Resources

The Decision Layer: Why Rainbird Matters Now

Enterprises have discovered that generative AI can do almost everything except the one thing that matters most in regulated industries: make a decision they can defend.

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“Between the plan and the act” — the layer that decides, records why, and can be audited afterwards

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Our other resources

This resources hub contains essays, and other assets to support those who care about trust in the agentic stack.

5 resources

“Wide, or right” — general models spread across every task and land squarely on none of them

Generality Is The Enemy Of Precision: Why Enterprise AI Is Stuck In Pilot Purgatory

Walk into any large financial institution today and you’ll find the same scene: dozens, sometimes hundreds, of AI pilots and almost nothing in production.

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“A plan is not a decision” — only a decision layer can be held to account for the call it makes

Agentic AI Needs Judgement, Not Just Autonomy

Agentic AI has become the dominant architecture for organisations trying to get value for their AI investment.

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“Can you show your working?” — four signs your decision automation would not survive the question

Four Signs Your Decision Automation is Putting You at Regulatory Risk

Financial institutions have spent years and poured millions into automation, but with mixed results.

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“One poisoned fact” — a deterministic graph will not accept what it cannot trace to a source it was given

Securing the World Model: How Determinism Protects Enterprises from LLM Poisoning Attacks

In October 2025, a research collaboration between Anthropic and the Alan Turing Institute exposed one of the most uncomfortable truths in modern AI.

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“Same question, same answer” — in financial services the regulator asks what the system did, and why

Deterministic Graph-Based Inference: The Key to Safe AI in Financial Services

As financial institutions increasingly adopt Large Language Models (LLMs) to enhance customer experiences and streamline operations, a critical challenge has emerged: how can these powerful but inherently probabilistic systems be deployed safely in a highly regulated environment?

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Decision automation for regulated work

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