The agentic CX loop
Most CX AI answers.
Clarity learns.
Clarity listens, reasons, acts, verifies and learns. Every conversation makes the next one better.
The problem
An open loop forgets.
Signal goes in. An answer comes out. Nothing returns. The same gap reappears next quarter, and nobody knows it was there.
Open loop
#48120 · refund window
Customer asks
09:14
AI answers
Sent
Conversation closed
09:16
Nothing checked it
no score
Nothing returned to the source
no fix
Wrong again
Closed loop
#48120 · refund window
Customer asks
09:14
AI answers
Sent
Scored against the rubric
Unverified
Knowledge base updated
+1 article
Answer now grounded in an approved source
Verified
Answered from source
What it costs
An open loop leaks at every stage.
Not one failure. Five, compounding. These are figures customers gave us about their own operations.
A national service operator, measured across its own channels.
And fifteen reports rebuilt every day to keep up in the meantime.
80% of inbound calls still open a ticket.
Enterprise organisations name a single digit figure. Evaluating eight calls per agent per month.
They trace to knowledge gaps. 10% were scored against the wrong article entirely.
And when it fails, it fails in public.
−47
NPS, voice channel
A telco virtual advisor answered live customers for months before anyone measured it. Then it was switched off.
20
Calls a day, peak
Two vendors, six months. The pilot never handled more than twenty calls a day, and failed the moment real customers used it.
26%
Of a scored sample
Answers that were wrong or had no source to check them against. Found only once someone scored the conversations, not before.
$600k
Per month
Recruitment and onboarding spend to replace agents who leave, in a team where the same gaps resurface every quarter.
How it works
Five stages. One data layer.
Follow one customer conversation all the way round. Each agent does its part on the same data layer, and what the loop learns is written back, which is why the next conversation is better grounded than the last.
One conversation, all the way round: Voice of Customer ingests it, Brain Guard grounds it, AI Support Agent answers, Agent QA scores it against your rubric, and what the loop found is written back. Synthetic conversation, real verdict taxonomy.
In production
The loop, running today.
Five stages, live in banking, telecom, marketplaces and delivery.
55%
less time spent on voice of customer analysis.
“Clarity helps us replace guesswork with real data from our feedback. I can go into product meetings with clear evidence.”
Jen Barwick
Head of Customer Experience
Chosen by OpenAI to surface insights from customer conversations securely.
700 million
weekly active customers
One AI layer across a national contact centre operation.
126 million
global subscribers
“Clarity treats all of our unstructured customer feedback like true data. Their NLP models pick up on sentiment and sort it into categories. We can search everything instantly, see the numbers, and even build our own customizable feedback dashboards.”
Alexandra Motto
Director of Product and Design Operations
20 million
monthly customers
Turning rider and captain feedback into smarter everyday experiences.
70 million
weekly active customers
Listen · Voice of Customer
Every channel. One stream.
Calls, chat, WhatsApp, social, app reviews, tickets and surveys land on the same data layer, in the same shape, in real time.
Signal · Voice of Customer
The complaint outside and the ticket inside.
Public signal joined to contact centre signal on one data layer. A spike that means nothing alone means something next to the other one.
Act - AI SUPPORT AGENT and AGENT ASSIST
It resolves what it can. It hands over what it cannot.
AI agents work across chat and voice. Agent Assist sits beside your people with a drafted answer and the source it came from. Every response carries a confidence score, and the score decides who finishes the conversation.
Resolves end to end.
Chat and voice, in your customer’s dialect, inside your existing contact centre.
Copilots your agents.
A drafted reply, the source it came from, and a confidence score, in the agent’s own console.
Knows when to stop.
Below your confidence threshold the conversation goes to an agent, with everything so far attached.
Verify · Brain Guard and Agent QA
Three ways an answer can be wrong.
Only one of them is the agent’s fault. Unverified means no source existed to check the answer against. That gap belongs to the knowledge base, not the person.
Verdict taxonomy is the real one: verified, unverified, incomplete, failed. Distribution illustrative.
100%
of conversations scored
3 to 8%
is what quality teams sample today, on their own numbers
One rubric
yours, applied to human and AI alike
Reason codes
on every failure, so the fix has an owner
Learn · Brain Guard
It finds the gaps, then closes them.
Brain Guard reads every verified conversation. Where the knowledge base has no answer, it identifies the gap and writes the article. Where two articles contradict each other, it finds the conflict and fixes them. Most platforms cannot tell you the gap existed.
Deployment
The loop doesn’t stop at your border.
In-region cloud, sovereign private cloud, or fully on-premise with GPU inference in your own datacenter. Model and cloud agnostic: bring your own models, or run open weights on your hardware. Same topology in every mode.
Reference architecture
Channels
voice · chat · social · tickets
↓
Your boundary
Data layer
Inference
Verify
Knowledge
↓
Deployment
in-region · sovereign · on-premise
How to start
Thirty days. Your data. Your rubric. A number you didn’t have before.
Step 1 · scope
A working session, not a slide deck. We agree the use cases, the success criteria and the rubric. Yours, not ours.
Step 2 · one file transfer
No integration. Historical call transcripts, ticket exports and your knowledge base, moved once.
Step 3 · the readout
Every conversation scored. Verified, unverified and failed, with reason codes. Plus the quality baseline that sets your opening bar.
Fixed price, fixed scope, credited in full against a first-year contract.
The thirty days start when your data arrives, not when you sign.
For security and data governance
You’re being asked to release call recordings. Here’s everything you need to say yes or no.
We know this decision sits with you, and we know it is the step where these projects actually stall. So rather than ask your colleague to relay answers, here is the pack: what we need, what we do with it, where it is processed, what gets stripped before storage, and what happens to it afterwards.
What data we need to start, and what we don’t
PII removal at ingestion, technical note
Processing locations and data residency options
DPA template and sub-processor list
Close the loop on every conversation.
And somewhere at the end of the loop, a customer got a straight answer the first time they asked, and an agent had the right one to give.
Clarity. Secure customer service that builds the product.