Skip to content

// 06 · Service

AI strategy & audit

A clear-eyed read on where AI pays off and where it does not — an architecture review, an honest risk and feasibility assessment, and a prioritised roadmap before you commit budget.

  • OpenAI
  • Anthropic
  • Gemini
  • Ollama
Start with a Sprint

// The problem

Why this is hard

The pressure to 'do AI' produces a lot of expensive projects that were never going to work. Before you commit budget, you need an honest read on where AI actually pays off, what it will cost to run, and what the real risks are — from someone with no incentive to sell you the biggest build.

// What we build

What you get

Opportunity & ROI map

Where AI pays off in your product, ranked by impact and effort — and where it doesn't.

Architecture review

A clear-eyed assessment of your current (or proposed) approach, benchmarked across providers.

Risk & feasibility assessment

The honest read on cost, reliability, and failure modes before you build.

Prioritised roadmap

A sequenced plan you can act on, starting with the highest-leverage, lowest-risk move.

// How it fits together

The system we build

  1. Discovergoals + constraints
  2. Map opportunitiesimpact × effort
  3. Review architecturebenchmark providers
  4. Assess riskcost + feasibility
  5. Roadmapsequenced plan
A representative shape — abstract by design; we build it in your stack and your conventions.

// Deliverables

  • Opportunity & ROI map
  • Architecture review
  • Risk & feasibility assessment
  • Prioritised roadmap

// How we work

From prototype to production, in four moves.

01

Discovery

We map the problem, the data, and the eval that defines "done".

02

Prototype

A working slice in weeks — real model, real data, measured.

03

Production

Hardened, observable, evaluable. Shipped where users live.

04

Scale

Cost, latency and reliability tuned as load and scope grow.

// Typical engagement

What it takes to work together

  • Architecture Sprint

    $4–8kfixed · 1–2 wks

    1–2 weeks

    De-risk before you build — architecture, a plan, and a working proof-of-concept.

  • Build

    from$20kfixed scope or pod

    typically 1–3 months

    Ship the product end-to-end, in your repo and conventions.

  • Run & Scale

    from$4k/ month

    ongoing

    Operate and improve after launch — SLA, monitoring, and iteration.

Indicative ranges — the final price and timeline depend on your project's scope, complexity, and integrations. A paid Architecture Sprint pins them down.

// FAQ

Common questions

We benchmark across providers — including open and local models — and have no incentive to recommend the biggest build, so the recommendation follows the evidence, not our margin.

A prioritised roadmap you can act on — an opportunity and ROI map, an architecture review benchmarked across providers, and an honest risk and cost assessment — not a slide deck of generic AI trends.

Yes — plainly, with the reasoning. Telling you where AI won't pay off is part of the value, and it's far cheaper to learn that in an audit than three months into a build.

// Related

All services
15+systems shipped
6+ yrsin production
~4 wksto a first slice
99.9%uptime SLA

// Let's build

Ready to build with ai strategy & audit?

Tell us where you are. We reply within a day with a concrete next step.