Our client, a real estate company from UAE approached us with a problem. Despite having experienced sales agents, quality leads and competitive properties, their sales numbers were consistently declining. Management observed something was wrong with how their team handled client calls, but they had no systematic way to identify the issues across hundreds of conversations happening every week.

What we built

We designed and implemented a system that listens to every call of sales agent and customer, and automatically identifies problems and opportunities. Here's how it works:

Call processing pipeline: every recorded conversation of agent and customer gets uploaded to storage immediately after it ends. We use Whisper AI to convert speech to text with high accuracy, even when people talk over each other or audio quality is poor. The Pyannote model figures out who is speaking when, which is crucial for understanding conversation dynamics.

Analysis engine: the system evaluates each conversation for specific patterns that matter to sales performance. It tracks how much time each person talks, counts interruptions and checks if agents are using required phrases from their sales scripts. We also built custom analyzers that look for company-specific requirements - like whether agents mentioned warranty information or asked about financing needs.

Instead of just flagging problems, the system identifies what went right in successful calls. If an agent consistently closes deals when they mention certain features early in the conversation, the system captures that pattern and suggests it to other team members.

Reporting: managers get dashboards showing team performance trends and individual agent KPI metrics which are agreed and set customly. Agents can see their own scores and compare them to top performers. When someone has a particularly bad call - lots of interruptions or missed script points - the system identifies a sales agent and notifies manager.

Results delivered

After six months, the company saw a 10% increase in sales. Specifically in numbers:

  • 25% reduction in client interruptions because agents could see their patterns and learned to listen more
  • 50% improvement in sales script compliance because agents got immediate feedback about missed talking points
  • 50% faster problem resolution since managers could spot issues right away instead of discovering them weeks later during random call reviews

But the real win was changing how they approach training. Instead of generic coaching sessions, managers now have specific data about what each agent needs to work on. Someone who interrupts clients gets different coaching than someone who forgets to mention key product features.

How can it work for other industries

The same approach works for any business that relies on phone conversations with customers:

  • Call centers can use it for quality assurance and compliance monitoring
  • Insurance companies can ensure their claims handlers follow proper procedures
  • Healthcare practices can analyze patient consultations to improve communication and satisfaction

The technology behind

Technical implementation: we built the system using Python's FastAPI framework following the microservices architecture. Apache Kafka handles the message queues between backend services, which is important when processing dozens of records. The entire system runs on AWS for reliability and scalability - recordings go to S3, EC2 instances handle the heavy AI computations, while ECS containers automatically scale based on workload and we use Redshift and PostgreSQL for storing the metrics data which is calculated after transcribing the voice record into a text. AI models in use are OpenAI's Whisper and Pyanno models which offer a distinctive high rate of transcribing audio from many languages (English, Arabic, Spanish, German in our case) and finding patterns.