Conversational AI

Conversational AI is software that enables organisations to automate and analyse customer interactions across voice, chat, and email using NLP, machine learning, and speech recognition. Contact centres use it to handle repetitive queries, coach agents in real time, and extract operational insights from every conversation — at a scale impossible with manual processes.

It captures spoken or written customer input, converts it to structured data via speech recognition and NLP, identifies intent, and triggers the appropriate response or workflow — an automated reply, an agent prompt, a QA flag, or a post-call summary. Convin's platform does this across 100% of interactions simultaneously.

To solve three problems at scale: too many interactions to review manually, inconsistent agent performance, and slow feedback loops. Convin customers report 28% AHT reduction and 94% QA automation coverage within 90 days — without increasing headcount.

Key benefits: 100% conversation coverage (vs 2–5% with manual QA), real-time agent coaching that reduces script deviations, automated compliance logging for audit-ready operations, and conversation analytics that surface customer intent trends invisible to spot-check reviews. Speak to a Convin product specialist at convin.ai/demo.

Insurance (IRDAI-regulated renewal and claims), BFSI/NBFCs (RBI collections), healthcare (patient communication and appointment management), EdTech (admissions and enrollment support), e-commerce (post-purchase support), and telecoms (retention and account management) — all high-volume, high-compliance environments where AI coverage and consistency directly affect both operational cost and regulatory risk.

Traditional tools evaluate 2–5% of calls after the fact, use fixed IVR scripts, and produce weekly reports. Conversational AI evaluates every interaction in real time, adapts coaching to individual agent behaviour, and surfaces actionable insights within minutes of a call ending.

Convin's Conversational AI is built on automatic speech recognition (ASR) for voice transcription, natural language processing (NLP) for intent and sentiment detection, machine learning models for QA scoring and coaching recommendation, workflow automation for post-call action triggering, and a real-time analytics layer that aggregates interaction data across voice, chat, and email channels.

Yes. Convin customers report 17% CSAT improvement and 21% FCR improvement. The mechanism: real-time agent coaching ensures every agent follows the right resolution path on every call, reducing the inconsistency that drives repeat contacts and poor reviews.

Yes. Convin customers report 28% AHT reduction, 80% reduction in manual QA effort, and 21% improvement in FCR — each of which directly reduces cost per interaction. Automating QA scoring and post-call workflows eliminates manual overhead that scales linearly with interaction volume.

Convin integrates with existing telephony infrastructure (Genesys, Avaya, AWS Connect) via API — no rip-and-replace required. Implementation takes 2-3 weeks: telephony integration, compliance rule configuration, QA scorecard setup, and a calibration session with the customer success team. Most customers run a focused pilot before full deployment.