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AI Consulting Services Decide Before You Build

Our AI consulting services help you decide what to build before you commit an engineering budget. Over two to four weeks we audit your workflows and data, rank the highest-return automation candidates, estimate cost and effort for each, and hand over a prioritised roadmap you own outright.

  • Fixed fee, named deliverables
  • The roadmap is yours to keep
  • Priced separately from any build
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How it runs · 5 steps

  1. Workflow and data audit
  2. Opportunity mapping
  3. Build-vs-buy on each item
  4. Governance decisions
  5. Costed roadmap handover

Built with

  • Workflow audit
  • Data readiness review
  • Access mapping
  • ROI modelling
  • Effort estimation
  • Risk register
  • Privacy review
  • Model hosting options

More in AI Services

Questions first?Talk to an engineer

The most expensive AI mistake is not a failed model

It is six months spent building a system for a workflow that was not costing much in the first place. Consulting front-loads that judgement: which processes are genuinely expensive, which are automatable at acceptable accuracy, what your data actually supports today, and what compliance requires before anything ships.

We will also state the obvious conflict of interest. A firm that both advises and builds has an incentive to recommend building. Ours is managed by pricing discovery as its own contract, and by writing down the option of doing nothing — with its cost — alongside every recommendation.

The Problem

What this usually fixes

  • Competing internal proposals, no way to rank them

    Three departments each have an AI idea and there is no shared basis for comparing them, so the loudest sponsor wins.

  • A pilot stalled and nobody diagnosed why

    Something was built, it did not stick, and the reason was never established — so the next attempt repeats it.

  • Data that will not support the ambition

    The use case assumes clean, accessible, permissioned data. Nobody has checked whether that data exists in that condition.

  • Governance discovered late

    Privacy, residency, and human-oversight questions surface during procurement instead of during design, and stall the launch.

How We Work

The process

Each step produces something you can review — a document, an environment, or working software — rather than a percentage in a status report.

  1. Workflow and data audit

    What your data can actually support today — where it lives, how clean it is, who may see it, and what must change before a model can be trusted with it.

  2. Opportunity mapping

    Candidate use cases ranked by hours recovered, error rate reduced, or revenue influenced, each with an effort estimate and an honest confidence level.

  3. Build-vs-buy on each item

    Where a subscription product wins, we say so. Custom is recommended only where your process is genuinely the advantage.

  4. Governance decisions

    Privacy, model hosting, data residency, and human oversight written down before they become a compliance question.

  5. Costed roadmap handover

    A sequenced plan with milestones, effort, and risks named — delivered as a document you own and can execute with anyone.

What You Get

Why teams choose this

  • A plan that survives scrutiny

    Ranked, costed, and defensible to a board, with the assumptions behind each estimate written down rather than implied.

  • Fewer wasted quarters

    The cheapest place to change your mind is week two. Discovery makes that possible instead of discovering it in month five.

  • Internal literacy

    Your team leaves the engagement able to evaluate the next proposal without an outside opinion.

  • No lock-in

    The roadmap is yours, including the option to take it to a different firm. That is what keeps the recommendation honest.

Stack

Technologies we build with

Tools we have delivered production work on, not a capability matrix. We pick per project and will explain the trade-off behind each choice.

Assessment
  • Workflow audit
  • Data readiness review
  • Access mapping
Analysis
  • ROI modelling
  • Effort estimation
  • Risk register
Governance
  • Privacy review
  • Model hosting options
  • Human-oversight design
Industries

Who this is for

Most valuable where manual workload is significant and in-house AI expertise is thin.

  • Healthcare
  • Financial services
  • Insurance
  • Manufacturing
  • Logistics
  • Legal & professional services
  • Education
  • Public sector suppliers
Questions

AI Consulting: common questions

Related reading

What do AI consulting services actually deliver?

AI consulting delivers a decision document, not code. A typical engagement produces four artifacts: an audit of current workflows and data readiness, a ranked list of candidate use cases with estimated effort and expected return, the governance decisions covering privacy and model hosting, and a sequenced roadmap with costs and risks attached. The test of a good engagement is whether you could execute the plan with any provider, including one that is not us.

Who should hire AI consulting, and who should skip it?

Hire consulting when the business case is unclear — competing internal proposals, a stalled pilot with no diagnosis, or a regulated deployment needing governance settled first. Skip it when you already know what to build and can state its success criteria; in that case you need engineering, and paying for a strategy phase delays the thing that matters. Startups building an AI product almost always fall into the second group and should go straight to discovery and build.

How long does an AI consulting engagement take?

Two to four weeks for most readiness-and-roadmap engagements, running as a fixed-fee project with named deliverables and a defined end date. Larger organisations with several business units or heavy compliance requirements can run to six weeks. We deliberately do not sell open-ended advisory retainers as a first engagement — scope on advisory work drifts easily, and a fixed end date is what forces the roadmap to be finished rather than extended.

Will you recommend building something regardless?

No, and the structure is what makes that credible rather than the promise. Discovery is priced as its own contract, so our fee does not depend on a build following it. Every recommendation is written alongside the option of doing nothing and its cost, and where an off-the-shelf product beats a custom build we name the product. We would rather lose the implementation than hand over a roadmap you regret executing.

What are AI consulting services?

AI consulting services are advisory engagements that help a business decide where artificial intelligence will produce a measurable return before anything is built. A typical engagement audits current workflows and data readiness, identifies and ranks candidate use cases, estimates effort, cost, and expected benefit for each, resolves governance, privacy, and compliance questions, and delivers a prioritised roadmap. The deliverable is a decision document, not code. Consulting is most valuable when a company knows AI matters but holds competing internal opinions about where to start, or when a previous pilot stalled and nobody has diagnosed why. A good engagement ends with a plan the client can execute with any provider.

What services do AI consulting companies offer?

AI consulting companies typically offer seven services: AI readiness and data audits, use-case discovery and prioritisation, ROI and cost modelling, technology and vendor selection, proof-of-concept development, AI governance and compliance frameworks, and team training or change management. Some also provide fractional AI leadership for companies without an internal head of AI. Firms that both advise and build can carry a recommendation straight into implementation, which avoids the common failure where a strategy deck is delivered and nothing ships from it — but they also have an incentive to recommend building. Pure advisory firms offer more neutrality. Ask which model a firm follows and how it manages that conflict.

How much do AI consulting services cost?

AI consulting services typically cost $5,000–$25,000 for a two-to-six-week readiness audit and roadmap, with hourly advisory at roughly $150–$400 in North America and Western Europe and $50–$150 from specialist teams in India and Eastern Europe. Enterprise strategy engagements from large consultancies commonly start above $100,000. A proof of concept usually falls between $10,000 and $40,000 depending on data complexity. Many firms credit the discovery fee against a subsequent build, which is worth asking about explicitly. Treat any consulting proposal that cannot name its deliverables as documents — a readiness audit, a ranked use-case list, a costed roadmap — with caution, because scope on advisory work drifts easily.
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Not sure which problem to start with?

That is exactly what discovery is for. Two to four weeks, fixed fee, and the roadmap is yours whoever builds it.

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