lets teams stay in control of cost, and the free plan includes one AI agent and five AI conversations to get started. Analytics are built to show what to fix next. Sellio follows every conversation to how it ended and how it felt, placing automation, resolution, and CX rates beside each other in one funnel. Topics are drawn from real chats, so teams can see what customers actually ask about, and response time is tracked so it can improve over time. CSAT is described as explaining itself, tying satisfaction back to the conversations that produced it. Automations are part of the same toolkit, sitting alongside the inbox, tickets, AI agent, and analytics in the product's main navigation, so routine steps can be handled while people focus on the conversations that need them. Getting started is deliberately simple. Sellio asks you to add one line to your site: a single script tag that installs an on-brand live chat widget, free to start. Website chat and the shared inbox are free, and the AI is used only when you want it. Email and messaging channels start on the Mini plan, and you pay only when you need more. Channels connect so that website chat, WhatsApp, Instagram, Telegram, and email all land in one inbox, and Slack, Discord, and Teams can mirror new conversations for your team. The overall approach is to centralize every conversation first, then layer AI on top of a knowledge base you control. For users, the outcome is one place to work rather than several. Teams see every reply, note, and handoff in a single thread, so the next step is always clear and colleagues can pick up work as a team. AI gives a first reply on website chat and hands off without losing context, so customers are not left waiting while staff stay in control of cost. Tickets keep ongoing work attached to the conversation that started it, and analytics turn real chats into topics, response times, and CX and CSAT measures that show what to fix next. Sellio describes itself as support that fits your industry, with concrete scenarios for each. For ecommerce, live chat on Shopify sits in the same inbox as WhatsApp, Instagram, and email. In hospitality, WhatsApp, Instagram, and website chat share one inbox for the front desk and operations. SaaS teams put website chat first and every other channel beside it in one shared inbox. Local businesses get a live chat bubble on their site plus the channels their neighborhood already uses. For help desks, work is raised from a conversation, assigned, and kept with its full history. Agencies get a shared inbox their team can assign, note, and resolve together, and any website can start with one script tag. Sellio is aimed at stores, hotels, SaaS, local teams, and agencies — anyone whose customers message on a mix of channels. Supported conversation channels are website chat, WhatsApp, Instagram, Telegram, and email, with Slack, Discord, and Teams able to mirror new conversations. The integrations page lists Stripe, ClickUp, Zoom, Salesforce, Discord, Telegram, Trello, GitLab, WhatsApp, Messenger, Jira, Linear, Shopify, Notion, Microsoft Teams, Zapier, Instagram, Asana, Slack, HubSpot, and GitHub, noting that six integrations are available now and the rest are on the way. Pricing starts free: website chat and the shared inbox cost nothing, with no trial clock and no credit card. The Free plan includes two seats, one channel, and one API key, plus one AI agent and five AI conversations. Nothing expires, and paid plans add more seats, channels, AI agents, and AI credits. Sellio's core value proposition is straightforward: one shared inbox for every conversation, the channels customers already use, and the numbers that show how every answer went. Start free with website chat and the shared inbox, then add AI when you are ready.