03 — AI & LLM solutions
We design AI features around a measurable job: answer customer questions from your documentation, read invoices, draft offers, route tickets, operate internal tools. We pick the model per task, ground it in your data and test it like any other code.
01What we build
AI & LLM solutions
- 01AI agents that use your tools via MCP
- 02Assistants grounded in company knowledge (RAG)
- 03Document and email processing
- 04Voice agents and transcription
- 05Recommendation and classification models
- 06AI features inside existing products
02How we do it differently
Senior engineers. Agentic tools. One standard of quality.
- 01
Evaluations, not vibes
Each AI feature ships with a test set and quality metrics that run in CI.
- 02
Your data stays yours
Private deployments, EU regions or self-hosted open models when the data requires it.
- 03
Costs under control
Caching, model routing and budgets per feature, visible on a dashboard.
03Technologies
A stack as wide as your ambition.
- AI & ML Claude (Anthropic) Frontier models for agents, reasoning, coding and long documents.
- AI & ML Agent frameworks Claude Agent SDK, LangGraph and custom tool loops for real workflows.
- AI & ML Model Context Protocol Connects assistants safely to your tools, files and systems.
- AI & ML RAG & vector search Answers grounded in your documents, with pgvector, Qdrant or Pinecone.
- AI & ML OpenAI & Gemini APIs Multi-model setups, chosen per task, cost and data policy.
- AI & ML Open models Llama, Mistral and others, self-hosted when data must stay in-house.
- Backend Python The language of data, automation and machine learning.
- AI & ML PyTorch Training and fine-tuning custom models.
- AI & ML Computer vision Detection, OCR and quality inspection from images and video.
- AI & ML Speech & voice Transcription, voice agents and text-to-speech.
- AI & ML Evals & guardrails Measured quality, safety filters and cost controls for AI features.
04Questions
Questions we hear most.
Which AI model do you use?
It depends on the task. We often use Anthropic’s Claude for agents and long documents, and combine it with other providers or open models where cost, latency or data residency matter.
Is it compliant with the EU AI Act and GDPR?
We classify the use case, document data flows, add human oversight where needed and keep logs. Legal sign-off stays with your counsel, and we prepare everything they need.
Can AI work with our internal systems?
Yes. Through APIs and the Model Context Protocol an assistant can read from and act in your CRM, ERP, ticketing or file storage, with permissions you control.
05Blog
More from the blog
-
AI-native software development: what changes for you as a client
AI coding tools are everywhere. What an AI-native software house does differently, what it changes in cost, speed and quality, and what it does not. -
RAG in production: building an AI assistant that answers from your documents
What it really takes to ship retrieval-augmented generation: document preparation, retrieval quality, permissions, evaluation and cost.
06Related services
Everything software, under one roof.
- 04 SaaS & product development From idea and MVP to a subscription product with billing, analytics and scale. Learn more
- 05 E-commerce Shopify, WooCommerce and headless commerce built for conversion and growth. Learn more
- 06 Cloud & DevOps AWS, Google Cloud and Azure infrastructure as code, CI/CD and 24/7-ready operations. Learn more
Next step
Let’s build what’s next.
Tell us about the idea, the problem or the system that needs help. A senior engineer replies within one business day.
Start a project