I've been working on a deterministic verification engine for AI-generated financial claims.

The original idea was fairly simple:

An LLM should generate claims. It shouldn't be the authority that verifies them.

But after building and testing the system, I realized the problem is much bigger than hallucination detection.

The question I'm now working on is:

Our architecture looks roughly like this:

LLM ↓ Candidate claim ↓ Claim normalization ↓ Evidence ↓ Assumptions + Constraints ↓ Proof / Derivation ↓ Contradiction analysis ↓ Deterministic verification ↓ Auditable outcome ↓ Trust 

The important part is that the verification layer is independent of the model.

For example, if an LLM says:

we don't want the LLM's confidence score to determine whether that statement is trustworthy.

Instead, the system should be able to determine:

• What exactly was claimed?
• What evidence is being used?
• Can the claim actually be derived?
• Which assumptions are involved?
• Are relevant constraints satisfied?
• Is there contradictory evidence?
• Can the result be reproduced?
• Can we explain the verification outcome?

I recently ran a 66-case benchmark.

Structured fixture claims: 66/66 passed.

Then I ran the same pipeline with live GPT-5.1-generated claims:

19/66 passed end-to-end.

The failures were:

• 31 pipeline execution failures
• 18 claim binding failures
• 2 contradiction detection failures

Meanwhile, several deterministic verification components were still passing their tests, including evidence graph integrity, deterministic calculation, rule application, missing evidence detection, reproducibility, and auditability.

The result changed how I'm thinking about the problem.

The bottleneck isn't necessarily the deterministic verifier.

There is a difficult translation layer between:

Probabilistic language ↓ Formal representation ↓ Deterministic reasoning 

We're now rebuilding the benchmark so that instead of simply saying "this case failed," we can identify the first invalid state:

Transport → Parsing → Schema validation → Normalization → Claim binding → Evidence graph → Verification → Outcome mapping 

That's where I think the interesting engineering/research problem is.

We're also exploring a broader framework around claims, evidence, assumptions, constraints, proofs, contradictions, and trust.

One idea we're particularly interested in is treating trust as an emergent output of the verification process, rather than simply using an LLM confidence score.

This is still early research/product development. The benchmark is internal and isn't third-party validation, and the mathematical Trust model still needs empirical validation.

I'm also actively looking for people to work with.

We're looking for:

Researchers interested in:

• formal verification
• trustworthy AI
• AI evaluation
• formal methods
• argumentation systems
• knowledge representation
• mathematical modeling

Marketers / growth partners who can help us:

• communicate the problem clearly
• reach technical and business audiences
• find early adopters
• build a community
• develop the startup's go-to-market strategy

Engineers and technical collaborators interested in building reliable AI systems.

And particularly industry partners in finance, risk, audit, compliance, or other areas where incorrect AI claims have serious consequences.

I'm interested in finding people who want to build with us, not just give feedback from the sidelines.

If this problem interests you, DM me or comment below.

I'd especially love to hear from researchers and marketers who think this is a problem worth tackling.

We're still early — which is exactly why now is a good time to get involved.