Entelligence AI
Entelligence builds AI-powered engineering intelligence for teams using AI-assisted development. It connects code, pull requests, agent sessions, incidents, deployments and production outcomes so teams can improve quality, reliability and cost as engineering work compounds.
At a Glance
- Software engineering organizations
- Engineering leaders
- Teams using AI coding agents
- Enterprise software and technology companies
- +1 more
AI Tools by Entelligence AI
(1)
agentplay
Coding Agent Session Replay Tool
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Latest News
Initial public release of agentplay, an Apache-2.0 local coding-agent session explorer
Published the Self-Improving Coding Agents whitepaper
Let the Cheap Model Decide: Luna-First Code Review Found 12 More Bugs Than GPT-5.6 Sol at the Same Cost
GPT-6 Astra Cost 1.6x More Per Verified Bug Than GPT-5.6 Sol
Products & Services
Contextual pull-request and local-change review using repository-wide context, team guidelines, historical learnings and production evidence; supports prioritized findings, sequence diagrams, summaries, feedback learning and an agentic review/fix/re-review loop.
Routes coding-agent requests per turn by quality and cost, using policies such as Balanced and Eco, with Proxy or BYOK access and savings analytics.
Analytics for AI coding-agent adoption, spend, token waste, context health, budgets, workflow behavior and engineering outcomes.
Conversational interface over Entelligence's World Model, answering questions across code, tickets, deploys, incidents and agent sessions in the web app, Slack and CLI.
Market Position
Entelligence positions itself as a broader engineering-intelligence and operational-memory layer rather than only an AI code-review tool. Its code review is differentiated by repository-wide context, historical review learnings and production incidents; its adjacent Model Router, Agent Insights, reliability workflows and Ellie interface extend that context across cost and production. The site explicitly compares its offering with CodeRabbit, Greptile, BugBot, LinearB and Workweave.
Leadership
Founders
Aiswarya Sankar
Founder and CEO; LinkedIn identifies her as an ex-Uber engineer and lists earlier work as NLP Lead at ML Collective, as well as machine-learning and software-engineering roles.
Executive Team
Aiswarya Sankar
Founder & CEO
Ex-Uber; previously NLP Lead at ML Collective and held machine-learning/software-engineering roles.
Harsh Singhal
Head of Product & Design
Leads product and design at Entelligence.
Founding Story
Entelligence was started to address the gap created when AI-generated code accelerated software development without making the resulting code production-ready. The initial vision was an AI Tech Lead that could understand a team's codebase and engineering context, review AI-generated code, maintain documentation and provide better measures of engineering impact. The company's later vision describes a compounding loop in which reviews, routed models and production fixes leave reusable context for the next task.
Business Model
Revenue Model
Commercial plans combine billed seats, usage, product access and deployment terms; billing can include subscriptions, LLM usage/overage and Model Router credits or balance. Enterprise deployment can use the customer's cloud and keys.
Pricing Tiers
Plan referenced on the pricing page; product access and terms vary by plan.
Plan referenced on the pricing page; product access and terms vary by plan.
Includes Agent Insights; commercial terms are negotiated and can include deployment and workspace controls.
Target Markets
- Software engineering organizations
- Engineering leaders
- Teams using AI coding agents
- Enterprise software and technology companies
- Organizations with production reliability and compliance requirements
- Reviewing AI-generated code before production
- Preventing recurring bugs and production incidents
- Reducing coding-agent model and token spend
- Measuring engineering outcomes and AI adoption
- Investigating incidents, deployments and delivery bottlenecks
- Keeping architecture and agent instructions current
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