
Ask Ellie is an AI-powered chat agent from Entelligence AI that lives inside Slack and serves as a direct answer engine for engineering teams. It pulls data from code repositories, release pipelines, incident logs, and product signals to answer natural language questions instantly. The core value is eliminating the need to dig through multiple dashboards or context-switch between tools — instead, users get one clear, contextual answer. This engineering AI assistant is designed for leaders, managers, and developers who need fast, reliable insights without manual data gathering. It connects signals across GitHub, Linear, and other tools to provide a unified view of the engineering workflow.
Engineering leaders often struggle to get quick answers about team velocity, code health, or production stability because information is scattered across different platforms. Ask Ellie solves this by enabling anyone to ask questions directly in Slack and receive composited answers that pull from real-time data. This reduces time spent on manual reporting and context switching, allowing teams to make faster, more confident decisions. The pain point is especially acute during releases or incident response, where every minute counts. Ellie’s ability to surface what changed and why eliminates guesswork and helps teams stay aligned.
The first major feature is "Generates reports on demand." Ellie understands PRs, reviews, commits, and delivery flow from GitHub and Linear. Users can ask questions like "What changed this sprint?" or "What slowed down the last release?" and Ellie will analyze the data to provide a concise report. It works by fetching commit history, pull request statuses, and cycle time metrics, then synthesizing them into a plain-English answer. This is useful because it removes the need to manually compile spreadsheets or log into multiple tools — the answer is delivered instantly in Slack.
The second feature group is "Answers real engineering questions." Instead of presenting raw metrics or dashboards, Ellie gives direct responses with a chart only when it enhances understanding. Users ask questions in Slack like "Which teams have the best vs worst cycle time?" or "What’s our bug backlog trend?" and Ellie returns a natural language answer. The system is integrated with Slack, so it fits naturally into existing workflows. This approach reduces cognitive load because users don’t need to interpret complex dashboards — they get the insight they need, in their preferred channel.
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The third feature set includes "Understand what happened in production" and "Turns answers into action." Ellie connects releases, errors, and incidents to show exactly what changed and what broke after a deployment. It also links signals across tools — for example, correlating a spike in errors with a recent code change. This allows teams to quickly identify the root cause of incidents without manual investigation. Additionally, Ellie can automate error tickets in Linear based on incident history, turning insights into automated actions. The system builds a model of the team’s codebase and failure patterns, learning over time which components are risky.
Ellie works by building a comprehensive model of the engineering system. It learns ownership structures and recurring failure patterns from code, delivery, and product signals. It reasons across time — comparing what happened before, during, and after events to provide contextual answers. Ellie also understands intent behind work, distinguishing planned changes (like feature work) from regressions or incidents. Furthermore, it tailors answers based on who is asking: engineers get technical details, managers get summaries, and leaders get strategic overviews. This workflow ensures that every stakeholder gets the right level of detail without unnecessary noise.
Concrete use cases include founders asking "How is the team moving over time?" to gauge long-term productivity trends. Engineering managers ask "Are there any incidents after deployment?" to quickly assess release impact. Teams ask "What should we prioritize right now?" and receive prioritized context from open tasks, incidents, and code changes. Ellie also integrates with Posthog for product analytics, enabling questions about user conversion and feature usage. Another real scenario is automating sprint analysis for code quality and deliverables — Ellie can summarize a sprint’s outcomes, identify bottlenecks, and suggest improvements. Outcomes include faster incident resolution, better sprint planning, and improved code health.
Ask Ellie is designed for engineering leaders, CTOs, VP Engineers, and engineering managers who need real-time visibility into team performance and production health. It also benefits individual engineers who want quick answers without leaving Slack. The platform integrates with GitHub, Linear, Posthog, and other standard tools, and the pricing page provides details on plans. By replacing scattered dashboards with a conversational AI assistant, Ask Ellie helps teams move faster with confidence. The core promise is engineering visibility without adding more dashboards — one place to ask, one clear answer, no digging, no delays.
Engineering leaders, CTOs, VP Engineers, and Engineering Managers who need immediate visibility into team performance, code health, and production reliability. Software engineering teams who want to reduce context switching and get answers directly in Slack without logging into multiple tools. Founders and team leads looking to understand engineering output and prioritize effectively. Also suitable for individual developers who need quick code or incident context during their daily workflow.