Introspection provides an agent cloud that lets services firms encode their specialists' judgment into production-ready AI agents. The platform uses versioned "agent recipes" to define workflows, skills, and evaluation criteria, enabling teams to inspect, modify, and improve agent behavior over time. It targets domains like consumer lending, royalties auditing, and healthcare operations, where complex, judgment-heavy processes can be automated and continuously refined.
Funding
Funding not disclosed
Founders
Product
Problem
Services firms in finance, law, and human capital accumulate decades of expert judgment and institutional knowledge, but this intelligence remains locked in the minds of specialists and is difficult to scale or convert into AI capabilities. Building frontier AI systems has traditionally required dedicated research and engineering organizations with substantial resources, putting this capability beyond the reach of most services firms.
Solution
Introspection provides an agent cloud that brings AI research capability inside the firm, allowing domain experts to encode their judgment into production-ready agents. The platform uses versioned "agent recipes" that define how an agent works, which skills and tools it uses, and how its outcomes are judged, making agent behavior reproducible and governable. Specialists can direct the system to investigate failures, incorporate expert corrections, and test improvements against representative cases before deployment. The runtime gives agents the systems, context, and permissions needed to perform real work across internal teams and client engagements, while production experience and expert feedback feed into a continuous research loop that improves agent performance over time.
Target Audience
Primary customers are established services firms in finance, law, human capital, and healthcare that need to convert their specialists' institutional judgment into scalable AI agents for internal operations and client-facing services.
Features
- Versioned agent recipes that define workflow logic, skills, tools, and outcome evaluation criteria in source-controlled files
- Evaluation framework that tests proposed improvements against representative cases before production deployment
- Production evidence loop that turns observed failures and expert corrections into better releases and stronger quality gates
- Long-horizon vertical agents capable of multi-step tasks like borrower onboarding, document chasing, income verification, and underwriter handoff
- Domain-specific judges that enforce compliance rules and handoff completeness standards
- Skills library and MCP (Model Context Protocol) integration for connecting agents to external systems and tools