Gotham has its detective. Production AI needs one too. I build the systems that catch what others miss — evaluation harnesses, red-teams, autonomous agents — engineered to hold up long after the demo ends.
Five compartments. Every one earns its place — no gadget carried that hasn't been tested under fire.
Prompt systems and typed contracts that hold up under production pressure, not just demo day.
Vector search built to survive drift — with detection that fires before quality quietly decays.
Agents that check each other's work, orchestrated with typed state so failures can't slip through silently.
Catching regressions and adversarial breaks before Gotham — or a customer — finds them first.
The plain, unglamorous scaffolding underneath every gadget above — the part that actually ships.
Before the case files, there's the training. Every detective starts somewhere.
Five investigations, five case files pinned to the board. Pull the thread on any one.
Cinematic, line-by-line AI code walkthroughs — LLaMA 4 via Groq returns a structured script driving an animated timeline with voice narration, variable tracking, and a live call stack.
Benchmarked GPT-4o and Claude Sonnet across 50 MMLU prompts with ROUGE-L, BERTScore and bootstrapped 95% CIs — cutting regression detection from days to under 4 minutes.
A 3-agent LangGraph pipeline — static analysis, OWASP scanner, LLM fix proposal — autonomously flagged 23 issues including 4 critical vulnerabilities.
Production RAG with Qdrant vector search and cosine-similarity drift detection that auto-triggers re-indexing, plus a live ops dashboard tracking p95/p99 latency.
A tool-augmented agent running a 4-step reasoning chain — market context to investment thesis — with a full SHA-256 reproducibility audit trail.
Got a problem worth solving — a system that needs to actually hold up? Shoot your shot.
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