Useful AI, not trendy AI.
A good AI feature is data quality, guardrails, cost per request, latency — and knowing when not to trust the model.
01
What we develop
Adding "AI" to a product is not just plugging a chatbot onto a page. Done right, an AI feature is software engineering in its own right — data, context, guardrails, costs, latency, user experience. That is our job.
Business copilots
Assistants embedded in your application that understand your context and data.
RAG over your documents
Query your contracts, documentation and knowledge bases in natural language.
Data extraction
Turning heterogeneous documents, PDFs and emails into clean, structured data.
Assisted generation
Product descriptions, template answers, reports — always with human review.
Classification & routing
Sorting, prioritizing and escalating automatically according to your business rules.
LLM integration
OpenAI, Anthropic, Mistral APIs — or self-hosted open models for confidentiality.
02
A concrete example
A software vendor wanted its customers to find a specific clause across contracts of several hundred pages. We built a retrieval-augmented search engine: natural-language questions return the exact clause, its context and its reference.
- accuracy on their test sets
- 95%
- accuracy on their test sets
- response time per query
- < 2 s
- response time per query
03
Our AI engineering
Five rules we apply to every project — they make the difference between a useful AI feature and a demo that ships to production.
Use case first
Which task, what volume, what expected quality, what budget per operation. The tech comes after — never before.
Systematic evaluations
Every AI feature is measured on a representative test set, before and after each change. No numbers, no trust.
Guardrails by design
Human validation on sensitive outputs, deterministic fallbacks when the model is unsure. Errors are planned for, not suffered.
Controlled costs
Model choice per task, caching, context limiting: the API bill is a metric we track like any other.
Sovereignty available
Self-hosted open models for the most sensitive data: nothing ever leaves your perimeter.
04
When AI makes sense — and when it doesn't
We are honest about this: not every feature needs AI. A well-indexed classic search beats an LLM in some cases; an explicit business rule beats a probabilistic model when the logic is deterministic. Part of our job is telling you when AI is not the right answer — that is what separates a useful AI project from a trendy one.
05
Technologies
LLM APIs
OpenAI, Anthropic, Mistral, Google — and self-hosted open models (Llama, open Mistral…).
RAG & vector databases
pgvector, embeddings, chunking and reranging tailored to your documents.
Python & TypeScript
AI inside your stack, not next to it.
Observability
Cost, latency and error-rate tracking for every AI operation.
Frequently Asked Questions
Get In Touch
Got a project in mind? We'd love to hear about it.