RAG, agents, copilots and fine-tuning — chosen on evidence.
Generative AI / LLM
The full generative stack: retrieval over your own documents, tool-using agents, copilots embedded in existing workflows, prompt systems and fine-tuning where it genuinely beats retrieval.
- Typical timeline
- 6–16 weeks
- Engagement model
- Fixed scope or squad
What you get
- Retrieval-augmented generation with citation and grounding checks
- Tool-using agents with server-side permission enforcement
- Prompt systems versioned like code
- Fine-tuning and adapter training where the data supports it
- Groundedness, hallucination-rate and refusal-correctness evaluation
What changes
- Answers traceable to a source document
- A measured hallucination rate rather than an assumed one
- Model and prompt changes compared before promotion
Architectures this usually produces
The assessment decides from your answers — these are the patterns it most often lands on for this service.
Retrieval-augmented generationTool-using agentFine-tuned LLM
Questions people ask
- Should we fine-tune or use RAG?
- Usually RAG first: it is cheaper, updates instantly and cites sources. Fine-tuning earns its cost when you need output conformance or lower per-request price at high volume. The assessment answers this from your data, not from fashion.
Considering generative ai / llm?
Run the free assessment first. It scores feasibility against your own data and volumes, and tells you what it would cost before anyone quotes you.