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CH Chats
Operations · Conversational AI

Chat the way
you already chat.
Governed anyway.

A text box, a model, and an answer. That part is familiar on purpose. What is different is everything around it: the turn is anchored to a sanctioned use case, your guardrails are in the prompt, your filters are on the text, and ten evaluators read the answer before you do.

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Why it matters

Adoption is not
the problem.
It already
happened.

Your people are using AI today. Most of it is happening on consumer accounts, with company information in the prompt, and no record that any of it occurred. Blocking it moves the traffic, it does not reduce it. The only thing that changes behavior is an internal option that is not worse than the one they are already using.

Not a downgrade

Real models, real speed, attached documents, saved threads. If the governed tool is slower or dumber, staff go back to the browser tab.

One model catalog

Choose from the providers your organization has approved. Individual models can be enabled, disabled, or set to require approval before anyone can use them.

Every turn recorded

Prompt, model, attachments, response, and evaluator scores are all captured. When someone asks what the AI was told, there is an answer.

Cost in the open

Input, output, and total tokens per exchange, so consumption is a number you watch rather than a surprise at renewal.

The panel beside the box

Five things you
can attach to
a question.

The chat surface is a text box with a panel next to it. That panel is where governance stops being a policy document and becomes something you click.

01

Use case

Pick the sanctioned scenario this question belongs to. The use case description is itself the prompt, written once and approved, so the same task gets asked the same way by everyone.

02

Risks

The risks linked to that use case, scored on a probability and impact matrix. You can see what this kind of question is known to go wrong at before you ask it.

03

Guardrails

Behavioral boundaries organized by industry and purpose. Selected guardrails are injected into the prompt to constrain the response, not appended as a hopeful disclaimer.

04

Filters

Pattern-based content filtering built from a library covering personal data, toxic content, and injection attempts. Patterns combine, and there is a test area so you can prove one works before trusting it.

05

Documents

Attach approved documents as context. The model answers from your policy, your contract, your standard, rather than from what it happens to remember about the topic.

06

Prompt library

Save what worked and reuse it. Good prompts stop being tribal knowledge that leaves when one person changes teams.

Ask better

The platform will
improve your
question first.

Type a rough version of what you want and use Generate. You get back a sharpened prompt, the risks that kind of request carries, and the guardrails the platform recommends attaching to it. Accept it and it becomes your prompt. Most bad AI output starts as a vague question, and this is where that gets caught.

Enhanced prompt

A tighter, more specific version of what you typed, ready to send or edit further.

Surfaced risks

What could go wrong with this class of request, identified before the answer exists rather than after.

Suggested guardrails

The platform matches your description against the guardrail library and proposes the ones that fit.

One-click adoption

Take the suggestion or ignore it. Either way the choice is visible and recorded.

After the answer

Ten evaluators
read every
response.

With monitoring on, each response is scored by ten independent behavior checks. Each returns a score out of one hundred, with deductions weighted by severity, so a single critical finding costs far more than a handful of minor ones. The results sit under the answer where the person who asked can see them.

Prompt injection / jailbreak Hallucination Behavioral drift Refusal calibration Bias Dangerous advice Bigotry Logical fallacy Partisanship Misinformation
The difference

Same question.
Same model.
Different record.

An employee asking an AI a work question on a consumer account, against the same question asked here.

Consumer chat
Chats in Governance 1st
Approved use case
Whatever they thought of
Selected from the registry
Boundaries on the answer
The vendor's defaults
Your guardrails, in the prompt
Sensitive data in the text
Nothing checks
Pattern filters you configured
Quality of the response
Trust and hope
Ten evaluators, scored
Evidence a year later
None
Prompt, model, sources, scores
Get started

Try it on a
real question.

Book a demo and bring something your team actually asks AI. We will run it once with nothing attached, then again with your use case, guardrails, filters, and documents in place, and put the two answers side by side.

Book a demo → See Agents →
✓ Anchored to a sanctioned use case
✓ Guardrails and filters on every turn
✓ Ten evaluators, visible scores
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