Practical AI that ships, and keeps running.

What we build

Agents that do the work. Not chatbots.

Every system we build has a job, a measure of success, and a cost you can plan for.

• 01

  Custom LLMs

  Models shaped to your domain, your data, and your budget.

  • Model selection and fine-tuning
  • Training data preparation
  • Structured output for real systems
• 02

  Retrieval and RAG

  Answers grounded in your documents and records, not the open internet.

  • Embeddings and vector search
  • Ingestion pipelines for your sources
  • Citations and access control
• 03

  Agents and workflow automation

  Systems that do the work inside the tools your team already uses.

  • Tool-using agents with clear limits
  • Integration with your APIs and data
  • Human review where it matters
• 04

  Evals, safety, and cost control

  The work that keeps AI in production after the demo.

  • Evaluation suites and regression tests
  • Guardrails, moderation, and fallbacks
  • Usage tracking and cost per request

Where it runs

Your cloud, or your own hardware.

We pick the deployment that fits your data, your compliance needs, and your budget, and we can move between them as you grow.

• AWS

  Amazon Bedrock

  Managed foundation models inside your AWS account, wired to your data and your IAM. The fastest path if you already run on AWS.

• Google Cloud

  Vertex AI

  Gemini and open models on Google Cloud, next to the data you already keep there.

• Your hardware · Our specialty

  Self-hosted open-weight models

  Open-weight LLMs served on your own GPUs with vLLM-class inference. Predictable cost, no per-token bill, and your data never leaves the building. We run this in production today.

Proof

We run production AI for a global audience.

inVoak, our own product, puts LLMs to work for real users every day: reading wine labels and menus, answering questions in a conversational sommelier, and ranking recommendations against each user’s palate. It runs self-hosted LLM inference in production, in 20 languages across 170+ countries, with GDPR, CCPA, and DSA coverage.

How we work

Start small. Scale what works.

• Fixed-scope pilot

  One use case, real data, and a working system in weeks. A clear deliverable, a clear price, and an honest read on whether it is worth scaling.

• Embedded AI engineer

  A senior engineer joins your team for weeks or months, working in your repos and your rituals, to take AI features from prototype to production.

1. 01

   Scope

   A short call, then a written plan with milestones, risks, and a price.

2. 02

   Prove

   An evaluation set built from your data, so progress is measured, not guessed.

3. 03

   Ship and run

   Production deployment, monitoring, and cost tracking. We stay through launch.