Generative AI & RAG Development Services
Applications for LLMs based on your own data, including enterprise knowledge systems, document intelligence, and fine-tuned models—complete with evaluation frameworks which prove the answers are correct—and these are deployed within your own cloud tenant.
When GenAI is the right answer
Years of documents, wikis, tickets, and contracts — yet finding the answer to any question still takes a twenty-minute search.
It's a promising demonstration of a large language model but couldn't be relied upon when faced with real questions; without grounding and evaluations, generative AI remains just a toy.
The legal and security teams are blocking the use of generative AI since the data would leave your perimeter; that doesn't have to happen.
What we build
Enterprise RAG systems
Knowledge assistants based on your documents, wikis, and databases – they provide answers along with the sources and say 'I don't know' when it's appropriate.
Document intelligence
Pipelines for the extraction, classification, and summarization of contracts, reports, invoices, and forms by combining optical character recognition, vision models, and large language models.
LLM fine-tuning & prompt engineering
In cases where RAG is not sufficient, models can be adapted to your domain language, output formats, and quality standards, together with rigorous evaluation carried out before and after.
GenAI audits
A review by engineers of your current GenAI systems regarding their accuracy, cost, and latency as well as security, including a list of fixes that are prioritized.
Custom LLM applications
Your products and the internal tools they include have generation, summarization, and analysis features that are tailored to your actual workflows.
Private, secure deployments
Everything runs within your own Google Cloud or AWS tenant, and your data never leaves your perimeter to train anyone else's model.
Where this earns its keep.
Every engagement runs spec-first, agent-accelerated, human-owned.
A written specification that has been approved by the client prior to any coding taking place — covering the architecture, the edge cases, and the acceptance criteria.
The engineers arrange for the AI agents to carry out the specification in parallel, taking weeks rather than quarters.
The evaluations, tests, and security checks are carried out together with the build, and humans examine every decision that is important.
The system has been deployed to your GCP or AWS, complete with monitoring, production evaluations, and retraining loops.
From the portfolio.
Automatic video metadata that creators can rely on
Production-quality AI incorporated into a media platform's product to automatically convert video content into summaries, titles, and timestamped chapters, thereby enabling creators to spend less time on manual cataloguing.
Tools we reach for.
Chosen for each project based on evaluations and fit — never out of habit.
Questions, answered.
How do you prevent hallucinations?
Is our data safe? Where does it live?
Should we fine-tune a model or use RAG?
How do you measure answer quality?
Which LLM will you use?
Have a use case in mind? Let's pressure-test it — free.
A 30-minute call with our engineers, not a salesperson. You'll receive an honest assessment of feasibility, a preliminary architecture, and a clear next step.
Book a free AI consultation