AI Consulting
Readiness assessment, use-case selection and a pragmatic adoption roadmap. We map your current processes and quantify where AI changes the economics of the work, and where it does not.
AI consulting services: a roadmap first, then the team that builds it.
Our AI consulting services answer one question before you commit budget to a build: where does AI actually pay off in your business? We assess readiness, select the use cases worth pursuing and write a pragmatic adoption roadmap. If you choose to continue, the same senior team builds against that roadmap through a thirteen-phase lifecycle we publish in full. We sign an NDA before any technical discussion, and you own all code and IP.
What it covers
Three consulting lines, one accountable team.
AI consulting is one of our six service lines, and it can be engaged on its own, without a development commitment. It covers three kinds of work. See all six service lines.
Digital Transformation Consulting
Modernization strategy that connects legacy estates to AI-era capability. The plan favors incremental replatforming over a big-bang rewrite, so the business keeps running while the systems underneath it change. How we run a modernization.
Software Consulting
Architecture reviews, technical due diligence and delivery audits. When the work concerns an existing system, the review and audit leave a documented baseline, so whatever follows runs against facts rather than assumptions.
How an engagement runs
What our AI consulting services produce.
Consulting exists to produce a roadmap; development exists to build against one. Every step of a consulting engagement points at that document, and none of them assumes you will buy a platform.
01NDA, then discovery
We sign an NDA before any technical discussion, so your roadmap and data are confidential from the first call. Discovery then maps the business context, the stakeholders and the constraints before anything is proposed.
02Readiness assessment
We check whether your data and systems can support AI today. Is the data structured and consistent enough to use? Is there a path from where it lives to a system that can read it? Sometimes the honest answer is "close this gap first", and the assessment says so rather than recommending a build on data that cannot support it.
03Use-case selection
Candidate workflows are scored by how much manual time they cost and how automatable they are. The result is a prioritized list, so the first spend goes toward a proven bottleneck instead of a general-purpose AI platform.
04The roadmap
A pragmatic adoption roadmap with a clear recommendation on where to start, not a generic AI strategy deck. Goals become testable, prioritized requirements, and you approve them before any design work begins.
Why one team
Why consulting and delivery belong with one team.
The usual model splits the work. A strategy firm writes the plan, and a separate development shop inherits it. We were founded to build the systems we used to consult on, so there is no handoff between the two.
The whole process is published
Every engagement runs the same thirteen phases, from discovery to continuous improvement. Our SDLC page explains what happens in each phase, what you review and what you approve, so you can read the method before the first call.
NDA first, and the IP stays yours
An NDA is signed before any technical discussion, and complete transfer of source code and IP is written into every engagement. AI tools run under enterprise terms that exclude training on your data.
Senior engineers, accountable
The engineers who scope your project are the engineers who build it. AI speeds up the work in every phase, and a named senior engineer is accountable for every decision.
Consulting alone, or a pilot next
Consulting can be the whole engagement. If the roadmap points to a build, a fixed-scope pilot is the lowest-risk way to evaluate how we work: fixed price, fixed timeline, full IP transfer. How we work sets out every engagement model.
In practice
Readiness-first consulting, applied to two industries.
Two of our industry pages show what the readiness assessment looks like when it is scoped to one kind of business.
Construction: preconstruction readiness
For mid-size contractors, the assessment covers document review, estimating and bid management before any tooling gets built. It also names the audit-trail and documentation-accuracy requirements the bid process already has to meet. Read the construction page.
Manufacturing: data readiness for SMEs
For small and mid-size manufacturers, shop-floor data is scored for quality and for sensor and PLC integration maturity. There is no size floor; what matters is whether there is real data to work with. Read the manufacturing page.
Common questions
Readiness assessment, use-case selection and a pragmatic adoption roadmap. The same service line also covers digital transformation consulting and software consulting: architecture reviews, technical due diligence and delivery audits.
Start with AI consulting if the use case is still open, and with development if it is already settled. Consulting exists to produce a roadmap; development exists to build against one.
No. Any service line can be engaged on its own, so consulting can be the whole engagement. If you do continue, the same team builds against the roadmap through the same thirteen-phase lifecycle.
That is a normal, useful outcome, not a dead end. It tells you which gap to close first, such as data that is not yet structured or integrated, rather than building a model on data that cannot support it.
There is no price list, because scope drives price and honest scoping needs a conversation. We scope the problem with you before proposing anything, and a first project can run as a fixed-scope pilot with a fixed price and timeline.
An NDA is signed before any technical discussion, so your roadmap and your data are confidential from the first call. AI tools are used under enterprise terms that exclude training on your data.