Pre-launch red-team for AI support bots

Would your chatbot survive its first lawsuit?

Air Canada's chatbot invented a refund policy that didn't exist. A tribunal made the airline pay. Before you ship your AI agent, find out what it promises when nobody is watching.

See a sample report →
Built on real cases: Moffatt v. Air Canada (2024 BCCRT 149) · the $1 Tahoe bot · DPD · NYC MyCity
It has already happened

Three ways a support bot becomes a liability

These aren't hypotheticals. Every scenario in our test suite is reconstructed from a documented, public failure.

Case 01

It invented a policy that didn't exist

A bot offered a bereavement fare it had no authority to offer. The company was held liable — disclaimers did not save them. Does yours invent commitments too?

Moffatt v. Air Canada, 2024 BCCRT 149 — C$812 awarded
Case 02

It agreed to something absurd

A dealership bot agreed to sell a $76,000 vehicle for $1. After it went viral, it absorbed 3,000+ copycat jailbreak attempts from strangers trying the same trick.

Chevrolet of Watsonville, Dec 2023
Case 03

It never routed the message that mattered

A customer says "I dispute this transaction." Under Regulation E that can start a formal error-resolution clock. Your bot answers politely. Nobody in disputes ever hears about it.

Flagged by the CFPB, 2023 — no hallucination metric catches this
The test nobody else runs

Every tool measures hallucinations. None ask about legal routing.

When a customer says something legally operative — "I dispute this charge", "I want my data deleted", "I revoke authorization" — does your system recognise it, preserve it, and route it to a human?

Regulation E — error resolution Regulation Z — 60-day billing notice GDPR Art. 15 / 17 CCPA opt-out

Miss the routing and you have a problem that no dashboard will ever show you. We test for exactly this.

The deliverable

A report you can forward to your lawyer

Not a dashboard. Not a subscription. A document — with transcripts — that tells you where your bot crosses the line before a customer finds out.

30 red-line scenarios against your live bot

False promises, refund & policy invention, legal routing, escalation dead-ends, brand self-harm, price & commitment errors.

Every failure with the actual transcript

Full conversation logs, exact trigger phrases, and the step where your bot crossed the line — reproducible by your team.

Severity ranked by legal exposure

Each finding mapped to the precedent or regulation it violates, so legal and CX can triage in one meeting.

Fix guidance per finding

Concrete guardrail and escalation-path recommendations, written for the team that owns the bot — not a generic checklist.

Pricing

Pay once. Get the report. Done.

No subscription, no integration, no access to your customer data.

Most popular

Pre-Launch Red-Line Report

$299

Single bot, 30 scenarios, delivered within 5 business days.

Multi-bot / multi-language

$499

Up to 3 bots or locales, same scenario suite, consolidated report.

Re-check after you fix it

$79

We re-run the same suite after your fixes and verify each finding is closed.

Not a subscription. You pay once, you get a report. If we find nothing, the report says so — and that's worth knowing too.

FAQ

Questions teams ask before buying

Do you need access to our systems or customer data?

No. We test with synthetic users against your public chat widget or a sandbox account you create for us. We never read real customer conversations, never connect to your backend, and never train on your data.

Which bots can you test?

Any text-based support bot reachable through a web chat widget or API — Intercom, Zendesk, Drift, custom-built. We don't test voice agents (that's a different discipline).

How is this different from Cekura, Coval or Hamming?

They sell subscription platforms for teams that build and operate voice agents at scale. We sell a one-time report for teams that just shipped a text bot and want to know their legal exposure before launch. Different buyer, different moment, different deliverable.

What if you find nothing wrong?

Then you get a documented, scenario-by-scenario pass record — the same artifact your legal team would want after an incident, except you have it before one.

Who are you?

Built by an engineer who has spent years on both sides of this: shipping AI agents and reading the incident reports after they go wrong. The scenario library is maintained as new cases and rulings emerge.