AI Calls: minutes, analytics, and agreement tracking

From telephony or conferencing, without separate manual upload if the source provides an API or a standard export. On input, the recording, participants, time, and identifier are captured so the call can be linked to a client, queue, or project.

The environment is chosen based on the data set and your security policy: cloud or on-prem. The request log, terminology dictionary, and, if required by policy, a ban on the provider training on your data are recorded separately. The storage layer is responsible for recording and transcript retention, access separation, masking, and controlled deletion.

The confidence threshold determines where a person is needed: disputed fragments go to a manual review queue instead of being returned as a reliable result. Noise, interruptions, overlapping voices, and rare terminology affect recognition, so we test their impact on your recordings before launch rather than promising accuracy in advance.

Through a pilot on historical calls. An expert labels the reference sample, then we calculate five metrics: errors in domain terms and names, accuracy of required protocol fields, completeness of detected agreements and next steps, share of manual edits, and time from call completion to result in the target system. Target values are set after labeling, not before.

To CRM, the project system, email, or the corporate portal, taking access roles and retention period into account. The record is processed under a service account with minimal permissions, idempotently and with change auditing, so reprocessing does not create duplicates.

For sales: capture the request, agreements, and next step with CRM field population. For the contact center: call review against the quality rubric and a queue of cases for the manager. For project teams: project-linked decisions, open questions, owners, and deadlines. The environment is the same; only the evaluation rules and the output differ.