AI use case · Is your data ready for customer service automation?An AI agent in front of customers says what your data says. Publicly.
Service agents promise instant answers, around the clock. Every answer draws on your records: who the customer is, what their contract covers, what the current policy says. When tickets don’t tie to accounts and entitlements don’t match billing, the agent makes promises the business can’t keep, at scale.
Starts with a 30-minute call. Commits you to nothing.
What it needs
What customer service automation needs from your data.
Each requirement, the systems it lives in, and what we check. The AI is the easy part; this list is the hard part. Each check feeds the Truth Score™, a score out of 100 for how far your data can be trusted.
Truth Score pillars that gate it mostAccuracyGovernanceConnectivityThe six pillars
Where it breaks
Every system looks fine. The gaps between them don’t.
Where customer service automation breaks: not inside one system, but where two of them disagree about the same customer, deal or invoice.
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Support ↔ CRM
Tickets from people the CRM doesn’t know
What it does to the AIThe agent answers without the account context.
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Support ↔ Billing
Premium service levels on customers who stopped paying
What it does to the AIThe agent promises what the contract no longer covers.
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Documents ↔ Support
Help articles that contradict the current policy
What it does to the AIThe agent quotes the old refund rule, to every customer who asks.
Gaps to test for this use case, not client findings.
Before you sign
Questions to ask the vendor selling it.
Four questions that separate a demo from a deployment. Bring the answers, or the proposal, to the Truth Score Read and we test them against your records.
- Q1
Which of our systems does the agent read before it answers?
If it reads only the help centre, it can’t know what this customer is entitled to.
- Q2
What does it do when the customer isn’t in the CRM?
Unknown customers are where wrong promises get made.
- Q3
Can every answer be traced to its source?
When a customer quotes the agent back to you, you need to know where it got the answer.
- Q4
Where are the hand-offs to a person, and who sets them?
Refunds, cancellations and complaints should be decided by policy, not by the agent’s confidence score.
How a Truth Score Read tests it
Customer service automation, tested against your own records.
Our engine matches the customers, people, deals and invoices across the exports from the systems behind the decision. Every red flag is checked by hand against sample records before sign-off, and the verdict comes in writing. Here is what that means for this use case.
- 01
Tie tickets to customers
Support users and organisations matched to CRM accounts and billing customers, by email, domain and name.
- 02
Check what customers are owed
Service levels and entitlements in support compared with contracts and billing status.
- 03
Read the categories
Tags, reasons and resolutions profiled, so you can see which intents the agent could learn reliably.
- 04
Give the verdict
Which kinds of question an agent can take now, and which must stay with a person.
Two systems are enough to start. We send the exact field list after the call.
How the Truth Score Read worksWhat you get back
See what comes back: a real sample memo.
One fictional B2B software company, Example Co: 72,837 records in 7 exports from its CRM, finance and HR systems, run through the same engine and the same checks as a paid Truth Score Read. Each system passes its own checks. Matched across systems, only 13% of deals tie to an invoice.
Questions
Customer service automation, answered plainly.
Anything else, ask on the 30-minute call. It commits you to nothing.
Book the callHow accurate does our data need to be for a customer-facing agent?
Accurate enough that every answer about an account, a contract or a policy is right, because the customer will act on it. That bar is higher than for internal tools, which is why we test the joins between support, CRM and billing first.
Can we deploy an AI agent on messy support data?
You can deploy it on the parts that are clean. General how-to questions may be; anything involving entitlements, refunds or account status needs the systems to agree first.
Do you review our help content?
Help content is assessed with your team, not from exports alone: we review it together for contradictions and outdated policy, alongside exports of tickets, accounts and contracts.
Which support systems do you work with?
Any that exports, which is all of them. Zendesk, Intercom and others all produce exports of users, organisations and tickets. No login and nothing installed.
Is your data ready for customer service automation? Find out before you sign.
Exports from two or more systems in. A signed verdict on this use case within 5 working days of complete exports. Starts with a 30-minute call. Commits you to nothing.
Starts with a 30-minute call. Commits you to nothing.