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AI integration & LLM development

A demo takes an afternoon. Making AI dependable inside a business process takes engineering: the right data, guardrails, evaluation, cost control and a fallback for when the model is wrong. We add AI where it removes real work.

Discuss your ai in production project

You might be here because

Staff spend hours reading PDFs, invoices or reports and typing the data into another system.
You built an AI prototype and are not sure it is safe to put in front of customers.
You want AI features in your product without sending sensitive data where it should not go.

What we build

Document extraction: invoices, prescriptions, lab reports, statements
Assistants and agents connected to your own data and tools
Evaluation sets, guardrails and human review steps
Cost and latency tuning, model selection

Tools

Claude
OpenAI
Python
Node.js
pgvector
AWS Bedrock

Other services