AI strategy that a senior engineer would actually write.
Where AI fits in your stack, what to build, what to buy, what to skip. Scoped honestly against your team's footprint and what you can operate after we leave.
- 2 to 4 week engagement
- written architecture and roadmap
- build vs buy matrix per workflow
AI Strategy, in plain terms.
We sit with your engineering and product leads, look at the systems you already run, and write a plan. The plan names the workflows where AI returns more than it costs, the ones that look promising but will quietly drain a quarter, and the ones to skip. It is opinionated. We tell you what we would do if it were our budget.
Build versus buy is decided on the merits. We have integrated commercial models, hosted open-weight models on our own infrastructure, and stitched both into existing applications. The recommendation comes with a reason. If a vendor gets you 80 percent of the way for a fraction of the engineering hours, we will say so. If your data residency or margin profile means you should self-host, we will say that too.
Data governance is a first-class concern in the plan. We name which data can move to a third-party API under your current agreements, which workflows require an on-prem or VPC-isolated endpoint, and whether retrieval-augmented generation closes the gap between a general model and what your specific corpus requires.
You walk away with a written architecture document, a sequenced roadmap, and the questions your team should be asking vendors. No proprietary framework, no certification track to push. Just the read a senior operator would give you over a long lunch, written down so the CFO and the engineer can both work from the same page.
Common triggers for this engagement.
- 01The board is asking for an AI plan and the answer needs to be more than a list of tools
- 02Your team has run pilots that did not graduate and you want to know why before spending more
- 03You are weighing a managed AI vendor against building in-house and the trade-offs are not clear
- 04Data sensitivity, compliance, or unit economics push you toward self-hosting and you need an honest architecture
Questions teams ask before signing.
How is this different from an AI consulting firm's strategy deck?
We run production infrastructure for a living. The plan is written by people who will have to live with the consequences if the architecture is wrong, so the recommendations are sized to what your team can actually operate.
Do you recommend specific AI vendors and models?
Yes. We name names, including the ones we would skip. We have integrated the major commercial APIs and hosted open-weight models in our own environment, so the recommendation is grounded in what we have seen behave in production.
Can you help us decide whether to self-host or use a managed API?
That is one of the core questions we answer. The decision turns on data sensitivity, request volume, latency tolerance, and your team's operational depth. We walk through each and give you a defensible answer.
What do we get at the end?
A written architecture document, a sequenced roadmap with rough effort estimates, a build-versus-buy matrix per workflow, and the question list your team should bring to vendor conversations.
Frequently scoped alongside this work.
Technology Assessments
An outside read on the systems you've been too busy to audit. Risk, fitness, and the three things that should change this quarter — written for the CFO and the engineer.
Fractional CTO
Senior technology leadership for the quarters that matter most — architecture calls, hiring strategy, and the bets your team is too close to make. We embed with your leadership, not your tooling.
Software Development
Production-grade builds for the systems your team is going to live with for years. We staff the work and ship it — not just architect it.
Cloud Consulting
Multi-cloud, hybrid, P2V migrations, and cloud-to-cloud network architecture. We tell you which one your stack actually needs — and which one to walk away from.
Compliance Readiness
Mapping your environment to the controls auditors actually check, and closing the gaps before the audit window opens. We've sat on both sides of that table.