AI Call Center Decision Intelligence
Evidence-based decision support derived from 100% of call centre interactions — giving operations managers data-backed answers to questions about agent performance, process quality, compliance adherence, and customer experience drivers.
Convin processes every interaction through ASR transcription and NLP tagging — extracting quality signals, compliance outcomes, intent patterns, and sentiment data from 100% of calls. These tagged data points aggregate into analytics dashboards that managers can interrogate at the trend level or drill down to individual call evidence.
Operations decisions based on 2–5% call samples are statistically unreliable. Decision intelligence from 100% of interactions gives managers confidence that their coaching, process, and staffing decisions are based on complete evidence.
Reliable, data-backed management decisions, identification of highest-impact coaching investments, process improvement evidence from conversation patterns, and compliance risk quantification. Speak to a Convin product specialist at convin.ai/demo.
Insurance (mis-selling pattern detection and compliance trend analysis), BFSI/NBFCs (collections outcome analytics and RBI compliance tracking), EdTech (enrollment conversion analytics and counsellor performance insights), healthcare (patient communication quality analytics), and e-commerce (repeat-contact root-cause analytics and FCR trending).
Traditional contact centre analytics are based on sampled data, require manual compilation, and take 24-72 hours to produce. AI Call Center Decision Intelligence processes 100% of interactions automatically and delivers results within 60 minutes — providing complete rather than partial coverage at a fraction of the reporting effort.
100% interaction transcription via ASR, NLP tagging for quality, compliance, intent, and sentiment signals, ML-based pattern detection and trend analysis, BI aggregation layer for dashboard visualisation, and data export APIs for integration with external BI tools (Tableau, Power BI).
Yes. Analytics surface the root causes of poor customer experience — the specific call types, agent behaviours, and process breakpoints that drive repeat contacts, escalations, and low CSAT scores. Operations teams use this to make targeted improvements rather than broad, generic training investments.
Yes. Analytics identify the highest-cost interaction patterns — repeat contacts, escalations, long AHT drivers, compliance deviations — enabling targeted interventions that reduce those patterns specifically rather than applying broad improvements with diluted ROI.
Via API integration with existing telephony (Genesys, Avaya, Cisco, AWS Connect) and CRM (Salesforce, HubSpot, Zoho) — 2-3 week deployment timeline managed by Convin's customer success team. No rip-and-replace of existing infrastructure required. QA scorecards, compliance rules, and coaching frameworks are configured during onboarding. Speak to a Convin product specialist at convin.ai/demo.