Generative AI Solutions

Grounding comes first: responses are retrieved from your own documents through a RAG pipeline with citations back to source passages, so any answer can be checked. On top of that we add output validation against schemas, confidence thresholds, refusal behaviour for out-of-scope questions, and human review on high-risk actions. Every generation is logged, so a failure can be traced and the retrieval or prompt corrected.

RAG is usually the right starting point when the problem is access to knowledge that changes — policies, contracts, product documentation — because you update the index rather than retrain. Fine-tuning with LoRA or QLoRA suits fixed tasks where tone, output format or a specialised vocabulary matter more than freshness. Many production systems use both, and we test the options against a scored evaluation set before committing.

Pricing is structured in stages rather than as a single figure. Discovery and use-case triage is fixed price; the grounded prototype is scoped against a defined dataset and evaluation set; production hardening is quoted once the architecture is settled. Running costs — inference tokens, vector storage and embedding refreshes — are modelled separately, so you can see the ongoing bill before committing to a build.

Yes. We deploy open-weight models into your own cloud tenancy or on-premise hardware where data residency, confidentiality or regulator expectations rule out third-party inference endpoints. The application layer is written against an abstraction, so the model provider can be changed later without rewriting the product. Deployment scripts, the evaluation harness and the prompt assets are handed over with the codebase.