Inspectcat gives engineering leaders visibility into AI usage and spend, repository readiness, and the documentation layer agents need to work safely across real systems.
Readiness Overview
Organization score
72 / 100
Highest blocker
Agent guidance
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Paste a public GitHub repository URL to see a concrete slice of the product: readiness score, checks, and practical findings for AI-assisted engineering.
Platform
Inspectcat connects repository readiness, AI usage visibility, and generated technical context so engineering teams can manage AI adoption as a real operating system.
Code readiness
Scan repositories for setup context, validation commands, agent guidance, architecture notes, and safety signals.
Start with a repository scan, get a readiness score, and fix the gaps that slow down AI-assisted engineering.
Learn MoreRepository scan
72 / 100
Highest blocker
Agent guidance
README context
ReadyValidation command
Needs workAGENTS.md guide
BlockedArchitecture notes
PartialAI usage and cost
Connect AI providers to track adoption, token usage, vendor mix, spend drivers, and projected run-rate.
Bring OpenAI, Anthropic, and GitHub Copilot usage into one view for active users, service accounts, model mix, token volume, spend, and forecasted cost.
Learn More2.7k
Active users
API keys
Service usage
Projected
Token cost
Generated context
Turn primary repositories, connected services, and maintained page lists into useful technical context.
Define the docs you need, map the repositories behind the system, and generate maintained technical context for people and agents.
Learn MoreInputs
main-web-app
primary repository
payments-service
HTTP data source
identity-api
auth dependency
Output
Generated docs site
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A subscription for leaders and engineering teams who want signal on AI-assisted development: adoption patterns, tooling shifts, and concrete ways to improve team leverage.