Rainbird Technologies
Rainbird provides deterministic AI for automating expert judgment in regulated work, encoding policies and regulations as knowledge graphs that produce consistent, evidence-backed decisions. The platform uses symbolic reasoning with backward chaining—no LLM in the decision loop—so identical inputs always yield identical outcomes at zero token cost. Every decision ships with an evidence tree in natural language and JSON, enabling banks, insurers, and healthcare organizations to scale decisions like KYC risk rating and claims triage while satisfying regulator inspection.
- Regulatory & Compliance Technology
- Artificial Intelligence
- Financial Technology
- Insurance Technology
- Software Only
Funding
Funding not disclosed
Founders
Founder details are not available yet.
Product
Problem
Regulated processes such as KYC, claims triage, and mortgage underwriting ultimately narrow to a single judgment: approve or refer, pay or investigate, compliant or not. This judgment typically sits with a small number of experts who can only reach a limited number of cases, creating a decision bottleneck. Probabilistic and agentic AI systems cannot reliably take on this work because they produce inconsistent answers, cannot guarantee policy adherence, and fail to provide evidence that regulators can inspect.
Solution
Rainbird provides deterministic AI for automating expert judgment in regulated work. The platform captures policies, regulations, and expert knowledge as structured knowledge graphs using a no-code authoring environment, then reasons over those graphs symbolically using backward chaining to reach verifiable conclusions. Every decision is accompanied by an evidence tree that shows the exact rules, facts, and certainty values that produced it, in both natural language and JSON. Because no LLM is in the decision loop, identical inputs always produce identical, policy-faithful outcomes at zero token cost. Rainbird integrates with existing systems and AI agents through REST API and MCP, allowing agents to delegate judgment calls that require determinism and auditability. Knowledge graphs are versioned like code, enabling re-performance of historical decisions against the policy in force on the decision date.
Target Audience
Primary customers are regulated enterprises in banking, insurance, tax, and healthcare, including financial crime compliance teams, underwriting and claims departments, and mortgage lending operations that need consistent, defensible decisions at volume.
Features
- Knowledge graph architecture that encodes regulations, policies, and expert judgment as human-readable, versioned, and portable rules
- Symbolic reasoning engine using backward chaining that asks only for the data it needs and operates without any LLM in the decision loop, ensuring deterministic outcomes at zero token cost
- Evidence tree accompanying every decision, displaying the full chain of rules, facts, and certainty values in natural language and JSON for regulator inspection
- No-code Rainbird Studio that lets policy owners author, test, and approve knowledge graphs without developer involvement
- Rainbird Agentic Knowledge Engineer (RAKE) and Claude Skill that turn written policy into deterministic decision agents with human review
- Federation via REST API and MCP for deployment across applications and AI agent workflows
- Deployment flexibility across SaaS, private cloud, or self-hosted environments with ISO 27001 certification and HIPAA compliance
- Use-case accelerators covering KYC/CDD risk rating, transaction monitoring alert adjudication, mortgage underwriting, APP fraud reimbursement, and targeted support decisioning