Smart Call Analysis with AI
Problem: manual call processing is a bottleneck
Imagine you manage a 50-operator call center. Each day, 500 calls come in with an average duration of 4 minutes. To evaluate quality, you need to listen to at least 10% — that's 200 minutes daily. After each call, the manager fills out a report — another 5 minutes. Multiply that by 500 calls, and you get 41 hours per month just on reports. AI does all this in seconds. We have deployed such solutions for over ten projects, processing more than 500,000 calls and reducing analysis time by 70% compared to manual methods. Typical integration cost starts from $5,000, cutting manual analysis costs by 80%. You spend hours listening to recordings to identify issues in scripts or evaluate performance. Manager errors go unnoticed, and customers leave. We automate this process with AI integration.
What tasks do we solve?
- Transcription and sentiment analysis: after each call, we automatically get text, determine emotional tone (positive/negative/neutral), and highlight key topics.
- Smart routing: AI analyzes the history of inquiries and predicts which department can best handle the request — the call is redirected before connection.
- Search across recordings: the entire archive becomes searchable — need to find mentions of a competitor over the past month? A few queries in Elasticsearch.
How we do it: architecture and stack
Post-call analysis (quick start):
Completed call → PBX Webhook → Download recording → STT (Whisper or Vosk) → NLP (spaCy + transformers) → CRM Real-time processing (via SIP trunk):
Call → PBX → SIP trunk → Media Gateway (RTP → PCM) → AI Server (VAD + ASR + NLU) → Action (routing/prompt) For deployment we use:
- Models: OpenAI Whisper (large-v3), custom fine-tuned models based on BERT for classification.
- Infrastructure: vLLM for inference, ONNX Runtime for optimization, pgvector for embedding storage.
- MLOps: MLflow for experiment tracking, Kubeflow for pipelines.
Why post-call analysis is easier to deploy?
Post-call analysis does not require network changes — you only need to configure a Webhook in your PBX control panel. The real-time option is more complex: it requires a dedicated SIP trunk and a GPU server for real-time audio stream processing.
Comparison of options
| Criterion | Post-call analysis | Real-time via SIP |
|---|---|---|
| Deployment time | 1–2 weeks | 1.5–2 months |
| Latency | Minutes | <500 ms |
| Infrastructure requirements | CPU + basic server | GPU + SIP gateway |
| Capabilities | Analytics, search | Routing, operator prompts |
How AI improves call routing?
Real-time call analysis allows routing based on predictive analytics: customer sentiment, call history, intent. AI makes decisions in 400 ms (p99), imperceptible to speakers. For example, if a customer is irritated, the call is directed to the most experienced operator. This increases service level and reduces repeat calls. We implemented real-time scenarios in two projects — clients noted a 15% increase in satisfaction.
Process of work
- Analytics: audit of current PBX, requirements gathering, selection of scenarios (post-call, real-time, or both).
- Design: integration architecture, model selection, security setup.
- Implementation: we write code in Python (FastAPI + Celery for async), integrate with CRM via REST/SOAP.
- Testing: run on historical recordings, A/B test on 10% of calls. Achieve transcription accuracy of 95%+.
- Deploy: containerization (Docker + Kubernetes), monitoring (Prometheus + Grafana).
What is included (deliverables)
- API documentation for integration schemas.
- Access to a test environment during development.
- Training your team on dashboards.
- Code warranty and SLA for error handling within 4 hours.
With over 10 PBX integration projects and 500k+ calls processed, we bring 5 years of experience in NLP and MLOps. Typical integration cost starts from $5,000, cutting manual analysis costs by 80%.
Timelines
- Post-call analysis: from 1 to 2 weeks.
- Real-time integration: from 1.5 to 2 months.
- Full cycle (both scenarios + dashboards): up to 3 months.
Integration cost is calculated individually after auditing your infrastructure.
Typical mistakes when doing it yourself
- Ignoring Webhook ACK: Mango Office retries the request — duplicate recordings. Solution: return HTTP 200 immediately.
- Incorrect request signature: hash mismatch blocks requests. Solution: use HMAC with key-sorted parameters.
- STT model overload: high latency and cost. Solution: enable VAD to avoid processing silence.
We have extensive experience in NLP and MLOps with over ten PBX integration projects. Order an audit of your PBX – it's free and takes 30 minutes. We will prepare a proposal with exact timelines and cost. Contact us for a consultation – we will assess your scenario and offer the optimal solution.