Stop paying people to do
what software can do automatically.

RAG (Retrieval-Augmented Generation) grounds LLM responses in your actual documents and data rather than relying on the model's training knowledge alone. This eliminates hallucinations on domain-specific questions, keeps answers current, and makes every response auditable — you can trace exactly what source the AI drew from. For enterprise use cases, RAG is almost always the right architecture over fine-tuning alone.

We use RAG to anchor responses to retrieved source documents, implement confidence thresholds that trigger human review for low-certainty queries, and run structured evaluation suites that test for hallucination rates before launch. We also build citation interfaces so users can verify the source of every AI answer.

Yes. All data stays in your cloud environment. We never send your proprietary documents to third-party APIs without your explicit decision — and for clients with strict data privacy requirements, we run entirely on self-hosted open-source models using Ollama or AWS Bedrock.

A proof of concept is typically 4–6 weeks. A full production system with evaluation, deployment, and monitoring is usually 10–18 weeks depending on data complexity and integration scope.

Proof of concepts start from $6,000. Production systems from $20,000. Pricing depends on use case complexity, number of data sources, and integration requirements. Fixed-scope quote before signing.

For most business use cases, RAG is cheaper, faster to update, and more explainable than fine-tuning. Fine-tuning is the better choice when you need to change the model's style or behaviour rather than its knowledge — for example, training a customer service tone or a domain-specific classification task. We advise on the right approach after understanding your requirements.

You own everything — pipeline code, vector stores, prompt configurations, evaluation suites, and all processed data. We build in your cloud account so you are never dependent on our infrastructure.

Yes. Generative AI systems improve significantly with post-launch iteration — better retrieval, refined prompts, and updated source data. We offer optimisation retainers that run evaluation cycles monthly and ship improvements continuously.