AI Growth & Strategy
AI Consulting
An honest technical read on feasibility, scope, and build-vs-buy decisions before a line of code is written.
Talk to us about AI ConsultingAI consulting, sometimes called AI strategy work, is a short, focused piece of work that answers three questions before you spend on a build: is this feasible, what is the smallest version worth making, and should you build it or buy it.
It is done by the engineers who would build the system, not by a separate advisory team. The result is a written plan you can act on with us, with your own developers, or with another vendor.
Problems this solves
- Leadership wants an AI initiative and nobody is sure where it would pay off.
- There is a list of ideas and no way to rank them.
- A vendor has quoted for a project and you want an independent technical view.
- A pilot worked in a demo and stalled before production.
What AI consulting covers
AI strategy and use case selection
Which of your ideas are worth doing, in what order, and which to drop.
Feasibility review
Whether a specific use case can be built to the quality you need, and what stands in the way.
Build versus buy
Whether an existing product covers the need, and what a custom build would add.
Data readiness
Whether the data the use case depends on exists, can be reached and may be used.
Independent review of a proposal
A technical second opinion on a vendor quote or an internal plan.
Getting a stalled pilot moving
Why a demo that worked never reached production, and what it needs to get there.
Typical projects
Examples of the kind of work this covers, not a list of past clients.
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Before the first AI project
Leadership wants an AI initiative and the team needs a ranked, realistic shortlist.
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Before signing a contract
A vendor has quoted for a build and you want an independent technical read.
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After a pilot
A proof of concept impressed everyone and has not been used since.
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Before adding AI to a product
You want to know what a feature would cost to build and to run.
What an AI consulting engagement delivers
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Use case review
Your candidate ideas assessed for value, feasibility, data readiness and risk, and ranked.
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Build versus buy recommendation
For each use case, whether an existing product covers it, and what a custom build would add.
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Data and systems check
What data exists, where it lives, who may use it, and what has to change before it can feed an AI system.
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Technical approach
The architecture in plain language: models, retrieval, integrations, hosting, and where the real risk is.
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Estimate and first milestone
A realistic scope for a first version and what it would take, written after we understand the problem.
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A plan you own
A document your team can use without us. If something does not need to be built, it says so.
Is AI consulting the right fit?
A good fit when
- You are deciding whether and where to invest in AI.
- You want a technical opinion before signing a larger contract.
- You need a plan your own team can execute.
Probably not when
- The scope is already clear and agreed. Go straight to the build.
- You want a general presentation about AI trends. This is hands-on work.
How an AI consulting engagement runs
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01
Understand
A short call to understand the actual problem, not just the requested feature. We push back if the scope doesn't match the goal.
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02
Architect
A concrete technical plan and estimate before any code is written, covering stack, integrations, and where the real risk is.
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03
Build
Iterative delivery with visibility into progress, not a black box until launch day.
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04
Validate
Tested against real-world requirements and failure modes, not just the happy path, before anything reaches production.
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05
Operate
Deployed, monitored, and supported after launch, with infrastructure and observability included from day one. Never a forced retainer.
Questions about AI Consulting
What do we get at the end?
A written report with ranked use cases, a build versus buy recommendation, a technical approach and an estimate for a first version. It is yours to use with any team.
Do we have to build with Kiaanlab afterwards?
No. The plan is written so that your team or another vendor can follow it. There is no obligation and no forced retainer.
What if the answer is that AI is the wrong tool?
Then that is what the report says. A plain rule, a database query or an existing product is often cheaper and more reliable, and telling you that early is part of the job.
What do you need from us?
Time with the people who know the process, access to example data, and a clear picture of the business goal behind the idea.
How much does AI consulting cost?
It depends on scope, so we do not publish a price list. After a short call to understand the problem you get a written estimate with its assumptions, before any commitment. If the work is not worth doing, we say so.
How long does an AI consulting engagement take?
It is a short, bounded piece of work, not an open-ended retainer. The exact length depends on how many use cases are reviewed and how quickly we can speak to your people and see example data. You get the duration in writing before we start.
From the blog
Is Your Data Ready for AI? A Practical Readiness Check
Data readiness is not about volume. Six checks show whether your information can support an AI system, and what to fix if it cannot.
Why AI Pilots Stall Before Production
A working demo is the easy part. Pilots stall on integration, ownership, evaluation and trust, all of which can be planned for.
How to Evaluate an AI Use Case Before Spending on It
Five questions that separate AI ideas worth funding from ones that will stall, asked before any budget is committed.