What We Build

AI that ships: agents that act inside your product, retrieval over your data, models that run where your data is allowed to live. Built for production, not for the launch announcement.

The assessment wizard on this site is one of ours — it interviews you, scopes the project, and prices it. Try it before you write us.

• AI agents built into your product and workflows
• RAG systems over your documents, tickets, codebases, and wikis
• Company assistants: internal chat over your own knowledge, with access control
• LLM features in existing apps: drafting, summarization, classification, extraction
• Automation where a model earns its keep — and plain software where it doesn't

Agentic Workflows

A single prompt is a demo. Real automation is a workflow: an agent that reads the inbox, checks the calendar, drafts the offer — and knows when to stop and ask a human.

• Multi-step agents with tool access: email, calendars, CRMs, internal APIs
• Human-in-the-loop where mistakes are expensive — approvals built in, not bolted on
• Structured outputs your systems can rely on, not free-form text
• Orchestration that survives bad days: retries, fallbacks, audit trails

Private Models & Fine-Tuning

Some data can't leave the building. We deploy open-source models (Llama, Mistral, Qwen) on your infrastructure — AWS, Hetzner, or on-prem — so prompts and embeddings never cross your perimeter.

Fine-tuning pays off when prompting stops improving — tone, format discipline, domain shorthand, latency. We'll tell you which case you're in before we start, and prove the difference with evaluations.

• Private LLM deployment in your VPC or on-prem
• Fine-tuning and evaluation on your own data
• Small task-specific models when an API call is too slow, too expensive, or too public
• Zero-retention API configurations when private hosting is more than you need
• DPAs, EU data residency, no training on your data

The Unglamorous Parts

Anyone can wire up a chat window. The work is in retrieval quality, evaluation, cost control, and what happens when the model is wrong — that's where we spend our time, and it's why what you ship holds up.

• Evaluation harnesses, monitoring, retries, graceful degradation
• Model-agnostic: OpenAI, Anthropic Claude, Google Gemini, or open source via vLLM and Ollama
• LangChain, LlamaIndex, pgvector, Qdrant, hybrid search with reranking — and plain Python where frameworks get in the way
• A straight answer when the problem is better solved without AI

Where It Lands

The same building blocks, shaped by each field's constraints:

• Healthcare and legal: document AI and assistants on confidential data, kept inside your perimeter
• Finance and insurance: explainable models for fraud, claims, and underwriting — decisions you can audit
• Manufacturing: predictive maintenance and computer-vision quality control on SCADA, MES, and ERP data
• Retail: recommendations, demand forecasting, and conversational support that scales through peak season

How We Work

Most AI projects start with a two-to-four-week proof of concept on your real data. If the approach doesn't survive contact with your data, better to know in week two — the production build is scoped from what the PoC shows. Senior engineers, direct access, GDPR-compliant by default.

Tell us what you're planning and you'll have a written assessment within 24 hours — approach, model choice, timeline, and the risks.