The RAG answers.
Our own judge grades it.
A closed loop between two of the modules: Module 4 retrieves and answers, Module 1 scores that answer with a versioned LLM-as-judge. Nothing here is a mockup — every panel is a real run. Prove it works, don't just demo it.
These are the components behind AI systems that answer from your own data — and prove their answers are right. The RAG module, for example, is the pattern behind “draft this proposal informed by every proposal we've ever written.” Everything here is a real, measured run, not a demo.
Five modules, one instrument panel
Eval harness
LLM-as-judge with versioned prompts, drift detection, and cost/latency per run.
Proof the AI's answers are actually good — measured, not assumed.
MCP server
A guardrail layer around every tool call — auth, allowlist, rate limit, audit.
Safe, audited connections between AI and your real systems.
Agentic pipeline
A ReAct agent with HITL gates, prompt-injection defense, and a span-level trace.
Multi-step work with a human approval gate before anything irreversible.
RAG pipeline
Real Cohere embed + rerank, with a full retrieval trace and recall@k.
Answers grounded in your own documents — past projects, reports, proposals.
Cross-module loop
RAG answers scored by the Module 1 judge — the eval loop, end to end.
The whole system grading itself.
Every module ships with a hermetic test suite and a one-command demo. Read the code.