Example use cases

Get inspired with examples of Rainbird-powered solutions

Every example below shows a different way Rainbird gets integrated.

Look for whichever you recognise first — your decision ("I need something like our claims process") or your integration pattern ("I need something that works like an agent tool call"). Every example is tagged with both.

Use case	Decision domain	Integration pattern
Digital tax consultant	Tax & Audit	Guided conversation
Real-time fraud claim decisioning	Insurance — Claims fraud	Automated pipeline
Trade finance document compliance check	Banking — Trade finance	Document check
Mortgage decisioning for an AI lending agent	Banking — Mortgage underwriting	AI agent decision layer
Customer risk rating	KYC & Onboarding	Combined approach

Integration patterns

• Guided conversation — Rainbird asks an end-user or advisor clarifying questions, one at a time, adapting each question to what's already been answered, and delivers a reasoned outcome at the end. Whether that end-user is an advisor talking to a customer, or the customer themselves, the pattern is the same.

• Automated pipeline (also called straight-through processing) — Rainbird is called system-to-system, with no one seeing a UI. Data goes in, a decision comes back, and it flows straight into the next system.

• Document check — Data already extracted from a document (by your own OCR/IDP pipeline) is checked against rules held in a knowledge graph, returning a pass/fail with an explanation for each check. Rainbird checks structured data, not raw documents.

• AI agent decision layer — An AI agent calls Rainbird mid-workflow, over MCP or API, whenever it hits a decision that needs to be consistent and defensible. The agent handles the conversation and context; Rainbird handles the judgement call.

• Combined approach — Most real implementations blend two or more of the above. The most common blend: an automated pipeline handles the clear-cut cases straight through, and asks for additional information where required to reach a conclusion, escalating to a AI agent or guided conversation with a human reviewer to capture this data.

Don't see your decision here? The accelerator library has 23+ working knowledge graphs across banking, insurance, tax and healthcare — a closer match may already exist as a starting point.