Product and strategy
AI consulting and strategy that ends in a build plan
We help you decide where AI will earn its keep in your business, test the leading ideas on your own data, and turn the result into a costed, sequenced plan that your team or ours can deliver.
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Advice from a team that also builds
- OpenAI
- Claude
- Gemini
- Llama
- LangChain
- LangGraph
- Hugging Face
- Python
- Jupyter
- pandas
- Snowflake
- Google BigQuery
Most organizations have more AI ideas than they can fund, and a few vendor demos that made everything look easy. What they lack is a way to tell which ideas will survive contact with their real data, processes and budgets. Krapton's consulting answers that with engineering evidence: we study the work, the data and the systems, then try the most promising ideas before anyone commits to a full build.
Because the advice comes from a team that builds and runs AI products, it stays practical. Each recommendation comes with the data it needs, the model and integration approach, the risks to manage, a running-cost estimate and an honest view of whether an existing tool would do the job better than custom software. Sometimes the right answer is to buy, to wait or to fix a process first, and we say so.
Engagements range from a focused review of a single idea to an assessment across several departments. Either way, you keep the outputs: the scored list of use cases, the prototype code and its results, and a roadmap written so that any capable team, including your own, can pick it up.
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Where consulting helps
Clients usually come to us at one of these moments. Each calls for a different first step, and we shape the engagement around it.
Choosing the first use cases
A structured review of candidate ideas, scored on value, feasibility, data readiness and risk, so leadership funds the few with a realistic path to production.
Data readiness review
An assessment of whether the data an idea depends on exists, can be accessed, is clean enough and may lawfully be used for AI, with the work needed to close each gap.
Build, buy or extend
A side-by-side comparison of custom development, off-the-shelf AI products and AI features in software you already pay for, covering fit, total cost, lock-in and data handling.
Proof of concept
A short, time-boxed prototype on your real data that answers one question, such as whether extraction accuracy is good enough, before any budget goes to a full build.
AI policy and governance
Practical rules on approved tools, the information staff may enter, how outputs are checked and who signs off new AI features, written for your size and sector.
Rescuing a stalled AI project
An independent review of a pilot that never reached production or a feature that disappoints, finding whether the problem lies in the data, the model choice, the evaluation or the scope.
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What you leave with
Use-case scorecard
Every candidate idea scored on value, feasibility, data readiness, risk and running cost, with the reasoning written down.
Feasibility findings
Prototype code, the test data used and measured results for each idea we tried, including the ones that failed and why.
Architecture outline
Recommended models, data flows, integrations and hosting for each approved use case, detailed enough for an engineering team to estimate from.
Cost and effort estimates
Build effort and projected monthly running costs for each option, with the assumptions behind every estimate stated.
Risk and governance notes
Data protection, security, human oversight and vendor considerations for each use case, ready for your legal and security reviewers.
Sequenced roadmap
The order to build in, the dependencies between items, the measures that define success and the decision points along the way.
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How a consulting engagement runs
Four stages, with a decision point at the end of each, so you can stop or change direction whenever the evidence says so.
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Understand the business
Interviews with the people who own the goals and the people who do the work, plus a look at processes, systems and existing data, produce a long list of opportunities rooted in real problems.
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Score and shortlist
Each opportunity is scored on value, feasibility, data readiness, risk and cost. Together we agree a shortlist and the question each idea must answer before it earns a build budget.
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Test on real data
Our engineers build quick prototypes of the shortlisted ideas using your data in a safe environment, measure the results against agreed thresholds, and record what worked, what did not and why.
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Plan the delivery
We turn the evidence into a roadmap with architecture, estimates, risks and success measures, present it to your decision makers, and help brief your own team or another supplier if you prefer.
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Responsible choices from the start
Good strategy covers more than technical feasibility. For every recommended use case we set out what personal or confidential data it touches, where that data would be processed, how outputs will be checked, who stays accountable for decisions, and how the system will be monitored once live. Where regulation applies, such as data protection law or sector rules in finance and health, we frame the questions for your legal and compliance advisors rather than giving legal advice ourselves.
Cost belongs in the same picture. AI features carry running costs that grow with usage, and some carry change costs as teams adapt how they work. Our estimates include both, alongside the cost of doing nothing, so the business case compares realistic options rather than an optimistic build against the status quo.
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Related services
AI MVP development
Launch a first AI product that proves demand.
Build AI agent for business
You want an agent that takes real actions — schedules, drafts, runs reports — not just answers questions.
Automate business workflows
Repeatable processes (onboarding, billing reminders, fulfilment) eat engineering and ops time every week.
Build LLM evaluation pipeline
You ship LLM features by vibes — there's no automated eval, so model swaps and prompt changes are pure gut feel.
Fix LLM hallucinations
Your LLM confidently invents facts — fake citations, made-up API responses, wrong product details — and users have noticed.
Digital transformation
Redefine and future-proof your business in this digital age.
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Frequently asked questions
What does an AI consultant actually do?
An AI consultant helps you decide where AI is worth using and how to adopt it responsibly. In practice that means reviewing your processes and data, scoring candidate use cases, testing the strongest ones, comparing build and buy options, and producing a roadmap with costs and risks. With Krapton, the engineers who advise can also build, but you are free to take the plan elsewhere.
How do we find the right AI use cases for our business?
Start with work that is repetitive, rules-based or language-heavy and that people already find slow. Then check three things: whether the data exists, whether errors can be caught before they cause harm, and whether the value justifies the running cost. We run that assessment in workshops with your teams and test the leading ideas on real data before recommending them.
Do we need a proof of concept before building?
Usually, when the core question is technical, such as whether a model can extract fields accurately from your documents or answer reliably from your knowledge base. A focused proof of concept on real data answers that for a fraction of the cost of a build. If the approach is proven elsewhere and your data is ready, we may recommend going straight to an MVP.
Should we build custom AI or buy an off-the-shelf tool?
Buy when a mature product covers the task, handles your data acceptably and does not lock you into terms you dislike. Build when the task is central to how you compete, depends on your own data or systems, or no product fits your workflow. We compare both openly, including AI features in tools you already pay for, and we are not tied to any AI vendor.
How long does an AI strategy engagement take?
It depends on scope: reviewing one idea takes less time than assessing several departments. We agree the scope, duration and price before starting, and each stage ends with a decision point, so you can stop once you have the answer you need. Proof-of-concept work is scoped separately, because its length depends on data access and the questions being tested.
Can you help us write an AI policy for staff?
Yes. We draft practical guidance on which AI tools people may use, what information must never be entered into them, how outputs are verified before use and who approves new AI features. The policy is shaped around your size and sector and reviewed with your legal and security advisors, since we frame the risks rather than provide legal advice.
Ready to build AI that actually works in production?
Tell us about your AI project and get a free technical consultation within 24 hours. We'll map your use case, assess your data, and give you an honest feasibility assessment — no sales pitch.