Monitor AI conversations, detect policy violations, investigate incidents, and give your team the operational insights needed to continuously improve your AI applications.
The problem
Your AI is already talking to customers.
You’re not in the room.
Shipping an assistant is easy. Knowing what it said in production, catching a bad reply, and having someone own it is not.
You only see a sample
Sampling chats in a spreadsheet doesn’t scale. The failures that hurt are rare — and already in front of a customer.
The first alert is a complaint
Uptime alerts don’t catch a risky reply, a policy miss, or a confident wrong answer. Your users become the monitoring system.
There’s no incident trail
Screenshots and “can you find that chat?” aren’t a process. No shared evidence, no owner, no resolution.
Why Sentiora
See it. Catch it. Own it.
One loop: every production conversation in, policies on live traffic, an incident with a quote and an owner, then you change the prompt or policy with evidence.
Takes a few minutes to setup
Your app still calls its model.
Sentiora gets a copy.
Create a key
Generate a project API key and store it on your server.
Step 1
Add to your handler
Paste the SDK (or a REST POST) into the same function that calls your model.
Step 2
Live conversation
Once a real conversation arrives, the project is Connected.
Step 3
With and without
With Sentiora, vs how most teams do it today
Most stacks can tell you the model was up. They cannot tell you what it said, whether that was allowed, or who owns the follow-up.
With Sentiora
Without
What you see
The conversation: what the user said, what the model replied
Tokens, latency, maybe a log line
How much
Every production turn
A sample in a spreadsheet
When you find out
A finding, while it’s still an internal problem
After a ticket, a complaint, or a message that shouldn’t have gone out
What gets caught
Policy misses, risky replies, injection, and other live-traffic failures
Whatever a human happens to notice
What you open
The quote, why it was flagged, the thread around it
A screenshot and a guess
Who owns it
A named owner, open → resolved
“Can you find that chat?”
Where the work lives
One incident, with history
Slack, CSV, and inboxes
After you ship a fix
You can see if production got better
You hope it did
Your model
Unchanged. Sentiora is a copy from your existing handler
You still call the same LLM — you just have no operations layer on top
Lab evals
Still useful. They don’t replace watching production
Green in the lab, silent in production
Improve the system
Catch the pattern. Improve the reply.
01 — Define what ‘wrong’ looks like
Set the signal
Turn vague worries into checks for unsafe advice, made-up facts, policy misses, or prompt injection attempts.
02 — Keep the full context
Review the evidence
Open the exact customer exchange, the policy result, and the reason it was flagged—before it turns into a screenshot and a guess.
03 — Make the fix measurable
Ship the learning
Assign an owner, update the prompt or policy, then watch the same signal so you know whether production actually improved.
CATCH IT SOONER
Monitor live replies, turn risky moments into evidence, and feed the learning back into your AI before the customer has to tell you.
FAQ
