Service

AI Strategy and Consulting

Roadmaps, feasibility checks and architecture reviews before you commit budget.

What I do

Help founders and product leaders decide what to build with AI, what to buy, and what to skip. The output is a document: the problem stated precisely, the data you have and lack, an architecture with costs, and a roadmap in phases that each deliver something usable.

How I work

I ask for real examples of the task, not a slide about it. I look at the data before I propose a model. I write down how we will know it works before anyone writes code. Recommendations come with the trade-offs I rejected and why.

When to call me

Before a budget is committed, when an AI initiative has stalled, or when you need an independent review of a proposal from a vendor or an internal team.

What you get

Written assessment of the problem, data and constraints
Architecture proposal with build-versus-buy options and costs
Evaluation plan that defines what "working" means
Phased roadmap sized for your team

Selected work

Case studies for this service

Independent Product R&D

TargetTarget Reference Architecture

Designing Secure Agent Transaction Infrastructure

A protocol-neutral reference design for giving AI agents bounded authority, exact approvals, isolated credentials, safe outcome handling and auditable transaction evidence.

Design outputs

The counts below describe elements in this target reference design and its public documentation; they are not observed production results or performance measurements.

7 controls

Risks, enforcement points and validation methods mapped

4 identities

Human, agent, workload and downstream credential kept distinct

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hypREspace

Production

hypREspace: Rearchitecting NL-to-SQL into an Agentic Analytics Engine

A brittle multi-hop prompt chain became one observable LangGraph ReAct agent with a custom MCP layer over the existing FastAPI backend and Claude on Bedrock.

Production outcomes

Production status reflects the deployed client workflow and its live traceability; no comparative performance figure is claimed.

One agent

Replaced a multi-hop prompt chain end to end

Every step traced

Failures diagnosed from the exact step trace

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Compliance-focused accounting technology startup (under NDA)

TargetTarget Reference Architecture

AI Research Platform for a Regulated Accounting Profession

End-to-end MVP architecture for cited, verified tax research answers: LangGraph agent, hybrid RAG, a claim verification gate and region-locked multi-tenant infrastructure.

Design targets

The 95% accuracy target, P50/P95 latency objectives, seven-day freshness target and eight-skill count are design objectives and reference-concept counts, not observed results.

95%

Accuracy target the architecture is built to

Zero

Target tolerance for fabricated citations in the proposed hard-fail gate

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Related work

Related projects

Secure Agent Transaction Infrastructure

Bounded authority for agents that can transact

Independent Product R&DTargetTarget Reference Architecture

Tool access does not by itself establish transaction authority. This protocol-neutral target reference architecture turns agent proposals into bounded, policy-checked, transaction-bound, credential-isolated and auditable external actions.

Deterministic PolicyTransaction-Bound ApprovalScoped Grants

Questions I get asked

What does an engagement look like?

Two to four weeks. I interview the people who own the problem, look at the data and systems, and deliver a written architecture and roadmap you can hand to any engineering team, including mine.

Will you tell me not to use AI?

If a rules engine, a search index or a spreadsheet solves it, yes. The report says so and explains why.

Do you stay on afterwards?

Often as a fractional technical lead or reviewer while a team builds. That is optional and separate from the strategy work.

Have a problem that looks like this?

Tell me about it. I reply within one working day with a first take and no sales pitch.

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