A5 Tech OÜ · Estonia · Applied AI studio
Messy AI problems. Made measurable.
Documents nobody has time to read. Images nobody can measure. Audio nobody can label. Behavior nobody can quantify. AI prototypes that work in the demo and break in real use. We turn that mess into running software with evals, handover and a clear go/no-go.
01 — Choose your problem
Start with the pain
You should not have to know whether your problem is “computer vision”, “retrieval” or “signal ML” before asking for help. Start from the thing that hurts. The technical path comes after the problem is clear.
Pick the closest door. Each path leads to working examples, the kind of engagement it becomes, and the safest next step.
Documents & language
When the work is buried in text.
Use this path when the source material is too long, inconsistent or risky to review by hand every time.
Images & vision
When photos need to become decisions.
Use this path for detection, counting, measurement and image classification where the inputs are not clean lab examples.
Signals & research
When the signal is subtle.
Use this path for audio, behavior, facial-expression research, small datasets and evidence that must survive scrutiny.
AI product UX
When users need to trust the system.
Use this path for AI features, bots and interfaces where confidence, recovery and cognitive load decide whether people keep using it.
Prototype rescue
When the demo works, but the product does not.
Use this path for AI-built or vibe-coded apps that need a senior pass: architecture, UX, security, data flow, deployment, error handling and a roadmap from prototype to something people can safely use.
02 — Method
How an engagement runs
Most AI projects die in month three, when it turns out the data does not support the promise. We front-load that risk deliberately: week one produces running code against your real data, and if it does not work you have spent a week, not a quarter.
The panel is live — type help into it.
03 — Try it
Open a live tool
The best sales deck is a working system. Start with one of these, then bring the harder version from your own data.
04 — Scope
The safest next step
The quote depends on the real constraint: data, deployment, risk, users, timeline and what “done” means. Start with proof, a scoped build, or a rescue pass.
05 — Portfolio
Selected work
Live from github.com/ahsham
Tell us the problem.
Three sentences is enough to know whether this is a job for us. If it is not, we will say so and point you somewhere better.