What we build
- Document and evidence reading: names, amounts, dates and identifiers pulled out of uploads, screenshots and PDFs
- Summaries and drafts that a person checks before they go anywhere
- Translation inside your product, so content reaches users in their own language
- Classification and routing: incoming requests, tickets or leads sorted so people handle the right ones first
- Agents that run multi-step jobs on their own, with clear limits on what they may do
How we make it reliable
AI output is a guess, so we design for the cases where it's wrong. Every feature starts with a test set built from your real data, and we measure quality before launch, not after the complaints. Where mistakes are costly, a person reviews the output before it's used. Users are told where AI is involved.
We build in the stack you already have. We work across Go, NestJS, Java and Spring, Flutter, Angular and React, with PostgreSQL behind most of it, so the feature becomes part of your codebase instead of a separate island.
Shipped examples
- Apgavo: AI reads evidence uploaded by scam victims, extracts scammer identifiers and drafts summaries in two languages. Every output is checked by a person, and the AI use is disclosed in the terms.
- Car-wash booking platform: AI-powered content translation across the platform's three languages.
- A code-review bot that flags breaking API changes before they merge.
- An autonomous lead-generation agent for a manufacturer.
Questions
Which AI models do you use?
Whatever fits the job. Hosted models from the large providers work well for many tasks. When data can't leave your systems, we run open models on your own hardware: see private, self-hosted AI.
Can you add AI to software someone else built?
Yes, as long as we can work with the code or an API. Most of our long-running projects are systems we maintain and extend over years.
What will it cost to run?
It depends on volume and the model. We estimate running costs before we build and design to keep them predictable, for example by caching results or using smaller models where they're good enough.
What happens after launch?
We keep it running: we watch quality, move to better models as they come out, and fix what breaks. Building software and keeping it running is the whole point of the studio.