Barg Labs · Cejel

Trust certificates for code.

Is this claim backed by evidence that survives someone else checking it?

Cejel is a deterministic, offline trust certificate for code.

Evidence before action. Cejel checks the engineering evidence behind the claims a repository makes.

Our story

One question, applied to code.

Evidence before action.

Code makes claims: that it is tested, that it handles secrets safely, that its dependencies are disciplined, and that its implementation matches its documentation.

The question is whether those claims are backed by evidence that survives someone else checking it.

Cejel answers that question from the repository itself. It makes its checks deterministically, records the evidence it used, and abstains when the available source is too thin to support a responsible conclusion.

Barg Labs builds Cejel for teams that need to inspect code before they can trust it.

Approach

Cejel starts from the evidence a repository makes available. It checks observable engineering signals rather than inferring what cannot be supported by the source.

The work is evidence-led rather than demo-led. Cejel records what it found and abstains rather than presenting a misleading score.

Leadership

Barg Labs is led by Houman Azimi-Nejadi, a software engineer and founder with more than 25 years of experience building complex software systems across multiple domains.

The company works closely with expert collaborators across product, engineering, and web systems as Cejel develops.

Cejel

Cejel is a deterministic, offline trust certificate for code. It examines the observable engineering signals behind a repository — test integrity, secret hygiene, dependency discipline, isolation, CI, auditability, and whether the code matches its claims — then produces an evidence-backed certificate and report.

Cejel complements the scanners teams already use. It can incorporate SARIF output and OpenSSF Scorecard results into one portable view, with contributing evidence clearly attributed. When a repository is unreadable or the available source is too thin, Cejel abstains instead of presenting a misleading score.

It runs fully offline with no signup, telemetry, or model call, making it useful for AI-written code, acquisitions, inherited systems, and regulated environments where the source cannot leave the team's perimeter.

Contact Barg Labs

For Cejel conversations, partnerships, or early access, contact the team directly.

team@barglabs.ai