Enterprise AI consulting services
I help teams design multi-agent systems, build RAG and GraphRAG retrieval, tune models for domain tasks, and move AI pilots into governed production platforms. Each engagement has a defined scope, timeline, and handoff.
Where I help
Four ways I help teams build and ship AI systems.
How I scope projects
I define the success measure, evaluation plan, and handoff before implementation. The starting point can be retrieval, agent workflows, model behavior, or architecture.
Multi-Agent Systems
Design multi-agent systems with explicit tool access, memory boundaries, approvals, and specialist handoffs.
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, GraphRAG & Knowledge Retrieval
Combine semantic search with entity relationships when answers depend on both document meaning and connected business data.
Best suited for
Products and internal copilots that need trusted answers from documents, data systems, and live business context.
Knowledge graph, ingestion, and indexing design
Graph-vector retrieval with reranking and citations
Model Fine-tuning & Optimization
Use fine-tuning, prompt design, dataset curation, and inference tuning to improve domain accuracy, latency, and consistency.
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
Design identity, data flow, deployment, monitoring, and ownership for AI systems that must run beyond the pilot.
Best suited for
Teams moving from an AI experiment to a governed platform with clear operational ownership.
A reference setup for services and data flow
Security, compliance, and deployment patterns
Ways to work together
Choose a scope that fits the problem.
Use these as starting points. I share rates and a delivery estimate on the first call.
1-2 weeks · Fixed scope
AI Architecture Audit
Review an existing AI system across retrieval quality, agent design, evaluation coverage, cost, and failure modes.
You get
Scored findings with fixes ordered by risk and effort.
2-3 weeks · Fixed scope
Discovery Sprint
Test the use case, data readiness, and architecture options in 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 agent workflows or RAG pipelines with evaluation, deployment, documentation, and team handoff.
You get
Production system with monitoring, documentation, and handoff.
Monthly retainer
Fractional AI Architect
Ongoing architecture reviews, technical roadmaps, and delivery oversight for teams without a full-time AI architect.
You get
Weekly architecture sessions plus async design reviews.