Story

Sales teams often struggle with limited visibility into their calls, reviewing only 5-10% manually, which leads to missed opportunities. We built an AI-powered voice analytics tool that transcribes, indexes, and analyzes 100% of calls, turning them into actionable insights. In one case, this helped a SaaS client grow sales by 120% in 12 months.

What the tool does

We aimed to provide non-intrusive, automated QA at scale. So the key features include: - 100% call transcription: using ASR for accurate, fast transcriptions. - Searchable database: indexed transcripts for easy keyword and phrase tracking. - Customizable reports: automated manager reports, grouped by agent or team. - CRM integration: syncs data to tools like Salesforce and Zoho.

Limitations: currently lacks real-time alerts, sentiment analysis, and emotion scoring (planned for future updates).

Architecture overview - Audio capture: integrated VoIP or manual uploads. - ASR pipeline: transcribes calls via cloud-based speech-to-text. - Transcript indexing: elasticSearch stores and retrieves data efficiently. - Keyword matching: flags important terms like pricing or CTAs. - Reports: automated generation of weekly summaries.

Real-world impact. One SaaS client improved - 120% sales growth over 12 months. - 35% increase in close rate by identifying high-performing patterns. - 5-day reduction in sales cycle due to consistent messaging. - Churn dropped from 15% to 6% through better objection handling.

This was achieved without expanding the team — simply by leveraging the power of data.

Challenges & lessons learned - Keyword rules: over-flagging terms led to alert fatigue, so we customized per-client keyword sets. - ASR model issues: addressed by adding pre-filtering for noisy inputs and fallback models. - CRM integration: built middleware to adapt to varying CRM structures across clients. - Manager overload: simplified reports to highlight top deviations, avoiding information overload.

Next steps: what's coming

- Trend detection: analyzing keyword frequency over time. - Conversation templates: auto-tagging calls (intro, demo, pricing). - Call quality scoring: identifying poor audio or incomplete conversations.

Key takeaways - Focus on basics: transcription + search + simple flags bring massive value. - Human-in-the-loop: insights are most useful when actionable in real-time. - Scalability = simplicity: focused, simple solutions deliver better results. - Data ≠ insight: reports need to be curated and actionable for managers.

Conclusion AI is a powerful tool for sales teams, but success comes from turning raw data into actionable insights. By building scalable systems and avoiding complexity, we were able to achieve real business growth — and this approach is adaptable across industries.