Our Philosophy

We don't build prompt libraries.
We build the science of
knowing whether AI is safe.

Peeld is a research-focused company building evaluation infrastructure grounded in adversarial testing, structured evidence, and the belief that safety is not a feature you ship once.

What We Believe

Four convictions that shape everything we build.

01

Evaluation is infrastructure, not a checkpoint

Most teams treat evaluation as a gate before deployment. We treat it as a continuous operating layer. Models change, data drifts, regulations evolve. Evaluation must be embedded in the system, not bolted on after.

01
02

Safety is a research problem, not a compliance exercise

Checklists don't catch novel failure modes. Real safety requires adversarial thinking, structured experimentation, and the intellectual honesty to test assumptions you hope are true.

02
03

Evidence must be shareable, not siloed

A safety claim without evidence is marketing. We produce structured, auditable artifacts that teams can share with regulators, customers, and each other. Trust is built through transparency.

03
04

Complexity is the enemy of adoption

The best evaluation framework is the one teams actually use. We obsess over removing friction because every hour spent configuring pipelines is an hour not spent finding real risks.

04
The Difference

What we are not.

Prompt libraries with pass/fail labels

Structured evaluation grounded in AI safety taxonomies

One-size-fits-all benchmarks

Domain-specific modules built for your risk profile

Scores without context

Evidence artifacts with methodology, failure analysis, and audit trails

Run once before launch

Continuous evaluation embedded in your deployment pipeline

Research Principles

Built on science, not vibes.

100% of modules grounded in published AI safety research

Research-first

40+ structured attack vectors tested per evaluation

Adversarial by default

0 black boxes. Every criterion, rubric, and taxonomy is inspectable

Open methodology

Manifesto
“The cost of deploying unsafe AI is measured in trust. Once lost, no patch, update, or press release brings it back.”

This is why we exist. Not to add another tool to the stack, but to make the question “is this safe?” answerable, every time, with evidence.

See the research in action.

Explore our evaluation modules or talk to us about your safety challenges.