Artificial Intelligence

Most AI projects die at the prototype. They work in the demo, impress in the meeting, and then fall over on real users, dirty data, or a usage bill nobody forecast.

We build the boring part that makes the difference: systematic evaluation of outputs, cost ceilings, handling for the cases where the model gets it wrong, and an architecture that lets you swap models when a better one appears — and one will.

We always start with the same question: does this actually need AI? Plenty of problems are better solved by a database query or a simple rule — cheaper and more predictable. When the answer is yes, we pick the smallest model that does the job.

We work with commercial models (OpenAI, Anthropic, Google) and with open models hosted on your own infrastructure, when the data cannot leave the building. It is a technical and cost decision, not a fashionable one.

What we do

• Assistants and search over your internal documents (RAG)
• Agents that take actions in the systems you already run
• Fine-tuning and custom models for your domain
• Computer vision and image processing
• Classification, extraction and structuring of unstructured data
• Quality evaluation, testing and hallucination monitoring

Key benefits

• Repetitive work automated without adding headcount
• Answers grounded in your data rather than invented
• Per-request costs measured and capped before they scale
• Vendor independence: change model without rewriting everything
• Sensitive data processed inside the EU when that is a requirement

When it makes sense

• Customer support that answers from your own documentation
• Automatic triage and routing of incoming requests
• Data extraction from invoices, contracts or forms
• Internal search across years of scattered documents
• Content generation in your brand's voice and rules

Technologies we use

OpenAI · Anthropic · Python · PyTorch · LangChain

Why devcave

We have trained and shipped proprietary models for clients, including video generation from still images. We know where these projects usually fail — and we tell you before we start.