Deploy stage
Best for
Knowledge-heavy or action-taking work such as support, research, and operations.
How it runs
List the knowledge the model must read and the actions it may take.
Access mapEvery data source and tool, and who is allowed to use it.
Index your content and tune search until the right passage comes back for most questions.
Retrieval pipelineChunking, embeddings, hybrid search, and re-ranking tuned for your document types.
Wire up tools and agent loops, with a human approval step wherever the stakes are high.
Tool connectorsFunction-calling schemas and MCP servers for your CRM, ticketing, ERP, and internal APIs.
Measure faithfulness and citation accuracy on real questions before launch.
Grounding reportScores, plus the failing cases to fix next.
In practice
Numbers from the base model versus the model we shipped.
Associates spent hours checking standard agreements against a 200-page negotiation playbook.
Clause extraction F1
Before: 0.74After: 0.91
Review time per contract
Before: 45 minAfter: 12 min
FAQ
Yes, and it's often the strongest setup. Fine-tuning teaches the model how to use retrieved context and your formats, and retrieval keeps the facts current.
Usually. Elasticsearch, OpenSearch, and most vector databases can serve as the retrieval layer.
Often paired with
Share a sample of your data and a target metric. We'll come back with a plan and a timeline.
We reply within one business day (IST).
