Where I help
What usually matters
Some teams need a stronger retrieval core, others need multi-agent workflows or a safer path from pilot to platform. These are the patterns I most often design around.
Design multi-agent systems that coordinate tools, memory, approvals, and specialist roles without becoming hard to manage.
Best suited for
Teams replacing repetitive manual work with planner, reviewer, and executor workflows.
Clear agent roles and handoffs
Human review steps and safety rails
Build retrieval systems that surface the right context quickly, keep answers grounded, and make knowledge easier to use.
Best suited for
Products and internal copilots that need trusted answers from documents, data systems, and live business context.
Ingestion, chunking, and indexing plan
Hybrid retrieval with reranking and citations
Improve quality, speed, and consistency with the right mix of fine-tuning, prompt shaping, dataset design, and inference optimization.
Best suited for
Teams that already see promise but need better domain accuracy, tone control, or task fit.
Dataset curation and benchmark framing
LoRA or PEFT fine-tuning workflow design
Shape the end-to-end AI setup so it is secure, observable, and easy for real teams to run over time.
Best suited for
Teams moving from early AI experiments to dependable platforms, governance, and long-term use.
A reference setup for services and data flow
Security, compliance, and deployment patterns