AI consulting at Alpina Tech means helping you decide what to build, what to buy and what to leave alone, before you spend a build budget. It ends with a written assessment that names the use cases worth doing, the data work they need, the risks, and a recommended order. If you have already chosen a use case and want it built, go to AI development services.
Which use cases are worth pursuing?
We start from your processes and cost, not from the technology. For each candidate we ask the same questions:
- What is done by hand today, and how often?
- What does a wrong result cost, and who would catch it?
- Does the data the system needs exist, and can it be used?
- Can we measure success within weeks using real examples?
Candidates that pass go on a short list, ranked by value and by how easily they can be tested. Candidates that fail usually fail on data or on the cost of mistakes, and we write down why.
Should you build or buy?
Often the answer is buy. If a product already does the job inside a tool your team uses, building a copy rarely pays. We lean toward building when the logic depends on your own data or workflow, when the capability is central to your product, or when data cannot leave your environment.
| Situation | Usually |
|---|---|
| Generic task, common tool (meeting notes, email drafting) | Buy |
| Answers must come from your private documents | Build, often retrieval based |
| AI is the product feature customers pay for | Build |
| Strict data residency or audit trail | Build or self-host |
| Unclear value, no baseline | Neither yet, run a test |
Is your data ready?
Data readiness is the most common reason AI plans stall. We check where the data lives, what format it is in, how clean and current it is, who owns access, and whether personal or regulated information is mixed in. For retrieval systems we also look at how documents are structured, because scanned images and inconsistent versions hurt retrieval more than model choice does. The RAG guide shows what the pipeline needs from your content.
What about risk and governance?
We keep this practical and sized to your company. The assessment covers which data may go to a model provider and under what settings, what happens when the system is wrong, who reviews outputs, how decisions are logged, and which regulations apply to your sector. We also flag vendor dependence: if one model provider changed terms or pricing, how hard would it be to switch? We are engineers, not lawyers, so legal questions go to your counsel with our technical findings attached.
What is the short paid assessment?
It is a fixed-scope piece of work that produces decisions you can act on:
- Workshops with the people who run the processes.
- A review of sample data for the leading use cases.
- A ranked list of use cases with reasons.
- A build or buy recommendation for each.
- A risk and governance summary.
- A proposed first project, including how we would test it with an evaluation set.
Scope and fee are set after an intro call, because they depend on how many processes and data sources are involved. The output is yours whether or not you hire us for the build.
What do you get at the end?
A document your leadership can read, and a first project small enough to prove or disprove the idea with real numbers. One example of what that first project can look like is our work for a US-based Series-B legal-research SaaS, where a cited retrieval assistant reached retrieval precision above 85% on the client’s own evaluation set; see the case study. To book the intro call, contact us. If you already know you need engineers, look at hiring AI developers.
