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.
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Your candidate ideas scored against each other on value, data, feasibility, adoption and consequence, so funding goes to the strongest one.
Pass, conditional pass or fail on your top candidate, tested against real data extracts rather than schema documentation.
Phases with dependencies, a cost band per phase, and the evidence required to pass each decision point.
A number for phase one, not a range that moves. Yours to execute with us, in house, or with anyone else.
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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.
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.
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.
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.
What accuracy is achievable, and what does the process require? Two separate numbers. The required one comes from the process owner, not from engineering.
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.
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.
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.
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.
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.
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.
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.
Named use case, accessible data, identified owner. Scope a proof of concept instead.
If data is scattered across systems with no pipelines, owner or quality baseline, use case selection is premature. That is an AI readiness assessment.
If procurement, legal or a regulator is what actually blocks you, the work is classification and control design, which sits with AI governance consulting.
That is a monitoring and retraining problem, handled by MLOps consulting.
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.
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.
Explore our other related services to enhance the performance of your digital product
A scored audit of data, team, infrastructure and governance, for when the question is whether you can build anything yet.
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.
Evaluation suites, drift monitoring and retraining, for systems already in production.
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