All work

CompanyJames Chase ClientDigital Composite RoleSenior Python/Go Consultant PeriodMarch 2025 — Present TypeConsulting · AI/LLM

Building the future of data privacy search

Consulting through James Chase for Digital Composite — production-grade RAG and inference powering a global data-privacy knowledge base across 127+ jurisdictions, with vector search latency cut in half.

Python FastAPI RAG LangChain pgvector ChromaDB Postgres Go

127+

Jurisdictions covered globally

~50%

Reduction in vector search latency

99.99%

Availability target for Go microservices

The engagement

Digital Composite needed to bring data-privacy research to production scale — a global knowledge base that legal and compliance teams could query in real time across more than a hundred jurisdictions. Not a research notebook, not a demo. A system that lawyers and analysts could rely on around the clock.

I was engaged through James Chase to own architecture and delivery end-to-end on the AI stack.

What I built

Production-grade RAG, end-to-end

Led the design and implementation of the full RAG stack using Python, FastAPI, LangChain, ChromaDB, pgvector, and Postgres.
Designed inference pipelines integrating multiple LLM providers with tool calling, retrieval strategies, and structured outputs — engineered for latency, determinism, and graceful failure rather than research-grade experimentation.
Built an LLM evaluation framework covering retrieval quality, response accuracy, and regression testing so prompt, model, and retriever changes could be validated before deployment.

Performance and cost engineering

Cut vector search latency by ~50% through indexing strategy, chunking refinement, and hybrid retrieval tuning.
Integrated response-reuse patterns to reduce redundant LLM calls and control cost without degrading response quality.
Architected and managed Golang microservices with a 99.99% availability target.

API-first AI services

Deployed AI services as containerized FastAPI workloads with versioned prompts, model routing, and safe rollout strategies.
Designed maintainable, testable Python codebases with clear separation of orchestration, retrieval, inference, and API layers.
Operated with minimal supervision, owning architecture decisions end-to-end.

"Most LLM systems work in a demo. Far fewer work at 3am with real users and real data. That gap is where I spend my time."