AI looks promising. But before any leadership team commits its AI budget, the key question is usually the same: how much does it cost to build AI? “Somewhere in the range of $30,000 to $500,000+,” many AI services…
AI Consulting Services
You walk away with a ranked list of AI use cases worth funding, a feasibility verdict on the top one tested against your own data, and a costed roadmap you can take to a board.
If the answer is that none of it is worth building yet, you get that in writing too, in week two rather than month nine.
Trusted By Global Clients, Including Fortune 500 Companies
A ranked use case register
Your candidate ideas scored against each other on value, data, feasibility, adoption and consequence, so funding goes to the strongest one.
A feasibility verdict
Pass, conditional pass or fail on your top candidate, tested against real data extracts rather than schema documentation.
A costed roadmap
Phases with dependencies, a cost band per phase, and the evidence required to pass each decision point.
A fixed estimate
A number for phase one, not a range that moves. Yours to execute with us, in house, or with anyone else.
70+
AI and ML specialists
300+
Engineering specialists
2700+
Projects delivered
1800+
Clients served
15+
Years in business
ISO 42001: 2023
ISO 27001: 2022
ISO 9001: 2015
SOC2 Type II
HIPAA
GDPR
What AI consulting solves
Most organizations arrive with five to twenty candidate AI ideas and no common scale to compare them. The ideas came from department heads, vendor demos and competitor announcements, and they are not comparable: one is a cost reduction with a measured baseline, one is a revenue idea with no baseline at all, one is a compliance requirement with a deadline and no business case.
The decisions that determine whether an AI project reaches production are made before anyone writes code: which problem to attack, whether the data can carry it, who owns the metric it moves, and what happens to it after launch. This engagement answers all four on paper, at a fraction of what finding out during a build costs.
CONSULTING, POC, OR FULL BUILD
AI OPPORTUNITY ASSESSMENT
An AI opportunity assessment identifies where AI would change a business outcome, sizes that change, and ranks candidates so funding goes to the strongest one. The output is almost always a shorter list than the one it started with.
Adoption is the dimension that most often reorders a list. Back-office document workflows tend to outrank customer-facing assistants on the same value estimate, because the people affected already treat the manual version as overhead. Where consequence scores low, the compliance questions route to AI governance consulting before the roadmap is finalized, so the cost of oversight is priced in rather than discovered mid-build.
AI FEASIBILITY STUDY
An AI feasibility study tests whether one use case can be built at the accuracy your business process requires, using the data you actually hold, inside the systems you actually run. “Can AI read invoices” has a known answer. “Can AI read these invoices, from these forty-one suppliers, at the field-level accuracy your finance controls require, without a human checking every one” does not, and is the only version worth paying to answer.
A conditional pass is a normal result. It means the use case is viable once a named prerequisite is met, and the roadmap sequences that prerequisite ahead of it. Where the study fails on data, the question stops being about this use case and becomes a foundations question, answered by an AI readiness assessment.
Data
Does representative data exist in volume, accessible under existing agreements? Tested against real extracts, because the gap between an extract and its schema documentation is where most studies find their answer. Failure here is the most common result and the cheapest to discover.
Accuracy ceiling
What accuracy is achievable, and what does the process require? Two separate numbers. The required one comes from the process owner, not from engineering.
Integration
Can the system read from and write to the systems of record involved, at the required latency and permissions? Legacy platforms without APIs and write restrictions in a system of record are what most often turn a technical pass into a conditional one.
Economics
Does inference cost at production volume leave a positive margin against the value estimate? A use case failing only here usually survives at lower volume or on a smaller model, and the study says which.
AI PoC Development
MindInventory builds a working version of one use case against your real data and scores it against a pass mark your process owner sets before we start. We agree the kill criterion in that same conversation, so a negative result ends the program on evidence instead of drifting into a second round of funding. A PoC that fails tells you as much as one that passes, at a fraction of what finding out during a build costs. What we run is a proof of concept, not a pilot: it works on historical or sampled data with our engineers watching. Putting it in front of real users needs an operations plan and is scoped separately.
The kill criterion is the item most often omitted and the one that separates a PoC from an expensive demo. A PoC with no defined failure state cannot fail, which means it cannot inform a decision.
A frozen sample of real cases with agreed correct outputs, assembled by your domain experts rather than by the vendor.
A single number, set by the process owner, above which the PoC succeeds.
Current human or system performance on the same set. Without it, an accuracy number means nothing.
Which error types are tolerable and which are not. Not all wrong answers cost the same.
The result at which the program stops rather than being extended.
What you keep if the PoC ends here, including the evaluation harness.
AI ROADMAP
An AI roadmap sequences validated use cases into phases gated by evidence rather than by dates. MindInventory writes yours to make the dependencies visible: which capability has to exist before the next one is possible, and which phase is paying for infrastructure the later phases will use. Each gate states the result that lets you proceed, so you approve phase two on a number your own team can check rather than on a status update.
We sequence by dependency rather than by business priority. That gives you a less impressive phase one and a considerably higher chance of reaching phase three, because the highest-value use case almost always depends on data plumbing nobody has funded. Where you need the headline use case first, MindInventory prices the foundations work into that phase rather than leaving it to surface mid-build.
Layer
Foundations
What it covers
Data access, pipeline reliability, the evaluation harness
Why it sits here
Funded inside phase one, because a standalone data project is hard to approve and easy to cancel
Layer
Anchor use case
What it covers
One use case, chosen on the ratio of value to dependency rather than on value alone
Why it sits here
Proves the pattern, establishes internal ownership, and produces the reference numbers that fund the rest
Layer
Adjacent use cases
What it covers
Candidates reusing the anchor's pipeline, integration surface and evaluation approach
Why it sits here
Marginal cost per use case drops sharply here, which is where a program stops looking like unrelated projects
Layer
Capability extension
What it covers
New model types, data domains or integration surfaces
Why it sits here
Priced as new foundations work, because that is what it is
Roadmaps have a shelf life. A phase-three architecture chosen today is a hypothesis. Roadmaps written here name the decision phase three has to make and which assumptions to re-test at each gate, rather than naming the technology it will use.
COST AND TIMELINE
Deciding what to build is priced separately from building it, and the size of the eventual build does not affect the cost of the decision.
What moves a consulting estimate is the number of use cases in scope, whether data can be accessed directly or only described, and whether the use case carries a regulatory classification that must be established before an architecture can be recommended. Build bands run from $25,000 to $150,000 and above, set out with their cost drivers on AI development services.
The two-week feasibility assessment below covers one named use case and is offered without charge. Paid discovery is broader: multiple candidates, a full data inspection, and a sequenced roadmap.
When you should not buy this
MindAI builds AI systems for environments where being wrong has consequences. That focus makes some engagements a poor fit, and it is cheaper to say so here than to find out in week three.
You already know what to build and can prove it.
Named use case, accessible data, identified owner. Scope a proof of concept instead.
You cannot build anything yet.
If data is scattered across systems with no pipelines, owner or quality baseline, use case selection is premature. That is an AI readiness assessment.
Audit is the real gate.
If procurement, legal or a regulator is what actually blocks you, the work is classification and control design, which sits with AI governance consulting.
A live system is drifting.
That is a monitoring and retraining problem, handled by MLOps consulting.
Nobody owns the business metric.
With no operational leader accountable for the number a system would move, the engagement produces a roadmap nothing acts on. This is the most common reason we decline consulting work.
Start with one use case
Name a use case and give us access to a data owner. In two weeks you get a written feasibility recommendation, architecture options and a fixed cost estimate. Sometimes the recommendation is not to build it, and that arrives in the same document.
Frequently Asked Questions
Explore answers to common questions about AI Consulting Services
Consulting decides what to build and whether it is viable. Implementation builds it. The two are often sold together, which is why the deliverable question matters: a consulting engagement should produce a document that stands on its own if you never proceed to a build.
Two weeks, covering one named use case with access to real data. That is enough for a feasibility verdict and a costed estimate. Shorter engagements rely on described data rather than inspected data, and that gap is where most pilots discover their problem.
Usually the reverse. A validated first use case shapes a workable strategy faster than a strategy document identifies a use case. Starting broad tends to produce a roadmap nothing acts on, because no individual phase has a business owner who asked for it.
Partly. Opportunity assessment and architecture selection work from interviews and documentation. Feasibility does not. A verdict based on schema documentation rather than real extracts is an opinion.
Three roles: a business owner per candidate use case, someone with authority over the data, and one domain expert who can define what a correct output looks like. The third is the scarcest and typically needs eight to sixteen hours across the engagement.
For a sustained program, yes, and preferably both. An internal lead owns the metric and the prioritization, which no external party holds durably. Hiring one before there is a validated use case for them to own is the sequence that most often ends with the role vacant again inside a year.
Normalize on the deliverable, not the day rate. Ask what document exists at the end, who performs the data review, how many of your internal hours it consumes, and what transfers to you at close. The published market range spans roughly an order of magnitude at similar scope, driven more by overhead structure than by quality of recommendation.
Later phases are written as decisions rather than as technology choices, because they will move. The roadmap names what phase three has to decide and which assumptions to re-test at each gate, and the architecture is designed so a model can be replaced without rebuilding the system around it.
RELATED SERVICES
Explore our other related services to enhance the performance of your digital product
AI Readiness Assessment
A scored audit of data, team, infrastructure and governance, for when the question is whether you can build anything yet.
AI Governance Consulting
EU AI Act classification, ISO 42001 alignment, decision provenance and AI security, for when the question is whether it will survive audit.
The full build practice, with cost bands, delivery process and production case studies.
MLOps Consulting
Evaluation suites, drift monitoring and retraining, for systems already in production.
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