[ A ranked list, not a strategy document ]
The common failure in AI consulting is a strategy document nobody can act on — fifty pages of market context, a maturity model, and a recommendation to "explore opportunities." Our AI consulting services end differently: a ranked list of use cases with feasibility assessed against your actual data, effort and cost estimates you could procure against, and a clear statement of what to do first. We can build it, but the plan stands alone.
The common failure in AI consulting is a strategy document nobody can act on — fifty pages of market context, a maturity model, and a recommendation to "explore opportunities." Our AI consulting services end differently: a ranked list of use cases with feasibility assessed against your actual data, effort and cost estimates you could procure against, and a clear statement of what to do first. We can build it, but the plan stands alone.
If you have a list of AI ideas and no way to rank them, book an assessmentwhere AI creates value in your specific operating model, ranked and sequenced.
candidates scored on business value, technical feasibility and data readiness.
honest evaluation of whether a use case is buildable with your data today.
architecture, build-versus-buy analysis, vendor selection and delivery planning.
where LLMs genuinely help versus where they are the wrong tool, and what governance is needed.
how AI capability connects to your existing systems without a re-platforming programme.
policy, risk framework, data governance and regulatory position.
operating model, capability building and portfolio governance across multiple initiatives.
Asked
What is the refund window on a bulk order?
Retrieved from your documents
Answered
Bulk orders over ₹50,000 can be returned
within 21 days of delivery, against the
standard 7 days. The goods must be unopened
and in original packaging.
The model arranged the sentence. Every fact in it — the amount, the window, the condition — came from a document you own, and the source is attached so a wrong answer can be traced rather than argued about.
Workshops with the people who own the processes, a mapped inventory of candidate use cases, and each one scored on value, feasibility and data readiness. Typically two to four weeks.
For a specific use case: does the data exist, is it labelled, is the accuracy ceiling above your decision threshold, and what would it cost to run at volume. One to two weeks, and frequently the cheapest "no" you will ever buy.
A sequenced twelve-to-eighteen-month plan with dependencies, capability requirements, build-versus-buy positions and budget ranges per initiative.
Independent evaluation of AI vendors, platforms and models against your requirements, including the questions to ask that vendors would rather you did not.
Acceptable use policy, risk classification, human oversight requirements, data handling rules and a regulatory position — usable rather than aspirational.
Whether to build an internal AI team, use partners, or run a hybrid; what roles you need; and how AI initiatives get funded and governed.
For teams already building: an assessment of architecture, evaluation practice, cost model and production readiness, with a prioritised remediation list.
with process owners, not only leadership, because the useful use cases surface from people doing the work.
what exists, what is measured, what is documented and what is realistically accessible.
value, feasibility and data readiness, with candidates explicitly ruled out and the reasons recorded.
sequenced initiatives with effort, cost, dependencies and expected return.
the plan is yours to execute with anyone; we can advise during delivery or stay out of it.
We are consultants who build, which changes the advice. A consultancy that does not deliver has no feedback loop on whether its recommendations were realistic. Our estimates come from having built the same kinds of systems, which is why they tend to be higher and more accurate than strategy-only firms'.
We will tell you not to use AI. A substantial share of the use cases brought to us are better solved by fixing a process, cleaning a dataset or writing conventional software. Saying so costs us build revenue and is the reason our recommendations are worth acting on.
Engagements are short and fixed-price. Two to six weeks, defined deliverables, fixed fee. Open-ended consulting retainers create incentives we would rather not have.
You can take the plan elsewhere. Everything we produce is yours and is written to be procured against by any vendor. If our roadmap only makes sense if we build it, it is not a roadmap.
Most AI failures are data and process failures. The technology is rarely the constraint. Missing data, undocumented processes, no clear decision owner and no baseline metric are what stop projects. We assess these first because they determine whether anything downstream is feasible.
Start where the process is already measured. If you cannot state the current cost, cycle time or error rate, you will not be able to prove the AI system improved anything — and unprovable improvements do not get funded for phase two.
Buy before you build, where it fits. For common capabilities — transcription, translation, standard document extraction, off-the-shelf support bots — buying is usually faster and cheaper. Building earns its place when the capability is differentiating or the data is proprietary.
Governance before scale, not after. Once several teams are using AI independently, retrofitting policy is a political exercise rather than a technical one. Frameworks such as the NIST AI RMF and ISO/IEC 42001 give you a defensible structure, and the EU AI Act imposes obligations on deployers, not only builders — relevant if you operate in or serve the EU.
Capability matters more than any single project. The organisations getting value are the ones that built the ability to run AI projects repeatedly — evaluation practice, data access, governance and deployment paths — rather than the ones that shipped one impressive pilot.
Fixed-price, fixed-duration engagements. No open-ended retainers.
Indicative cost: an AI opportunity assessment runs $6,000–$15,000 over two to four weeks. A single-use-case feasibility assessment runs $3,000–$6,000 over one to two weeks. A full AI strategy and roadmap engagement runs $15,000–$35,000 over four to six weeks. Teams that would rather build than assess can validate a single use case. A technical architecture review of an existing build runs $5,000–$12,000. Advisory support during delivery is available monthly for teams executing the plan themselves.
If you have a list of AI ideas and no way to rank them, the assessment is the right first step. Two to four weeks, fixed price, and you leave with a scored, sequenced plan you could hand to any vendor.
An opportunity assessment runs $6,000–$15,000; a single-use-case feasibility assessment $3,000–$6,000; a full strategy and roadmap engagement $15,000–$35,000. All fixed-price.
Let’s talk about your ai consulting project. No obligation, just a conversation.
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