Structure the calls
Transcribe recordings, segment the text, and extract available identifiers and issue signals.
Audit readiness and resilient operations
Turn 10,000+ unstructured calls into evidence an audit can use.
One unnamed organization faced a high-stakes regulatory audit with recorded calls disconnected from its operating records. A traceable review pipeline helped resolve missed cases before the deadline and supported an audit passed with no exceptions.
Business consequence
The fallback was weeks of manual review—and still left edge cases difficult to verify.
The call center had logged more than 10,000 recordings with no labels, tags, usable metadata, or linkage to downstream records. Staff would have needed to listen to calls individually, infer who called, and then determine whether each issue had been resolved.
The audit required definitive answers: what was the call about, who was involved, what happened next, and where was the supporting evidence? Raw audio sat apart from EHR, CRM, billing, accounting, and order-management records needed to answer those questions.
Documented result
The audit passed with no exceptions.
10,000+recorded calls were included in the reported corpus.
Reported in daysversus weeks of expected manual review.
No exceptionswas the outcome reported for the regulatory audit.
The original case reports this result for one unnamed client. The exact elapsed time, customer identity, and audit body are not published.
The workflow also surfaced several unresolved issues. Those gaps were corrected before the audit deadline; the source does not publish a count.
Operating explanation
Connect what was said to what the organization actually did.
The review began by transcribing and segmenting the recordings. Available signals—including names, Medicare numbers, email addresses, phone numbers, complaint types, and refund requests—were normalized alongside call-origin metadata.
Those signals provided ways to map calls to available CRM and EHR records and then inspect relevant downstream evidence. For example, a refund request could be checked against its invoice and refund transaction, while an order issue could be checked for a cancellation, update, or follow-up communication.
Link operating records
Use call metadata and extracted entities to find relevant CRM, EHR, billing, accounting, or order records.
Disposition the issue
Classify examples such as billing complaints, cancellation requests, and scheduling errors, then define the expected evidence of resolution.
Resolve mismatches
Flag cases where the expected downstream event was missing so staff could investigate and remediate before the deadline.
Technical reason to believe
Record-linked verification made the review inspectible.
The custom pipeline used OpenAI Whisper for transcription, semantic search and ontology-aligned structure to organize the calls, and SemDB—the team’s semantic database—to connect unstructured voice content with structured operating records.
The mechanism stayed subordinate to the audit workflow: extract a supported signal, locate the relevant system record, check the expected event, and preserve where the evidence was found.
The same pattern may be relevant to compliance reviews, customer-service quality assurance, or clinical audit logging when the evidence and controls fit the domain. Those are possible applications, not additional outcomes claimed by this case.
Case authorMatt Furnari, CTO, Intelligence Factory
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