The Imandra Automated Reasoning Framework for Capital Markets

The offer: The Imandra Automated Reasoning Framework for Capital Markets lets you pair any large language model with automated reasoning - formal logic and machine-checked proof - so the AI you use is provable, not just plausible. Can be delivered as a virtual machine you run inside your own Azure environment (your source and data never leave your tenant), it is built on ImandraX, an automated reasoning engine and theorem prover. On top of it, CodeLogician augments any LLM - Anthropic, OpenAI, Google and more, directly or via OpenRouter - to formalize decision logic, map every case, and prove the properties that must hold or return the exact input that breaks them. The same engine reaches across the stack: system design (formalizing and checking specifications before build, and proving a migration behaves identically to the system it replaces), system integration (modeling message protocols such as FIX and ATDL into provably correct conformance suites that speed client onboarding), financial contracts in FpML, and smart contracts in DAML. Ponens, the open-source auditable system of record, captures and governs every step with computable, policy-based checks.

Who benefits: Capital-markets firms and their technology partners - the engineering, quantitative, risk, and compliance teams that build and operate trading, pricing, risk, settlement, venue-integration, and contract systems - who want to adopt AI and LLMs at scale while still being able to trust, prove, and evidence what that AI produces.

The need it addresses: LLMs write code and encode logic quickly but reason statistically, so they are least reliable at the edge cases and rare states where risk concentrates, and their output shifts across models, versions, and runs; contracts, protocols, and settlement logic carry the same exposure, and testing only ever samples behavior. That leaves regulated firms unable to prove, govern, or audit the AI-generated logic they now produce at scale. By grounding every LLM in formal logic and automated reasoning, the framework makes that logic provable, consistent across any model, and ready for the scrutiny of auditors and regulators - so you can align AI with your regulatory requirements.