Veridict: Verifiable Human Oversight for AI-Written Code

Vadodara, IND

veridict.zkred.tech

A merge gate that trusts AI-generated code by what's verifiable about it: automated formal checks plus anonymous proof of qualified human review, keeping human oversight viable as AI writes more of our code.

Endorsed by+3

Project Details

Veridict is a merge gate that lets projects trust AI-generated code through automated formal checks + anonymous proof of qualified human review

I had started building this project (solo) as part of The Secure Program Synthesis Hackathon and I really got the in depth and critical feedback of shortcomings and what should / shouldn't be there such as

Instead of Github the project should be integrated with git
The current Z3 check only validates that the specification is internally consistent; it does not prove that the generated Python code actually follows that specification
Strengthening the system would require true code-to-spec verification and moving all ZK proof generation fully to the client side.

So, the idea is already validated and next I wanto focus working on the given feedback, and get it property-based/differential testing against the spec, tighter checks, client-side proving.

A concrete output is

A git native system allowing orgs using git to implement merge gating which would help keep the maintainers/reviewers optionally private with client side proving
the deployed github app ready to be installed and to be used as reference implementation

Theory of Impact

AI is producing code faster than humans can verify it and it makes verifying more expensive than generating.

So veridict is trying to close the gap by addressing the code slice by making verification cheap and property-based , so humans stay in the loop on AI-written code, including AI's own.

Verifiable oversight of AI-generated artifacts is a neglected sub problem of scalable oversight. Veridict is a working prototype of one such gate. I'm not claiming it alone reduces existential risk; I'm claiming this is a concrete, buildable piece of the oversight tooling we'll need as AI writes more of the code that runs critical systems, including AI systems themselves.

People

Sumit Vekariya

Team Member

Grants Received– no grants recorded

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Funding Asks

grantmaking.ai Launch Round

Applied

Minimum

$12,000

Ideal

$30,000

What this ask is

Veridict is a merge gate that lets projects trust AI-generated code through automated formal checks + anonymous proof of qualified human review

I had started building this project (solo) as part of The Secure Program Synthesis Hackathon and I really got the in depth and critical feedback of shortcomings and what should / shouldn't be there such as

Instead of Github the project should be integrated with git
The current Z3 check only validates that the specification is internally consistent; it does not prove that the generated Python code actually follows that specification
Strengthening the system would require true code-to-spec verification and moving all ZK proof generation fully to the client side.

So, the idea is already validated and next I wanto focus working on the given feedback, and get it property-based/differential testing against the spec, tighter checks, client-side proving.

A concrete output is

A git native system allowing orgs using git to implement merge gating which would help keep the maintainers/reviewers optionally private with client side proving
the deployed github app ready to be installed and to be used as reference implementation

How the money will be spent

Minimum ($12k): ~3 months of focused build time to take Veridict from prototype toward a deployable, git-native system with a GitHub App as the reference implementation.

Covers:

Moving ZK proof generation fully client-side;
A git-native attestation bound and verified at the commit level (host-agnostic, not locked to GitHub)
Stronger spec-conformance checks (property-based / differential testing against the spec, going beyond the current Z3 spec-consistency check); and compute for ZK proving and evaluation runs.

Ideal ($30k): the above plus a part-time collaborator, language support beyond Python, a small external security review, and a longer pilot across several real repositories.

Discussion

DDeven ChoudharyEndorsement11d

Veridict tackles a growing challenge in AI-assisted development by combining automated verification with accountable human oversight. A thoughtful approach to improving trust and reliability in AI-generated code.

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PPratikshatrivedi214Endorsement9d

I've been following this space and honestly most tools for AI code review are just LLMs checking LLMs. Veridict is actual formal checks plus proof a real human looked at it. Saw the hackathon version and the feedback loop since then has been solid. Worth funding.

VViraj PatvaEndorsement9d

From a technical perspective, I like that Veridict acknowledges the gap between spec consistency and actual implementation correctness. The roadmap toward code-to-spec verification, property-based and differential testing, and client-side ZK proof generation addresses the right problems. A git-native merge gate with verifiable, privacy-preserving human review could become valuable infrastructure for trustworthy AI-assisted software development.