Find out where AI actually belongs in your business – and where it doesn’t.
Everyone is telling you to “use AI”. Three vendors gave you three different answers. What you need is not another opinion. It is a decision you can trust.
In 2-3 weeks we look at your processes and your data. We tell you what needs machine learning, what needs good software, and what should be left alone. You leave with a plan you own, whether you build it with us or not.
A practical decision in weeks, not months.
Clear signals that an assessment is the right first step.
The assessment is for teams that need a reliable decision before they commit to an AI build.
A decision package your team can actually use.
Each output reduces uncertainty before development starts, from the role of AI to data readiness, operating cost and the smallest useful pilot.
A map of your ideas
Each idea is marked deterministic or probabilistic. Deterministic means rules, databases and search: exact, predictable and cheaper. Probabilistic means machine learning or a language model: powerful, but only with the right data.
Data readiness report
What you have, what shape it is in, and what needs cleaning before anyone writes code.
Running costs and privacy
What each option costs per month once it is live, and where your data goes.
Pilot plan
The smallest thing that proves value, with a timeline and an estimate. Sometimes the answer is “no AI needed”. We will say so.
One-hour walkthrough with our architect and your team
We review the recommendation together so your team understands the reasoning, trade-offs and next step.
Use the simplest technology that reliably solves the problem.
AI earns its place when language, uncertainty or fuzzy patterns are part of the problem. Exact business logic should stay exact.
Where AI has no place
Where AI earns its place
We still use it to build your product faster.
We use AI in analysis, coding, testing and documentation. You get the speed either way, with a software architect at the steering wheel.
From competing opinions to a decision you can act on.
The point of the assessment is not another AI proposal. It is clarity about what to build, what not to build and where to start.
A smaller solution can be the better decision.
A feasibility assessment should reduce uncertainty before development, not simply confirm the most expensive idea on the table.
From a broad AI automation idea to a focused document-processing pilot.
01 / ChallengeThe client wanted to automate several internal workflows with AI.Their initial concept combined document intake, internal search, request routing and workflow automation into one large implementation.
02 / FindingMost of the workflow did not need AI.Rules, approvals and system-to-system actions could be handled with deterministic software. AI was most useful for extracting information from unstructured documents and classifying incoming requests.
03 / RecommendationStart with one measurable document-processing use case.We recommended separating the standard workflow from the AI layer and validating document extraction first, with clear data requirements, operating costs and success criteria.
04 / OutcomeA smaller first scope with a clearer path to value.The client left with a defined pilot, a lower-risk implementation path and a roadmap for adding AI only where it could deliver measurable value.
The essentials, upfront.
The service is deliberately small and fixed enough to reach a decision before a larger implementation commitment.
Experienced engineers. AI as an assistant.
Every project has a software architect at the steering wheel, and a person is responsible for everything we ship.
Schedule your first conversation with our expert team.
30 minutes, no preparation needed. We will tell you on the call whether an assessment makes sense for you.