What We Do
AI consulting, AI architecture, dev team enablement, product management, fractional CTO / CPTO, fractional architect, and M&A technical due diligence.
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AI Consulting
Strategy that ends in a shipped system
Our AI strategy work ends in a system carrying production traffic: a readiness assessment, an adoption path sequenced by payback rather than ambition, and navigation through governance - the part that decides the timeline. Half the value is telling you where not to apply AI; the other half is the sequence through the use cases that survive. In an enterprise the model is the easy part - risk, legal, security, and audit decide whether anything ships.
Proof: We took the first production AI workload at a heavily regulated company through that company’s first AI governance framework - risk, legal, security, and audit, with PII and financial data in scope. The full account is on the Clients page. The system behind the work - six frameworks, seven practices - is documented in full on the AI Frameworks page.
AI Design & Build
The AI layer, designed and built
Our flagship engagement: a 3-6 month build that takes a company’s content and data and turns it into an AI capability running inside their platform. Ingest and break down the data, apply security and access patterns, do the AI work as orchestrated LLM pipelines, and inject the results back where the product uses them. Delivered by a small embedded team - a lead, a product person, two engineers.
We build it as workflow systems: deterministic orchestration owns the steps and invokes the model at bounded points, with retrieval mixed in - vector search where similarity helps, structured queries where precision matters. Two modes: embedded alongside your engineers, or a full buildout we hand over. Same patterns either way - the difference is whose hands write the implementation.
Proof: Production AI architecture built hands-on for an enterprise technology company building a new AI product line - their engineers now extend the patterns.
AI Dev Team Enablement
AI-augmented engineering, with numbers attached
Teams struggle with AI coding tools because they treat AI as one workflow when it is three - and the wrong one is how you ship fast and break what you cannot explain. We teach the Three Disciplines, and AI Code Nines: an SRE-style error budget for AI-written code, so stabilization gets scheduled from data instead of an incident. On one engagement we measured ~48K lines over 15 days at a near-constant 3-4K a day - the constancy is the finding. Once you can see the gear, you can manage it.
Proof: The curriculum is the AI Engineering Operating Model - the same six frameworks and seven practices we run our own builds on. The pieces behind it were extracted from a measured engagement with a small B2B SaaS engineering team, written up on the Clients page.
Product Management
The value narrative
Every product delivers visible value the user came for and latent value they do not know matters yet - and that line decides where you can afford friction. Friction before visible value is an optimization problem; friction before latent value is unrecoverable churn that never registers as a choice, it registers as nothing. The same blindness splits the buyer from the user, and most pitches pick one and lose the other.
The named method is Value Stream Mapping: a one-line pitch built out to experiences, activities, and components, with each persona held to three key interaction moments and six end-to-end touch points - the limit is what surfaces priority.
Proof: Value Stream Mapping for Product Definition in Resources - the full method, end to end.
Fractional CTO / CPTO
Technology leadership, value-optimized
Interim or part-time CTO and CPTO work. The job is decisions: what to build, who builds it, what it costs, and when the org needs to change shape - org design, hiring, roadmap ownership, budget, and the translation layer to the board. We have done it as employees first: scaled an engineering organization from 20 to 50, restructured four teams into seven, and carried the eight-figure budget that came with it.
The CPTO half adds product accountability - strategy built with business stakeholders, roadmap continuity through leadership transitions, and an explicit list of what the org is not building, which anchors a strategy as much as what it is.
Proof: Led a 50-person engineering organization at a heavily regulated company - scaled from 20, restructured from four teams to seven. Two decades of Director, VP, and CTO roles across Fortune 500 and startup environments.
Fractional Architect
Architecture measured by what good looks like
Part-time, embedded architecture - platform, delivery infrastructure, CI/CD, production readiness, hands-on in the repositories. Maturity is defined by operational tests, not adjectives. The standards we hold a platform to:
- A new engineer has the full product running locally in under two hours - on the laptop they received that morning.
- That same engineer deploys to production on day one. If the pipeline cannot make that safe, the pipeline is the problem, not the engineer.
- Deploys happen during peak traffic with nobody watching a dashboard. Ship the fix, go to lunch.
- Every application deploys at least once every 30 days, even when no code changed - vulnerabilities emerge in dependencies that were clean when you built them.
- Every system that holds state gets a user interface. Bring users to the data so they self-serve instead of filing tickets.
DORA describes the results; these five are the how - hit them and elite-tier numbers follow as a side effect. No organization meets all five on day one, so the standards order the roadmap - and the fractional engagement is the ordering.
Proof: The Quality Drawdown piece in Resources - measuring engineering health like a portfolio, from data already in your git log.
M&A Technical Due Diligence
The buy-or-build question
AI has turned every capability roadmap into a buy-or-build question, and acquiring an AI-native company is the buy branch. We price it: buy- and sell-side technical diligence - IP review, architecture and team capability, integration risk. The question the deal hangs on is not the stack inventory - it is whether the team keeps shipping inside your org, and what is defensible versus a replaceable wrapper around someone else’s API. That valuation difference is most of the deal.
Proof: M&A technical diligence on AI-native acquisition targets for an enterprise technology company, written up on the Clients page - alongside buy-side and sell-side work across two decades of engineering leadership.
What to expect
A typical AI build: three to six months, ending in something running
Engagements take the shape of the problem - but the most common shape is three to six months, with the first shippable system around month three. Here is what that looks like before the first call.
Weeks 1-3
Review
Where you actually are: architecture, team, data, AI readiness. The output is positions you can act on, not a readout deck.
Weeks 3-6
Product definition
What to build first and why: the value stream, buyer versus user, and an MVP defined narrowly enough to ship inside the engagement.
Weeks 6-12
Delivery to MVP
Built to production patterns - by your team with us embedded, or by ours. Either way, your engineers can extend it without us.
Week 12+
Then it’s yours
Exit cleanly with the patterns in your repository, or continue fractional - leadership, architecture, or enablement at the cadence the work needs.
Start the conversation
A working session: your situation, your constraints, and what we would do first.
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