Do you use open-source models?

Yes, where it makes sense. For sensitive data we recommend Llama/Mistral on EU-hosted infrastructure. For conversational use we usually mix commercial and open-source models.

You do. All code and models we build belong to the client. Repositories live in your GitHub/GitLab organization.

Do you do RAG (Retrieval Augmented Generation)?

Yes. We use Qdrant or pgvector for vector search with OpenAI or Voyage embeddings. Architecture is chosen based on data volume and sensitivity.

Fixed price or time & materials?

Both. Discovery sprint is always fixed-price. Pilot and production can be fixed-price when scope is well-defined after discovery.

AI implementation

From idea to production in weeks, not quarters

We run the project from discovery sprint through pilot to full integration with your systems. European stack, fixed scope, predictable outcome.

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What we do differently

Fixed-scope discovery sprint

One week, clear deliverables: technical plan, architecture, cost estimate, risk register. If we can't deliver - it's free.

EU-friendly stack

Azure OpenAI EU, Anthropic EU, Vertex AI EU. Data stays within the EU. Infrastructure set up to meet GDPR and the EU AI Act.

Production-ready from day one

We build on NestJS + PostgreSQL + Qdrant - battle-tested architecture from our internal products. No throwaway pilot code.

Integration with your stack

We wire AI into your CRM, ERP, ticketing, and existing auth. Nothing lives in its own silo.

Knowledge transfer

Every project ships with documentation, an internal workshop for your team, and operational hand-off recommendations.

Measurable ROI

We define success metrics upfront - from time saved per task to contact-center cost reduction. The final report shows real numbers.

A typical project

Discovery sprint (1 week)

Stakeholder interviews, data review, architecture selection, risk assessment. Output: a concrete plan with duration and price.

Pilot (4–6 weeks)

We build a working pilot on real data. We demo it weekly, collect feedback, and iterate.

Production (4–8 weeks)

Integration, authentication, logging, monitoring, rollout strategy. The pilot becomes a production system.

Hand-off and support

Documentation, team training, monitoring runbook. We stay available during the first weeks of production.

Frequently asked

: Yes, where it makes sense. For sensitive data we recommend Llama/Mistral on EU-hosted infrastructure. For conversational use we usually mix commercial and open-source models.
: You do. All code and models we build belong to the client. Repositories live in your GitHub/GitLab organization.
: Yes. We use Qdrant or pgvector for vector search with OpenAI or Voyage embeddings. Architecture is chosen based on data volume and sensitivity.
: Both. Discovery sprint is always fixed-price. Pilot and production can be fixed-price when scope is well-defined after discovery.