Work with me
I help teams design multi-agent systems, build RAG architecture that retrieves the right things, fine-tune models for their domain, and take AI pilots to secure production platforms. Here's what each engagement looks like.
Where I help
The work usually starts in one of these lanes.
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.
Multi-Agent Systems
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
RAG Architecture & Vector Search
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
Model Fine-tuning & Optimization
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
Enterprise AI Architecture
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
Engagement models
Concrete scopes, timelines, and deliverables.
Every engagement starts from one of these formats. Baseline rates and a delivery estimate are shared in the first scoping call.
1-2 weeks · Fixed scope
AI Architecture Audit
A structured review of an existing AI system: retrieval quality, agent design, evaluation coverage, cost, and reliability risks.
You get
Scored findings report with a prioritized remediation roadmap.
2-3 weeks · Fixed scope
Discovery Sprint
Shape an AI idea into a validated plan: use-case framing, data readiness, architecture options, and a working proof of concept.
You get
Reference architecture, PoC, and a delivery estimate.
4-12 weeks · Milestone-based
End-to-End Delivery
Design and ship a production AI system: agent workflows, RAG pipelines, evaluation loops, deployment, and team handoff.
You get
Production system with monitoring, documentation, and handoff.
Monthly retainer
Fractional AI Architect
Ongoing architecture ownership for teams that need senior AI leadership without a full-time hire: reviews, roadmaps, delivery oversight.
You get
Weekly architecture sessions plus async design reviews.
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