RayzentLabs
ai solutions

Custom AI solutions, scoped as a pilot before anything is built

When the need does not fit a category — a domain assistant grounded in twenty years of archives, a pricing model for a seasonal business, a defect check on production photos — the honest first step is a pilot that proves the idea on your own data before you fund the build.

The problem it solves

Custom AI work is where budgets disappear: open-ended scope, no agreed measure of success, and a demo that never becomes a system. A pilot with a metric fixes all three.

how it works

From your data to a feature people rely on.

  1. Written scope on day one: the question, the data, the success metric, the cost
  2. Prototype built against real examples in week one
  3. Evaluation, cost model and go / no-go recommendation in week two
  4. Pilot cost credited against the full build if you proceed
domain assistantsvisionmultilingualpilot first
in practice

What it looks like in three businesses.

Domain assistant

A manufacturer's twenty years of specs, tickets and emails made answerable in seconds by the engineering team.

Vision quality check

Packaging photos scored for defects on the line, with borderline cases sent to a supervisor.

Multilingual support

A Gujarati-first support layer that reads, classifies and drafts replies for a regional consumer product.

questions

Custom AI solutions: what people ask.

What if the pilot says no-go?

You keep the evaluation results, the cost model and the reasons, and you have spent two weeks instead of a quarter. Around one pilot in three ends this way, which is the format doing its job.

Do you sign NDAs?

Yes, before any data is shared. For sensitive workloads we also scope region-locked or on-premise model options at the pilot stage.

All questions

See custom ai solutions working on your data.

A 30-minute demo on your own use case: what we would build, how long it would take with AI in the loop, and what it would cost. No deck, no obligation.