IOanyT Innovations
SaaS Featured

IOanyT (Own Product) (AWS Marketplace)

TheHeartbeat.ai: From Concept to AWS Marketplace in Record Time

How we built a production AI/ML SaaS platform that analyzes 100% of contact center calls in real-time—proving our ability to take AI products from 0→1.

28%
Quality Improvement
15%
Satisfaction Increase
100%
Call Coverage
60%
QA Time Reduction

Quick Facts

Client: IOanyT (Own Product)
Industry: SaaS
Location: AWS Marketplace
Technologies:
AWS SageMaker Lambda S3 RDS React Python NLP FastAPI

The Visibility Gap: 95% of Customer Calls Go Unanalyzed

Contact centers are drowning in customer interactions—but analyzing them manually is impossible.

The Problem

Traditional contact centers manually sample only 2-5% of calls for quality assurance. This creates a massive visibility gap:

  • Blind Spots: 95%+ of customer interactions are never reviewed
  • Inconsistent Quality: No way to monitor all agent performance systematically
  • Lost Insights: Customer sentiment, feedback, and pain points buried in unanalyzed calls
  • Manual Bottleneck: QA teams overwhelmed—45 minutes to review a single call
  • Delayed Feedback: Agents receive coaching weeks after the interaction occurred

Real Impact

A 300-agent contact center handling 50 calls per agent per day generates 15,000 calls daily. At 2-3% sampling, they review only 300-450 calls—leaving 14,500+ interactions invisible.

TheHeartbeat.ai: 100% Call Coverage with AI-Powered Analytics

We built TheHeartbeat.ai as a production-ready AI/ML SaaS platform that analyzes every single call in real-time—transforming contact center operations through comprehensive AI-driven insights.

The Breakthrough: 100% Automated Analysis

Not samples. Not manual reviews. Every call—transcribed, analyzed, scored, and reported automatically. From 2-5% visibility to 100% visibility.

Speech-to-Text Processing

AWS Transcribe + Custom Models

  • 45+ languages supported
  • Speaker diarization (agent/customer separation)
  • Real-time and batch processing

AI-Powered Analytics

NLP, Custom PyTorch Models

  • Sentiment & emotion detection
  • Automated quality scoring
  • Topic & entity extraction

Real-Time Alerts & Dashboards

React, D3.js, WebSockets

  • Critical issue flagging
  • Agent performance leaderboards
  • Coaching opportunity identification

Seamless Integrations

RESTful API, Cloud Connectors

  • Google Drive, Dropbox, Box
  • GCP compatibility
  • RESTful API for custom integrations

Production-Grade AWS Architecture

TheHeartbeat.ai is built on AWS cloud infrastructure designed for scale, security, and reliability—ready for enterprise deployments.

Data Layer

AWS S3, RDS (PostgreSQL)

  • • Audio storage with lifecycle policies
  • • Metadata storage for structured data
  • • Encryption at rest (AES-256)
  • • VPC isolation for security

Scale: Millions of call minutes stored, queryable in milliseconds

AI/ML Processing Pipeline

SageMaker, Transcribe, Lambda

  • • Fine-tuned BERT models for sentiment
  • • Custom PyTorch emotion detection
  • • SageMaker endpoints for real-time inference
  • • Auto-scaling based on processing load

Scale: Process thousands of calls simultaneously

Application Layer

React, FastAPI, Redis

  • • React 18 with TypeScript frontend
  • • FastAPI high-performance backend
  • • Redis caching for fast responses
  • • Multi-tenant architecture

Scale: Thousands of concurrent users, sub-second responses

DevOps & Monitoring

CloudWatch, CodePipeline

  • • GitHub Actions CI/CD
  • • Multi-stage deployments
  • • HIPAA-ready infrastructure
  • • 99.9% uptime SLA

Scale: Zero-downtime deployments

Measurable Outcomes: 300-Agent Contact Center

We deployed TheHeartbeat.ai at a hospitality client's 300-agent contact center. The results were immediate and quantifiable.

Before TheHeartbeat

  • Manual QA sampling: 2-3% of calls
  • Average QA review time: 45 minutes/call
  • Inconsistent quality scoring (subjective)
  • Delayed feedback to agents: 1-2 weeks
  • High QA team workload and burnout

After TheHeartbeat

  • 100% call coverage: Every call analyzed
  • Real-time insights: Scores in minutes
  • 28% improvement in agent quality scores
  • 15% increase in customer satisfaction
  • 60% reduction in QA team hours

ROI Analysis

Cost Savings

  • • QA Team: Reduced from 8 FTE to 3 FTE
  • • Time to Insight: Weeks → Minutes
  • • Coverage: 2-3% → 100% (50x increase)

Revenue Impact

  • • 15% satisfaction increase → higher retention
  • • 28% quality improvement → better resolutions
  • • Data-driven coaching → faster ramp-up

Estimated Annual Value: $500K+ in cost savings and revenue improvement

Proof of End-to-End AI Product Development Capability

TheHeartbeat.ai isn't just a project—it's proof that IOanyT can build AI/ML products from concept to production to marketplace success.

1

0→1 Product Development

Took an idea to production-ready SaaS platform. Full ownership from vision to execution.

2

Production ML at Scale

Processing millions of call minutes in production. Real customer workloads, not demos or POCs.

3

AWS Marketplace Success

Listed on AWS Marketplace as production-ready SaaS. Enterprise procurement ready.

4

Measurable Business Outcomes

28% quality improvement, 15% satisfaction increase. Real customers, real results.

AWS Marketplace Listed Millions of Calls Processed 99.9% Uptime SLA HIPAA-Ready Infrastructure

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