The Problem With Manual Call Review
A mid-size e-commerce company handling 5,000 calls per day would need a dedicated team of 30–50 QA analysts just to sample 10% of calls — and even that sample is inherently biased toward escalations and complaints already flagged by agents. The remaining 90% is invisible.
This blind spot has real consequences. Product defect patterns emerge weeks later in returns data rather than immediately from call spikes. Agent coaching happens on intuition, not data. Emerging fraud patterns in payment calls go undetected. Customer churn driven by repeated unresolved issues is only visible in cohort analysis long after the damage is done.
Manual QA also doesn't scale. As call volume grows with the business, the QA headcount required to maintain even 10% coverage grows linearly — making comprehensive insight permanently out of reach.
How AI Call Recording Analysis Works
The system automatically processes every call recording through a pipeline that converts audio to structured, actionable data. Here is the complete flow:
Call recordings ingested automatically
Calls from your telephony platform (Exotel, Twilio, RingCentral, call centre dialler) are pushed to the pipeline via webhook or batch upload — no manual download required.
Speech-to-text transcription
An ASR model transcribes each call with speaker diarisation — separating the agent and customer voices — and outputs a timestamped transcript ready for analysis.
LLM-powered structured extraction
A fine-tuned LLM reads each transcript and extracts structured fields: call reason, customer sentiment, product mentioned, issue category, resolution status, and any follow-up required.
Agent performance scoring
Each call is scored against your quality rubric — tone, script adherence, resolution rate, upsell attempt — and flagged for supervisor review if it falls below threshold.
Aggregated dashboards & alerts
Structured data flows into your BI tool or dashboard. Spikes in complaint categories, emerging product issues, or agent performance trends are surfaced in near real time.
Key Capabilities
A production-grade call analysis system extracts far more than a transcript. Here is what the full pipeline delivers:
Speaker diarisation
Accurately separates agent and customer voice tracks even in noisy call centre environments — a prerequisite for meaningful sentiment and QA analysis.
Sentiment & emotion detection
Scores customer sentiment at utterance level — tracking frustration, satisfaction, and escalation risk across the full conversation arc, not just an end-of-call average.
Complaint & issue classification
Automatically tags each call with your issue taxonomy — delivery delay, wrong product, payment failure, return request — enabling trend analysis without manual coding.
Agent quality scoring
Scores every agent call against a configurable QA rubric — empathy, resolution rate, policy adherence, script compliance — and flags calls for coaching.
Product & SKU mention extraction
Links specific products, SKUs, and order IDs mentioned in calls to your catalogue — enabling correlation between product issues and call volume spikes.
CRM & helpdesk sync
Pushes call summaries, sentiment scores, issue tags, and follow-up flags directly into Freshdesk, Zendesk, Salesforce, or your custom CRM via REST API.
What This Means Specifically for E-commerce
E-commerce call analysis has a different profile from B2B or financial services. The value is concentrated in four areas:
| Business Area | What the AI surfaces | Downstream action |
|---|---|---|
| Product quality | Spike in calls mentioning a specific SKU or defect type | Alert buying team; pause reorder; trigger supplier review |
| Logistics | Delivery delay complaints correlated to specific pin codes or 3PL partner | Escalate to logistics ops; switch routing for affected zone |
| CX operations | Calls with repeat contact on same issue (FCR failure) | Coach agent; update resolution script; flag to QA |
| Revenue | Calls where return was avoided by agent de-escalation | Identify top-performing agents; replicate their approach |
Multilingual Coverage for Indian E-commerce
Indian e-commerce call centres routinely handle calls in Hindi, Tamil, Kannada, Telugu, Malayalam, Marathi, Bengali, Gujarati, and Odia — often with significant code-switching between English and a regional language. Standard ASR models trained on English or clean Hindi fail on this data.
Our pipeline is built on models with genuine Indian language coverage: Qwen3-ASR (52 languages), NVIDIA Canary-Qwen 2.5B, and Faster-Whisper fine-tuned on code-switched Indian English. The analysis LLM is fine-tuned to handle mixed-language transcripts without quality degradation.
For global operations, the same stack supports Spanish, German, French, Portuguese, Arabic, and 40+ other languages.
Technology Stack
The full pipeline runs on open-source models deployed on your infrastructure. No call audio or transcript leaves your environment unless you choose to use cloud APIs.
| Layer | Tools | Note |
|---|---|---|
| Speech Recognition (ASR) | Qwen3-ASR, NVIDIA Canary-Qwen 2.5B, Faster-Whisper | Supports Hindi, Tamil, Kannada, Telugu, Bengali, Marathi and 40+ other languages out of the box |
| Speaker Diarisation | pyannote.audio 3.x, NeMo Speaker Diarization | Works on 2-speaker (agent + customer) and multi-party conference calls |
| Analysis LLM | Qwen 3.5, Llama 4 Maverick, Mistral Medium 3.5 | Fine-tuned on your issue taxonomy, QA rubric, and product catalogue |
| Sentiment Model | Fine-tuned RoBERTa / DeBERTa, or LLM-as-judge | Utterance-level scoring with configurable granularity |
| Data Pipeline | Apache Kafka, Celery, Airflow | Handles high-volume batch processing and near-real-time streaming from live calls |
| Storage & BI | PostgreSQL, ClickHouse, Metabase, Superset | Structured call data queryable in your existing BI environment |
| Telephony Connectors | Exotel, Twilio, RingCentral, Ozonetel, NICE CXone | Plug into your existing call centre platform |
What Superteams Builds for You
We design and deliver the complete system — from telephony ingestion to BI dashboard — in 4–8 weeks. A typical engagement covers:
- Call audit & taxonomy workshop — reviewing a sample of your existing calls to define the issue taxonomy, QA rubric, and key data fields to extract
- Pipeline architecture — designing the ingestion, ASR, diarisation, and LLM analysis flow for your call volume and latency requirements
- Model fine-tuning — fine-tuning the analysis LLM on your specific issue categories, product catalogue, and agent script
- Telephony integration — connecting to your existing call centre platform (Exotel, Twilio, RingCentral, or custom SIP)
- Dashboard build — building a real-time dashboard in Metabase, Superset, or pushing structured data to your existing BI tool
- CRM / helpdesk sync — writing call summaries and tags to Freshdesk, Zendesk, Salesforce, or your custom CRM
- Handover & documentation — full system ownership transferred to your team with documentation and training
Ready to build?
Let's analyse every call, not just a sample
Book a 30-minute strategy call. We'll review a sample of your call data, map out the analysis pipeline, and give you a concrete estimate for the build.
Book a strategy call