Ways to work together

Structured ways to get senior judgment into a decision that is already moving.

These are formats, not packages. The work is the same underneath: frame the decision, make the tradeoffs explicit, and leave the team with a path they can execute. Scope follows a discovery call.

Lowest commitment · 90 minutes · Paid

Inference working session

Use a paid 90-minute inference working session to resolve one bounded decision and identify the next test.

One clearly framed inference decision
Options and explicit trade-offs
The next measurement or experiment
A short written decision note after the call

Ask about a working session

Signature

1 week

Inference Readiness Review

Find out where your production inference path breaks before it breaks — quota, provider concentration, cost per request, and where the latency budget is actually spent.

Best for: AI-native teams running production inference at real volume, with a model, provider, or capacity decision arriving in the next 90 days and no written record of what was chosen or why.

Quota and provider risk map with named single points of failure
Model-substitution matrix and the eval evidence each swap needs
Latency budget decomposed by hop, and fallback strategy
90-day sequence with named owners and decision gates

2-4 weeks

AI Architecture Sprint

Turn one consequential AI product or system decision into a production-ready architecture and a delivery path the team owns.

Best for: Teams with something important enough that the architecture cannot be improvised: an agent, a retrieval system, or an AI-native capability moving toward production without a senior architect in-house.

Target architecture and architecture decision records
System boundaries, guardrails, and human review paths
Evaluation, observability, latency, and cost plan
Phased delivery sequence and technical handoff

1-2 weeks

AI Direction Sprint

Answer which AI opportunity actually deserves to become a product initiative — and why the others can wait.

Best for: Founders and CTOs with strong market insight, several plausible AI directions, and no shared basis for choosing what deserves to ship first.

Opportunities scored by value, feasibility, uncertainty, and risk
Readiness and capability gap assessment
One prioritized bet with a defensible rationale
90-day sequence with owners and decision gates

Ongoing, usually after a sprint

Fractional AI Architect

Senior architecture judgment embedded beside the founder and engineering team, before the company needs or can justify the role full time.

Best for: Startups making a steady stream of consequential technical decisions — model and vendor choices, production reviews, roadmap tradeoffs — with no one senior enough to hold the architecture.

Founder and architecture working sessions
Model, vendor, and platform decisions
Architecture and production reviews
Technical risk, evaluation standards, and team unblockers

3-6 weeks

AI Engineering Enablement

Turn architecture decisions into shared engineering practice, so the same lessons stop being relearned team by team.

Best for: Scale-ups already running AI in production, but with inconsistent evaluation, unclear ownership, and no shared review standard.

Shared architecture principles and review standards
Evaluation and production workflow patterns
Ownership model and right-sized governance
Office hours and adoption measures

A good fit when

There is real momentum and a customer or market signal
AI is strategically relevant, not decorative
Several plausible technical directions are open
Architecture decisions are becoming consequential
The team is capable but has no senior AI architecture leadership
The company moves too fast for a traditional consulting engagement

Probably not me

A chatbot or a basic automation with no product ownership behind it
Generic AI training disconnected from an operating change
Inexpensive development capacity
Implementation resources rather than technical judgment

One active architecture or direction sprint at a time, plus a small number of advisory relationships.