Services
Three ways to work together — each designed for a specific stage of the AI journey. Strategy before build. Build before operationalize. No lock-in.
01
Define the right system before building the wrong one.
Most AI initiatives fail at the architecture stage — not because the technology is wrong, but because the design doesn't account for the operational reality of running it in production. This engagement establishes the AI architecture that fits your context: data flows, integration points, model selection, and the operational patterns that will keep it working after launch.
Best fit: Technology leaders who need to get alignment on direction before committing budget to build.
What's included
Current-state assessment of your AI landscape and gaps
Target architecture design (Agentic, RAG, or hybrid)
Integration pattern recommendations for your existing stack
Build-vs-buy decision framework for your specific constraints
A documented architecture you can hand to any engineering team
02
Working systems, not slide decks.
Hands-on engineering alongside your team. This is not advisory — it is delivery. The output is a working, deployable system: a RAG pipeline that answers real questions from your actual data, an Agentic workflow that automates a real process, or an AI-assisted engineering capability that accelerates your team.
Best fit: Teams with a defined problem and a commitment to ship — not teams still exploring whether AI applies.
What's included
Working prototype in the first two weeks
Iterative delivery with visible progress at each checkpoint
Engineering documentation and runbooks for your team
Handover session with the engineers who will maintain it
Post-delivery support window (2 weeks) to stabilize in production
03
The work that happens after the demo works.
Getting an AI system to work in a demo is the easy part. Getting it to work reliably at scale, with observable failure modes, consistent quality, and a team that can maintain it — that is a different problem. This engagement is for organizations that have something working and need to make it production-grade.
Best fit: Engineering teams with a working prototype that hasn't made it to reliable production yet.
What's included
Observability and evaluation framework for your AI system
Failure mode analysis and mitigation patterns
Cost and latency optimization for production load
Team enablement so your engineers can own it going forward
Incident response runbooks tailored to your deployment
Every engagement follows the same four steps — regardless of which service fits your situation.
01
A focused 60-minute session to understand where you are, what you're trying to achieve, and whether there's a fit. No pitch deck. No proposal until I understand the actual problem.
02
A written proposal with a clear scope, timeline, and fixed price. No open-ended retainers, no hourly billing with uncapped exposure. You know what you're getting before we start.
03
One engagement at a time. I work with one client per sprint cycle, which means your work gets full attention — not a share of capacity spread across a portfolio.
04
Every engagement ends with documentation, a handover session, and a short support window. The goal is your team owns it — not a dependency on me to keep it running.
Honest about constraints — so the right clients reach out.
Teams still in the "should we do AI?" stage — I work best once there is a specific problem to solve.
Organizations looking for a staff augmentation resource to backfill capacity.
Projects requiring large team delivery — this is a focused, individual practice.
Clients who need a vendor to own the outcome indefinitely — the goal is your team's ownership, not a managed service.
Start with a diagnostic conversation — 60 minutes, no obligation, focused on your actual problem.