AI Strategy & Architecture
From ambiguous AI ambition to a concrete plan with budget, architecture, and milestones.
A senior engineering team reads your problem, your data, and your constraints — then hands back an honest architecture, a phased plan, and a budget you can defend to your board.
What we ship
Concrete deliverables, not features.
01
Prioritized use case inventory
2–3 use cases that compound, ranked against impact, feasibility, and your team's readiness. Each with rough sizing and a recommended starting motion.
02
Reference architecture document
Concrete diagrams: data flow, model layer, application layer, deployment topology. Vendor-agnostic, tailored to your cloud, compliance, and existing stack.
03
Phased delivery plan with budget
Discovery → first production system → operations. Each phase has scope, duration, and budget. Defensible to your CFO, board, and procurement.
04
Build vs buy decision matrix
Where to leverage existing tools (LLM providers, vector DBs, observability) vs where custom engineering compounds. Honest about both, no vendor allegiance.
How it works
Discovery first. Weekly cycles. Pause anytime.
- Discover01
Discovery sprint
Two weeks, fixed scope. Working prototype on your data, architecture decision record, scoped follow-on proposal.
- Design02
Discovery & strategy
Weekly cycles with demos and decision points. Scope can be adjusted, paused, or ended after any cycle. No surprises at the end.
- Build03
Ship to production
Code in your repositories. Deployment in your cloud by default. Runbook written for your team to operate, monitor, and extend it.
- Operate04
Optional retainer
Monthly retainer to maintain, monitor, and extend the system. Quarterly review against business outcomes. No auto-renew.
Where it compounds
Scenarios where this work pays back.
01 · Use case
Pre-investment scoping
When a CFO or board has asked for an AI roadmap with credible numbers — and you need an engineering-first answer, not a deck from a consulting firm.
Typical output: phased plan with 3–5 phases, each independently scoped and shippable.
02 · Use case
Architecture review of a stalled initiative
Existing PoC stuck in dev. We diagnose what broke (data model, infra, eval gap) and write a re-architecture plan that takes it to production.
Typical engagement: 4 weeks, ends with a go/no-go on the original system.
03 · Use case
Vendor selection for a custom stack
You're choosing between Anthropic / OpenAI / open-source / multiple vector DBs / observability tools. We benchmark on your data and recommend, with reasoning.
Typical output: side-by-side benchmark on your data + cost model for 12 months at scale.
Engagement
Fixed scope after discovery.
- Duration
- 4–8 weeks
- What you get
- Architecture document, phased plan, scoped follow-on proposal
- Start with
- Discovery sprint, 2 weeks
Frequently asked
Questions before the first call.
Why pay for strategy instead of just starting a PoC?
Most stuck PoCs we see were started without an architecture review. The strategy work is what protects the much larger investment you'll make in the build. We're explicit when a use case shouldn't be pursued — that's part of the value.
How is this different from a McKinsey / BCG AI report?
Those are deck-driven and built by consultants who won't ship the system. We're engineers — every recommendation comes with a concrete deployment topology, vendor benchmark, or eval plan that engineering can act on the next day.
Do you commit to building what you recommend?
We can, but it's optional. Many clients run discovery with us and the build with their internal team or another partner. The architecture document is yours to use however you choose.
What do you need from us to start?
A 90-minute kickoff with the stakeholder who owns the problem, access to representative data (under NDA), and one technical contact who can answer questions during the 4–8 weeks. That's it.