AI · Strategy

AI Consulting and Strategy

The work that happens before any model gets built. Find the AI use cases worth building, and sequence them.

Overview

In short: The work that happens before any model gets built. Find the AI use cases worth building, and sequence them. Find the AI use cases worth building, sequence them into a roadmap, and decide on the architecture before any code is written.

AI consulting is the work that decides whether AI is worth building into your product, and where. Most teams have a list of AI ideas. Most of those ideas do not earn their cost. We run a structured pass on your use cases, score them on feasibility and return, and come back with the three or four worth building and the order to build them in. The output is a roadmap, an architecture, and a build-versus-buy recommendation you can take to engineering or to investors.

What we do

Capabilities

Opportunity mapping

We take your list of AI ideas and the problems your product actually solves, and find the use cases with real return. The rest get cut or parked.

Feasibility and ROI

Each candidate gets scored on data readiness, technical risk, build cost, and expected return. You see which ideas are cheap wins and which are money pits.

Roadmap and sequencing

A phased plan that puts the quick wins first and the expensive bets later. Sequenced so each phase de-risks the next one.

Architecture design

The shape of the system before any code is written. Which models, which data stores, which inference paths, and where the human review sits.

Model and vendor selection

Open-source versus frontier model. Build versus buy. Vendor lock-in cost. We write the recommendation down so procurement and engineering can act on it.

Governance framework

The policies, review cadence, and audit trail that keep an AI program inside your risk appetite as it grows.

What you get

Deliverables

Use case register, scored and ranked
Phased AI roadmap with dependencies
Architecture document
Build-versus-buy recommendation
Data readiness assessment
Governance and review framework

Technology

Tools we build with

Assessment

Feasibility scoringROI modellingRisk mappingData audit

Planning

RoadmappingArchitecture designVendor evaluationTeam planning

Engagement

How you can work with us

Prototype

2 to 4 weeks

A focused proof of concept that proves the hard part of your idea works. Clickable UX, the core feature running end to end, and a link you can share.

Testing the idea with users or investors

Team augmentation

Flexible

Our engineers embed alongside your team or take a separate workstream. We work with the stack and rhythm your team already uses.

Adding capacity to your existing team

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FAQ

AI Consulting and Strategy questions

What does an AI consulting engagement look like?

It runs as a focused two to four week sprint. We review your product and data, interview the people closest to the problem, score your use cases, and come back with a roadmap and architecture. You leave with a document you can hand to engineering or take to a board.

We already have an AI roadmap. Can you pressure-test it?

Yes. A common engagement is a review of an existing roadmap or a vendor proposal. We check the assumptions, flag the risks, and tell you where the plan is optimistic. That is often more useful than starting from scratch.

Do you help with model and vendor selection?

We do. Choosing between OpenAI, Anthropic, Google, and an open-source model is a real decision with cost, latency, and lock-in consequences. We write the trade-offs down so the choice survives the next round of model releases.

Will the roadmap be useful if we build with another team?

Yes. The output is vendor-neutral. We write it so your own engineers, another agency, or our team can pick it up and build against it.

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