Hear what the
words don't say.

No. In an on-premises deployment the system of record — transcripts, scores, metrics, search index — lives entirely in your SQL Server, and audio is streamed in memory from your recorder and discarded after transcription. Nothing is written to disk outside your boundary, and the only network path is outbound HTTPS. We also offer a fully air-gapped configuration with local inference for environments that permit no egress at all.

Workforce management suites are built around scheduling and adherence, and their speech analytics is usually keyword spotting — did the agent say the phrase or not. Overtone scores meaning, not vocabulary: whether the customer's objection was actually addressed, whether empathy landed, whether the disclosure came before the price.

We integrate with Verint, NICE IEX, Calabrio, and Alvaria rather than replacing them. Scores flow back into your WFM so coaching time gets scheduled against real gaps.

That's the design assumption. Scoring Studio lets you define your own dimensions, weight them, set pass thresholds, and require evidence, then apply that rubric to every call. Most customers start by importing the QA form they already use in a spreadsheet, so week-one scores are directly comparable to their historical baseline.

Managed SaaS with a standard connector: scored calls within five business days, production rollout in about three weeks. Private cloud: four to six weeks including your security review. On-premises: typically eight to twelve weeks, and the long pole is almost always your change-advisory process rather than the software.

Card numbers, government IDs, dates of birth, account and policy numbers are detected and masked before the transcript is persisted. You choose whether masked spans are recoverable by privileged role or destroyed irreversibly. The redaction pass runs before indexing, so PII never enters the search index.

No. Your data is never used to train shared models, and inference providers are contractually bound to zero retention. If you want a model tuned to your vocabulary — product names, plan codes, regional accents — that adaptation is trained on your data, for your deployment only, and stays there.

Because the honest number depends on annual conversation volume, deployment model, how many rubrics you run, and which systems we connect to. A single-site SaaS pilot and an air-gapped multi-site rollout are not the same product to deliver. Tell us your volume and stack and you'll have a real number, not a range.