Intelligent Customer Interaction Analysis Platform

Production-Grade GenAI System at Enterprise Scale

Industry: Telecommunications / Cable Services
Company Profile: Multi-billion dollar provider, 3.8M+ subscribers, nationwide footprint
Project Duration: POC validated in 4 weeks, production deployment in 8 weeks
My Role: Principal Architect & Technical Lead
Status: Phases 1-2 in production; Phase 3 (fine-tuning) initiated at departure

EXECUTIVE SUMMARY

As Principal Architect, I designed and deployed a production-grade GenAI platform processing 2M+ monthly call transcripts (85-90K daily) with 85% classification accuracy, delivering $1.2M annual retention value through automated intent detection and unsupervised pattern discovery.

The platform features serverless, zero-touch architecture using Cloud Run, Cloud Scheduler, and Vertex AI Pipelines, with fully automated ingestion from third-party Verint platform (AWS S3) to GCP processing. The system surfaced top 10 systemic customer issues driving churn and was adopted by 3 business organizations (Customer Experience, Data Science, Product) for proactive retention, predictive modeling, and strategic decision-making.

BUSINESS PROBLEM

Context

Customer service was handling over 2 million calls per month (85-90K daily), but there was no systematic way to turn those conversations into actionable insights. The company was missing early signals around:

Disconnect intent - High-risk customers not identified until too late
Competitive threats - Competitor mentions and comparison shopping
Recurring product issues - Equipment failures, service quality problems
Billing disputes - Rate changes, promotional pricing confusion

These factors were directly driving customer churn, but existing approaches provided no reliable extraction mechanism at scale.

Pain Points

No Systematic Insight Extraction:

Manual review limited to <5% of calls due to resource constraints
48 hardcoded keyword searches (e.g., “disconnect”, “bill went up”) missed nuanced language
2-week lag between call occurrence and actionable analysis
Inter-rater reliability of only 75% for manual categorization

Business Risk:

Reactive retention strategies instead of proactive intervention
Unable to identify emerging trends (device compatibility, policy confusion)
Missing revenue protection opportunities
No visibility into “unknown unknowns”

Technical Limitations:

Hardcoded rules requiring engineering changes for each new pattern
No confidence scores or explainability for decision-making
Processing time bottlenecks (20+ seconds per batch)
Not scalable or auditable for multi-team adoption

Strategic Requirements

The business needed a production-grade GenAI system that could:

Automatically classify transcripts at scale with measurable, auditable accuracy
Surface unknown patterns that manual review would miss
Remain cost-efficient at 2M+ monthly volume
Be fully automated and scalable for adoption across multiple business teams
Provide confidence scores to enable trust and decision-making

SOLUTION ARCHITECTURE

Architectural Approach

I designed a serverless, zero-touch architecture that processes transcripts end-to-end without manual intervention:

graph TD
    A[Verint S3 AWS] --> B[Cloud Scheduler]
    B --> C[Cloud Run]
    C --> D[GCS Staging Bucket<br/>85-90K daily recordings]
    D --> E[Vertex AI Pipelines<br/>Orchestration]
    E --> F[Gemini 2.5 Flash<br/>Transcription]
    F --> G[Cloud DLP<br/>PII Redaction]
    G --> H[PostgreSQL + PGVector<br/>Embeddings Storage]
    H --> I[Gemini 2.0 Flash<br/>Intent Classification]
    I --> J[BigQuery<br/>Analytics Warehouse]
    J --> K[Business Teams<br/>CX, Data Science, Product]

Key Design Principles:

Fully automated: Zero-touch operation from ingestion to classification
Scalable: Handles 2M+ monthly transcripts with multi-region processing
Auditable: Confidence scores and structured outputs for trust
Cost-efficient: Serverless components scale to zero when idle
Compliant: Automated PII redaction with Cloud DLP (18 types)

Phase 1: Zero-Shot Intent Classification

Problem: Need rapid time-to-value without labeled training data

Solution: Zero-shot LLM classifier with 24 multi-label intent categories

Implementation:

I designed a zero-shot prompt with 24 intent categories covering:

Disconnect intent, competitor mentions, billing disputes
Equipment issues, service quality, appointment scheduling
Rate events, promotional pricing, policy confusion
“Unknown” category for explicit handling of ambiguous cases

The model produces structured JSON output with:

{
  "primary_intent": "billing_dispute",
  "confidence": 0.87,
  "all_intents": [
    {
      "category": "billing_dispute",
      "confidence": 0.87,
      "evidence": "Customer states 'bill went up without warning'"
    },
    {
      "category": "rate_event",
      "confidence": 0.76,
      "evidence": "Discussion of promotional pricing ending"
    }
  ],
  "sentiment": "frustrated",
  "urgency": "high"
}

Technical Decisions:

Why Gemini 2.5 Flash for transcription?

Native audio transcription (eliminate separate Speech-to-Text step)
Superior handling of technical telecom terminology
Cost-efficient at scale ($0.0001 per call)
GCP-native integration (same VPC, low latency)

Why zero-shot first?

Faster time-to-value (6 weeks vs 2+ months for fine-tuning)
No labeled training data available initially
Flexibility to iterate on prompt engineering
Generated high-confidence labels for future fine-tuning dataset

Results:

6 weeks to production - Rapid deployment via serverless architecture
84-85% classification accuracy - Sufficient for production adoption
Multi-label capability - Captures nuanced conversations (e.g., billing + disconnect)
Structured outputs - Easy consumption by downstream systems

Phase 2: Unsupervised Pattern Discovery

Problem: What customer issues are we missing? What patterns aren’t visible through predefined categories?

Solution: Unsupervised clustering to let the data reveal emerging themes

Implementation:

I applied unsupervised discovery techniques to surface patterns the business didn’t know existed:

graph TD
    A[Step 1: Candidate Selection] --> A1[Low confidence calls < 0.7]
    A --> A2[Unknown category assignments]
    A --> A3[High-variance embeddings]
    A1 --> B[Step 2: Dimensionality Reduction UMAP]
    A2 --> B
    A3 --> B
    B --> B1[768D embeddings → 5D representation<br/>Preserves local + global structure]
    B1 --> C[Step 3: Density-Based Clustering HDBSCAN]
    C --> C1[Identifies clusters of varying density<br/>min_cluster_size=30, handles noise]
    C1 --> D[Step 4: Theme Extraction Gemini]
    D --> D1[Sample 15 transcripts per cluster<br/>LLM analysis produces:<br/>theme_name, description, key_phrases<br/>business_impact, recommended_action]
    D1 --> E[Step 5: Intent Overlap Analysis]
    E --> E1[Compare with 24 existing categories]
    E1 --> F1[New pattern<br/>add to taxonomy]
    E1 --> F2[Merge with existing<br/>>70% overlap]
    E1 --> F3[Needs human review<br/>ambiguous]

Results:

12 previously unknown intents discovered, including:
- Equipment swap frustration and delays
- Service transfer delays between addresses
- Smart home device compatibility issues
- International calling plan confusion
- Self-installation difficulties
- [7 others with significant volume]
$1.2M annual retention value through early detection:
- Proactive outreach to high-risk customers
- Product/policy improvements based on discovered issues
- Prevented churn through targeted interventions
Top 10 systemic issues surfaced that were previously invisible to the business

Phase 3: Fine-Tuning for Enhanced Accuracy (Initiated)

Status: Design completed and development initiated; in progress at departure

Objective: Push classification accuracy from 85% to ~95% through domain-specific fine-tuning

Planned Approach:

Training data generation: Leverage high-confidence Phase 1 labels (confidence > 0.90) as training examples
LoRA fine-tuning: Parameter-efficient fine-tuning of Gemini on domain-specific data
Hybrid cascade pattern: Simple keyword filters → fine-tuned model → zero-shot fallback
A/B testing framework: Gradual rollout with statistical validation

Expected Benefits:

Enhanced accuracy from 85% to 95%+
Reduced inference latency and cost per call
Better handling of domain-specific terminology
Improved confidence calibration

Framework Reusability: The architecture was designed to be reusable beyond call transcripts—applicable to chat logs, email, survey responses, and other unstructured customer feedback channels.

EVALUATION FRAMEWORK & OBSERVABILITY

Determining 85% Accuracy: Rigorous Evaluation Approach

To ensure the system was production-ready and trustworthy for business decision-making, I established a comprehensive evaluation framework:

1. Human-Labeled Test Set (Ground Truth)

500 transcripts manually labeled by domain experts (Customer Experience team)
Stratified sampling across intent categories and time periods
Inter-rater reliability validation (2-3 labelers per transcript, Cohen’s kappa > 0.80)
Quarterly refresh to capture evolving customer language

2. Multi-Metric Evaluation

Beyond overall accuracy, I tracked:

Precision & Recall per intent category - Identify weak categories
F1-Score - Balance between precision and recall
Confusion Matrix - Understand misclassification patterns
Confidence Calibration - Ensure confidence scores reflect true accuracy

3. Weekly Automated Evaluation

graph LR
    A[Every Monday 6 AM] --> B[Run inference on<br/>500-transcript test set]
    B --> C[Compute metrics vs<br/>ground truth labels]
    C --> D[Statistical significance tests<br/>z-test for proportions]
    D --> E{Accuracy drop<br/>>2%?}
    E -->|Yes<br/>p < 0.05| F[Send Alert]
    E -->|No| G[Dashboard update<br/>for stakeholder visibility]
    F --> G

4. Human-in-the-Loop Validation

For ongoing quality assurance:

Random sampling of 50 transcripts/week for manual review
Feedback loop: corrections fed back into evaluation set
Edge case identification for prompt refinement

Results:

Phase 1 accuracy: 84-85% across 24 intent categories
Precision: 82-88% (category-dependent)
Recall: 80-87% (category-dependent)
Confidence calibration: 0.85-0.90 confidence → 84-88% true accuracy

Monitoring & Observability

Real-Time Monitoring (Cloud Monitoring + Custom Dashboards)

System Health:

Processing throughput (calls/hour)
Latency (p50, p95, p99)
Error rates and failure modes
Cost per transcript

Model Performance:

Confidence score distribution (daily)
Intent category distribution (detect drift)
“Unknown” category rate (spike = potential drift)
Low-confidence call rate (<0.7 threshold)

Drift Detection

Implemented automated drift detection:

Data drift: Embedding distribution shift using KL divergence
Concept drift: Weekly accuracy on hold-out test set
Alert thresholds: >2% accuracy drop or >15% distribution shift

Result: Detected drift 2 weeks before user complaints during a major product launch that changed customer language patterns

Business Impact Dashboard

Created executive dashboard for non-technical stakeholders:

Top 10 customer issues (weekly trends)
High-risk customer counts (disconnect intent)
Competitor mention frequency and context
ROI tracking (retention value vs. platform cost)

MULTI-CLOUD INTEGRATION ARCHITECTURE

Serverless, Zero-Touch Data Pipeline

Challenge: 85-90K daily call recordings stored in third-party Verint platform (AWS S3), requiring processing in GCP Vertex AI

Solution: Fully automated, serverless orchestration with zero manual intervention

Architecture:

graph TD
    A[Verint Platform<br/>AWS S3] --> B[Cloud Scheduler<br/>Daily trigger, 6 AM]
    B --> C[Cloud Run<br/>Transfer Service]
    C --> C1[List new recordings from S3]
    C1 --> C2[Transfer to GCS<br/>85-90K files/day]
    C2 --> C3[Publish Pub/Sub message]
    C3 --> D[GCS Staging Bucket]
    D --> D1[Raw audio files<br/>Versioned by date partition]
    D1 --> E[Vertex AI Pipelines<br/>Kubeflow orchestration]
    E --> F1[Gemini 2.5 Flash<br/>Transcription]
    E --> F2[Cloud DLP<br/>PII Redaction 18 types]
    E --> F3[Vertex Embeddings<br/>768D vectors]
    E --> F4[Gemini 2.0 Flash<br/>Intent Classification]
    F1 --> G[Storage Layer]
    F2 --> G
    F3 --> G
    F4 --> G
    G --> H1[PostgreSQL + PGVector<br/>Vector search]
    G --> H2[BigQuery<br/>Analytics warehouse<br/>70+ fields]
    H1 --> I[Business Consumers]
    H2 --> I
    I --> J1[Customer Experience<br/>Retention campaigns]
    I --> J2[Data Science<br/>Predictive features]
    I --> J3[Product<br/>Strategic insights]

Key Design Decisions:

Serverless Components:

Cloud Run: Scales to zero, handles transfer bursts efficiently
Cloud Scheduler: Reliable daily triggering without manual intervention
Vertex AI Pipelines: Managed orchestration, no K8s management overhead

Multi-Region Processing:

Distributed across 7 GCP regions for parallelism
Handles 85-90K transcripts daily with <4 hour SLA
Automatic failover and retry logic

Cost Optimization:

Serverless → Pay only for actual processing time
Gemini 2.5 Flash → 50% cheaper than alternatives
Batch processing → Reduces per-call cost

Results:

Zero-touch operation: Fully automated from ingestion to classification
POC validation: 4 weeks to prove technical feasibility
Production deployment: 8 weeks to full-scale operation
Daily throughput: 85-90K recordings processed reliably
Latency: <4 hours from recording to classification

TECHNOLOGY STACK

Data Processing:

Vertex AI Pipelines (Kubeflow): Orchestration
Dataflow (Apache Beam): ETL/streaming
Pub/Sub: Event ingestion
Cloud Composer (Airflow): Scheduling

AI/ML:

Gemini 2.5 Flash: Audio transcription
Gemini 2.0 Flash: Classification & theme extraction
Vertex AI Embeddings: text-embedding-005 (768D)
Vertex AI Tuning: LoRA fine-tuning
Cloud DLP: PII redaction (18 types)

Storage:

GCS: Audio files, batch I/O, training artifacts
PostgreSQL 14 + pgvector: Vector search (HNSW index)
BigQuery: Analytics warehouse (70+ fields)

ML Libraries:

scikit-learn: Baseline classifier
HDBSCAN: Density clustering
UMAP: Dimensionality reduction

BUSINESS IMPACT

Multi-Organization Adoption

The platform was fully adopted by 3 business organizations, each leveraging the insights for different strategic purposes:

1. Customer Experience Teams

Proactive retention campaigns: Target high-risk customers (disconnect intent) with retention offers
Agent training: Identify common customer pain points for coaching
Quality monitoring: Track sentiment trends and escalation patterns
Result: Prevented customer churn through early intervention

2. Data Science Teams

Pre-built features: Used intent classifications as features in predictive models
Churn prediction: Improved model accuracy by 12% with intent features
Customer segmentation: Enhanced clustering with interaction insights
Result: Faster model development and improved predictive performance

3. Product Teams

Feature prioritization: Data-driven roadmap decisions based on customer pain points
Market intelligence: Competitor mentions and product comparison insights
Policy improvements: Identified confusing policies requiring clarification
Result: Strategic product decisions backed by customer voice data

Quantitative Results

Scale & Performance:

2M+ monthly transcripts processed (85-90K daily)
85% classification accuracy - Production-ready and trustworthy
<4 hour latency - From recording to classification
100% coverage - Every call analyzed vs. previous 5% manual sample
6 weeks to production - Rapid deployment via zero-shot approach

Revenue Protection:

$1.2M annual retention value through early issue detection and proactive intervention
Top 10 systemic issues surfaced that were previously invisible
12 previously unknown intents discovered via unsupervised clustering
Early detection advantage: 2+ weeks faster than manual review backlog

Operational Efficiency:

Zero-touch automation - No manual intervention required
Serverless architecture - Scales automatically, no infrastructure management
Multi-cloud integration - Seamless AWS (Verint) to GCP processing
Cost-efficient - Pay-per-use model with optimized per-call cost

KEY TECHNICAL CHALLENGES

Challenge 1: Handling 18 PII Types at Scale

Problem: Cloud DLP API 600 requests/minute limit with 100K transcripts

Solution:

Async batch processing: 500 transcripts per batch
Thread pool executor with rate limiting
Exponential backoff with jitter
Regional distribution across 7 regions

Result: 100K transcripts in 45 minutes (vs 3 hours sequential)

Challenge 2: Vector Search Performance

Problem: 100K+ vectors, need <100ms query time

Solution:

pgvector with HNSW index
Table partitioning by date and intent category
Pre-filter on metadata (date, intent) before vector search

Result: 12ms average query time (95th percentile: 45ms)

Challenge 3: Model Drift Detection

Problem: Customer language evolves, model performance degrades

Solution:

Hold-out test set: 500 human-labeled examples
Weekly auto-evaluation
Statistical tests with alert thresholds

Result: Detected drift 2 weeks before user complaints

LESSONS LEARNED

What Worked Well

Hybrid Cascade Pattern - Don’t over-engineer, use simplest solution first
Human-in-the-Loop - ML systems need continuous feedback
A/B Testing - Measure everything, deploy incrementally
Multi-Region Parallelism - Design for cloud constraints upfront

What We’d Do Differently

Start with Smaller Scope - Ship Phase 1 faster, iterate based on feedback
Monitoring Earlier - Observability from Day 1, not added later
Simpler Fine-Tuning - 1,000 examples vs 6,500 initial dataset
Versioned Taxonomy - Schema changes broke BigQuery 3x

KEY METRICS

Performance Dashboard

Metric                      Achievement         Status
──────────────────────────────────────────────────────
Monthly Volume              2M+ transcripts     ✓ Production
Weekly Processing           500K+ transcripts   ✓ Production
Accuracy (Phase 1-2)        85%                 ✓ Target Met
Time to Production          6 weeks (Phase 1)   ✓ Fast Deploy
POC Validation              4 weeks             ✓ Rapid Proof
Production Deployment       8 weeks             ✓ On Schedule
Retention Value             $1.2M annually      ✓ ROI Achieved
New Patterns Discovered     12 issues           ✓ High Impact
Coverage                    100% of calls       ✓ Full Scale

CONCLUSION

This project demonstrates production-grade GenAI platform implementation at enterprise scale—processing 2M+ monthly transcripts with 85% accuracy and delivering $1.2M annual retention value through automated intent classification and unsupervised pattern discovery.

Key Achievements:

Serverless, Zero-Touch Architecture: Fully automated pipeline from AWS (Verint) to GCP with no manual intervention
Rapid Time-to-Value: 6 weeks from zero-shot POC to production (Phase 1)
Unsupervised Discovery: Surfaced 12 previously unknown customer issues and top 10 systemic churn drivers
Multi-Organization Adoption: Insights used by Customer Experience, Data Science, and Product teams
Rigorous Evaluation Framework: 85% accuracy validated through 500-transcript test set with weekly monitoring
Production Scale & Reliability: 85-90K daily transcripts, <4 hour latency, multi-region processing

Business Outcomes:

$1.2M annual retention value from early issue detection and proactive intervention
Cost-efficient at scale: Serverless architecture with optimized per-call cost
Reusable framework: Designed for extension beyond call transcripts (chat, email, surveys)
Strategic insights: Enabled data-driven decisions across multiple business functions

Status: Phases 1-2 completed and in production serving 3 business organizations. Phase 3 (fine-tuning to 95% accuracy) design completed and development initiated at departure.

Technical Leadership: This project showcases my approach to building production-grade AI systems: focus on measurable business outcomes, rigorous evaluation and monitoring, serverless scalability, and cross-functional adoption through trust and explainability.

For detailed technical implementation, architecture decisions, and code patterns, please contact me directly.

Vishal Sharma

Production-Grade GenAI System at Enterprise Scale

EXECUTIVE SUMMARY

BUSINESS PROBLEM

Context

Pain Points

Strategic Requirements

SOLUTION ARCHITECTURE

Architectural Approach

Phase 1: Zero-Shot Intent Classification

Phase 2: Unsupervised Pattern Discovery

Phase 3: Fine-Tuning for Enhanced Accuracy (Initiated)

EVALUATION FRAMEWORK & OBSERVABILITY

Determining 85% Accuracy: Rigorous Evaluation Approach

Monitoring & Observability

Business Impact Dashboard

MULTI-CLOUD INTEGRATION ARCHITECTURE

Serverless, Zero-Touch Data Pipeline

TECHNOLOGY STACK

BUSINESS IMPACT

Multi-Organization Adoption

Quantitative Results

KEY TECHNICAL CHALLENGES

Challenge 1: Handling 18 PII Types at Scale

Challenge 2: Vector Search Performance

Challenge 3: Model Drift Detection

LESSONS LEARNED

What Worked Well

What We’d Do Differently

KEY METRICS

Performance Dashboard

CONCLUSION