In this session Ben Taylor, Rainbird CTO, and Mike Price, Head of Product, show how Rainbird turns policies, procedures, and training manuals into inspectable knowledge graphs you can refine, test, and deploy for precise, deterministic, auditable decisioning.
Using a fictitious bank KYC workflow (Secure Bank International), they walk through how to generate a first-pass model from documentation, refine it safely with Co-Author, and then test it at runtime to produce decisions backed by a full evidence tree.
What you’ll learn
- How “Generate from Docs” converts clear, decision-focused documentation into a first-draft knowledge graph.
- How to inspect the ontology, rules, and scoring bands, then correct or update logic when policy changes.
- Where LLMs help (extraction and editing) and where they don’t (reasoning), so outcomes stay consistent and explainable.
- How Rainbird handles missing data without guessing, by asking clarifying questions or consuming your existing data sources.
- How to validate outcomes with evidence trees and build confidence before production.
Resources shared in the webinar
- Rainbird Studio Community Edition: Experiment, model, and bring decisions to life, visit app.rainbird.ai
- Rainbird Academy: Learn the foundations of explainable decision intelligence, visit academy.rainbird.ai
- Rainbird Forum: Ask, discuss, and shape the conversation, visit forum.rainbird.ai