How Teams Turn Conversations into Action

After every call, Cognia converts the conversation into owner-assigned follow-up tasks — no manual CRM entry, no separate note-taking. Each task is attributed to the team member who made the commitment during the call, so nothing is reassigned incorrectly after the fact. Sales managers get a live view of what each rep committed to across every recorded call, making pipeline reviews more accurate and faster to run. Teams that previously spent 20–30 minutes per call on post-call admin typically report reducing that to under five minutes within the first week.

Yes. Support leads use CogniAIX to extract escalation owners and surface recurring themes across recorded calls without replaying each one. By searching across transcripts, support managers can identify the most common failure points in a product, find which issues are escalating most often, and track whether the same question keeps coming up — all from a searchable archive rather than individual call reviews. Teams with high call volumes have used this to cut the time needed to produce weekly support summaries by more than half, replacing manual listening sessions with query-based analysis.

We support private deployments with audit trails, role-based access controls, and data retention policies for teams with strict compliance requirements. Enterprise customers can configure which team members have access to which recordings, restrict exports, and require manager approval before transcripts are shared externally. We offer SOC 2 Type II documentation and can provide custom data processing agreements for enterprise contracts. If your organization has specific requirements around data residency, single sign-on (SSO), or integration with existing security tooling, please contact our team to discuss a tailored setup.

Most teams notice a change within their first meeting — not because the technology is complex to set up, but because the output is immediately visible. When a meeting ends and tasks have already been assigned and pushed to your project tool before participants leave the call, the difference is obvious on day one. The Fluxion Solutions case study on this page describes a team that cut their post-sprint follow-up admin from four hours to ninety minutes in their first sprint cycle. The pattern we see consistently is: immediate time savings on follow-up admin, and broader workflow improvements (fewer missed commitments, faster task completion) emerging over the first three to four weeks.

CogniAIX offers a free tier that includes transcription and basic task extraction with no credit card required. Paid plans add higher usage limits, integrations with Slack and Jira, team management features, and priority support. Enterprise plans include private deployment options, audit logs, SSO, and custom data retention policies. See current plans and pricing on our pricing page for up-to-date details. If you are evaluating CogniAIX for a team of ten or more, contact us to discuss volume pricing and a guided pilot setup.

CogniAIX integrates with Zoom, Google Meet, and Microsoft Teams, as well as audio and video file uploads for recordings from any source. You can set it to join meetings automatically, or you can upload recordings manually. Task outputs sync to Slack, Microsoft Teams channels, Jira, Asana, and Trello. If your team uses a tool not listed here, contact us — we have built custom integrations for enterprise customers and can scope the work required to connect CogniAIX to your existing stack.

Task extraction precision runs 84–92% in controlled environments, measured on a benchmark of 300 real recordings. The main factor affecting accuracy is audio quality — clean recordings with single speakers or well-separated speakers in a conferencing environment perform at the higher end of that range, while recordings with significant crosstalk or background noise perform lower. Owner detection accuracy (identifying who made each commitment) runs approximately 89% in lab conditions. We publish these benchmarks openly because we believe accuracy should be measured on real-world outcomes rather than on polished demo recordings.