Step 01
Connect your model
Point Verity at your model's endpoint. No rewrites, no new dependencies — just connect and start evaluating.
Evaluate your
model explainability.
Upload your model. Get back a complete picture of your model explainability — whether its explanations are technically accurate and actually understood by the people who rely on them.
How It Works
Three layers of model explainability evaluation.
Every model that runs through Verity gets checked on three dimensions of model explainability. Each one tells you something different about whether your explanations are ready to ship.
Layer I: Technical Check
We don't guess. We prove.
Verity checks whether your model explainability is grounded in real decisions, not just outputs that sound plausible and whether explanations stay consistent when inputs change slightly. No assumptions, just measurable results.
Accuracy > 0.85Consistency < 1.2
Layer II: Stakeholder Check
AI reviewers built to think like your end users check if explanations land with the right audience:
Compliance Officer
Clinical Expert
Lay User
Layer III: The Trust Score™
Verity combines technical accuracy and stakeholder clarity into one score you can act on. Set a threshold and block deployments automatically if your model doesn't meet it.
94/100Audit Passed
Built for every role
Whether you're building, shipping, or reviewing.
Verity is designed for everyone involved in getting an AI model from code to production.
Students & Early-Career Engineers
Build a portfolio you can actually defend.
Verity's free tier gives you the same evaluation tools used in production. Run your model through a full explanation check and export a scored report — concrete evidence your work meets professional standards.
ML Engineers & Small Teams
Validate faster, ship with less risk.
Drop Verity into your existing workflow. Set quality thresholds, catch explanation regressions in CI, and give reviewers a clear report instead of a spreadsheet of raw metrics.
Startups Shipping ML Features
Move fast without breaking trust.
You're shipping to real users. Verity gives you a quick confidence check on every release — so you know your model explains itself correctly without slowing down your team.
Governance & Review Teams
Clear evidence. No translation needed.
Get a single, readable score for every model — backed by both technical metrics and human-clarity results. Use it to approve releases, flag concerns, and maintain a full audit trail.
How to get started
Set up in minutes, not days.
Verity plugs into your existing workflow. Automate checks, flag only the failures, and keep model quality visible to everyone — builders and reviewers alike.
Step 02
Set your quality bar
Define the minimum standards for accuracy, consistency, and stakeholder clarity. Any model that falls short gets flagged before it ships.
Step 03
Catch problems before release
Every evaluation runs automatically. If explanation quality drops, you get alerted — before your users are affected.
Step 04
Export evidence for sign-off
Generate decision logs and review reports so your whole team — engineers and reviewers — works from the same source of truth.
What you get
Clear outputs your whole team can act on.
Every Verity evaluation produces four things — useful for engineers, reviewers, and anyone signing off on a release.
A clear score for every evaluation run
One number backed by both technical checks and human clarity tests. No guesswork, no subjective review.
Explanation accuracy breakdown
See exactly which explanations passed or failed — and why — so you know precisely what to fix.
Stakeholder clarity reports
Know whether your explanations land with risk teams, domain experts, or non-technical users before they ever see them.
Audit evidence you can share
Timestamped records for every evaluation run. Ready for model sign-off, compliance reviews, or external audits.
Common Questions
Questions we hear a lot.
I'm a student or just starting out. Is Verity right for me?
Yes. Verity's free Developer tier gives you access to the same evaluation tools used by teams shipping production models. It's a great way to add verified explanation quality to your portfolio — and show you understand more than just model accuracy.
We're a small startup moving fast. Is this overkill?
No. If you're shipping ML features to real users, you need to know your model explains itself correctly. Verity gives you that answer quickly and automatically, without adding friction to your release process.
Will this slow down our release cycle?
No. Most teams run Verity in CI on a representative test set and block merges only when quality thresholds fail — the same way you'd gate on test coverage.
Can we use Verity with our existing model setup?
Yes. Verity works with any model you can run inference on — hosted APIs, self-managed endpoints, or local development environments.
Who typically owns Verity on the team?
Usually ML engineering and risk or compliance share ownership. Engineers use it to catch issues early; reviewers use the reports to approve releases.
What do we show auditors or leadership?
Verity generates scored reports, change history, and stakeholder clarity outcomes you can share directly. No manual writeups required.
$verity deploy --production
Ready to ship AI your team can stand behind?
SOC2 Type II Compliant Environment