AI & LLM integration
Language-model features wired into products that already exist: retrieval over your own documents, structured extraction, and evaluation that shows whether it actually works.
The hard part of an LLM feature is not calling the model. It is deciding what goes into the prompt, what happens when the answer is wrong, and how anyone would know.
Retrieval over your own data
A model that has read your documents is far more useful than a general one. We build the indexing, chunking and retrieval that put the right passage in front of the model, and cite the source in the answer so a human can check it.
Evaluation is the deliverable
Every integration ships with a test set and a measured baseline. Without that, "it seems better" is the only available review, and changes become guesses.
/What this includes
Retrieval over private data
Indexing, chunking and search over your own documents, with citations so an answer can be verified.
Structured extraction
Turning invoices, contracts and free text into typed records that a database will accept and reject predictably.
Tools and agents
Giving a model access to your own APIs, with permissions and audit logging around every action it can take.
Evaluation and guardrails
A test set, a measured baseline and limits on what the model may answer, so a regression is visible before users find it.
/Technologies
- Languages
- TypeScript
- Python
- SQL
- JavaScript
- Dart
- Backend
- NestJS
- FastAPI
- Prisma
- SQLAlchemy
- Alembic
- Celery
- Frontend
- Next.js
- React
- Astro
- Tailwind CSS
- Flutter
- Data and analytics
- Microsoft Fabric
- PySpark
- Power BI
- PostgreSQL
- InfluxDB
- Redis
- Infrastructure
- Docker
- nginx
- Caddy
- Cloudflare
- GitHub Actions
- Linux
/Questions
Questions we get asked
How do we know the feature actually works?
Because it ships with an evaluation set built from your real cases and a measured baseline. Without that, quality is an opinion and every change is a guess.
Does our data leave our systems?
Only if you decide it should. We can build against a hosted API or against a model running on your own infrastructure, and the trade-off in cost and quality is stated before you choose.
Have a project in mind?
Tell us what you are building and we will tell you honestly whether we are the right fit.
Contact us