One of our clients faced a high-stakes regulatory audit with little time and even less structure. Their call center had logged over 10,000 recorded phone calls—completely unorganized and entirely unlabeled. There were no tags, no metadata, and no linkage to downstream records. The only fallback plan involved listening to each call individually, deducing the caller's identity from the audio, and manually verifying whether their issue had been resolved. This process, if followed, would have consumed weeks of staff time across multiple departments and left them with unverifiable edge cases. The client asked us for help.
The core problem was traceability. The organization had no systematic way to know what any given call was about, who the caller was, or whether their issue had been resolved. These weren’t optional questions; the audit required definitive answers. Furthermore, their operational systems—EHR, CRM, billing—were all disconnected from the raw audio data. Standard tools offered either generic transcription with no semantic structure, or search over embeddings without traceable verification. Neither option provided the clarity, security, or determinism needed to pass an audit.
We built a custom pipeline on top of SemDB, our in-house semantic database, designed specifically for structured retrieval across both unstructured and structured data. The first step was ingestion. We loaded the complete call corpus into SemDB and transcribed each recording using OpenAI’s Whisper model. The transcripts were then segmented using our proprietary chunking pipeline and embedded using SemDB’s hybrid architecture, combining semantic search with ontology-aligned structuring.
From these transcripts, we constructed an operational ontology tailored to the domain. Using custom extraction pipelines, we identified and normalized any available signal in the calls: names, Medicare numbers, email addresses, phone numbers, complaint types, refund requests, and more. We enriched this semantic graph with call metadata—most importantly, the phone number of origin—and began mapping these entities against records in the client's CRM and EHR systems.
Because SemDB is built to operate in secure environments, we were able to integrate with these external systems in a HIPAA-compliant manner. Unlike RAG-style systems, which would have tried to approximate outcomes using generative inference, we linked every record to verifiable downstream events. If a customer asked for a refund, we looked for the corresponding invoice and refund transaction in the accounting system. If they reported an issue with an order, we checked the order management system for cancellations, updates, or follow-up communications. Every claim in the call was matched with an observed system state.
To streamline this process, we defined a workflow within SemDB to disposition each call. Was the issue a billing complaint? A cancellation request? A scheduling error? Once dispositioned, we verified the expected resolution programmatically. Any mismatch was flagged for remediation. Several such mismatches were discovered—calls where issues had gone unresolved—but because we ran the pipeline before the audit deadline, these gaps were corrected in time.
The result was a full audit trail for every call in the corpus: who called, what they asked for, what happened, and where supporting evidence was found. The client passed their audit with no exceptions. The system identified and remediated several cases that had previously slipped through the cracks. What would have taken teams of people weeks to process manually was handled in days, end-to-end, without hallucinated responses or black-box logic.
This project demonstrates the extensibility of SemDB’s architecture for enterprise-scale semantic reasoning over unstructured inputs. By combining transcription, semantic extraction, and structured system integration, we were able to treat voice data as a first-class citizen in an auditable data graph. The ontology-driven design allowed us to bridge the gap between conversational content and operational outcomes with full traceability. No existing off-the-shelf product would have provided the determinism, transparency, and integration fidelity required for this use case.
To explore how this system architecture can be adapted to other high-risk domains—such as compliance reviews, customer service QA, or clinical audit logging—visit semdb.ai or contact the Intelligence Factory team.
