- A large language model reads or listens to every call and answers the same fixed questions: what the customer wanted, what happened, and whether the issue was resolved.
- Calls marked unresolved go straight to a daily follow-up list, so an open issue gets a callback before the customer calls again or cancels.
- Your quality team checks a sample of summaries against the recordings and tunes the prompt until the flags match their own judgment.
- The summaries and recordings run in your own environment, and PII redaction can remove personal data before a model ever sees it.
Know what every caller wanted, not just the ones you sampled
Your contact center records thousands of conversations a day. Supervisors and quality teams listen to a small sample, and agents write their own wrap-up notes at the end of a busy shift.
That leaves two gaps. You do not know, call by call, what the customer wanted and whether they left with an answer. And the customers most likely to leave are the ones whose issue stayed open, which you find out about only when they call again or cancel.
How call summary and resolution works
The pattern is generative AI over your own call data: a large language model reads each conversation and answers the same fixed questions about it, and Enterprise h2oGPTe runs the summaries inside your environment.
Step 1: bring in the calls
Start from transcripts, or from recordings through speech-to-text.
Step 2: ask the same questions of every call
A fixed prompt asks what the conversation was about, what the customer asked for, what the agent did, and whether the issue was resolved.
Step 3: write the summary
The model writes a short summary in your own format, ready for the CRM record or the case note.
Step 4: flag what stayed open
Calls marked unresolved go to a follow-up list with the reason, and sentiment analysis can add a view of how the customer felt at the end of the call.
Step 5: check before you trust it, then act
Your quality team compares a sample of summaries and flags against the recordings and tunes the prompt until the flags match their own judgment. Open issues then route to a callback team, and the call reasons show what drives repeat contacts.
Keeping recordings inside your own environment
Enterprise h2oGPTe runs on premises, air-gapped, in a private cloud or in the cloud, so call recordings and summaries stay where your policies say they must. Guardrails and PII redaction control what the model may see and write, which matters when a recording includes account numbers or health details a customer volunteers.
That governance answers a question many teams ask before they start: not whether the model can summarize a call, but whether the recording ever has to leave the building to get a summary. It does not.