Generative AI for Quality Automation

The Challenge

Replacing an Entire Vendor Workforce with LLMs

Vikram replaced an entire vendor review workforce with an LLM-powered QA system — delivering $3.7M in verified savings and ~$30M scalable potential across 32 products in production.

For most enterprises, manually reviewing millions of customer service conversations costs tens of millions annually — and delivers inconsistent, slow, unscalable results. Vikram was tasked with replacing this entire workflow with an LLM-powered system that could match and exceed human reviewer accuracy at a fraction of the cost.

The system needed to evaluate ~20 quality attributes across ~150 NLP patterns spanning multi-channel, multi-session interactions — while dynamically adapting to country-specific policies and product troubleshooting guidelines across 32 products and 76 workflows in multiple languages simultaneously.

"The challenge wasn't building an LLM system — it was building one reliable enough to replace an entire vendor workforce at enterprise scale."

System Architecture

Five-Layer LLM Evaluation Infrastructure

Layer 1

Advanced Prompt Engineering

Few-shot learning for nuanced quality reasoning. Chain-of-Thought prompting for explainability. Dynamic prompt generation — context-aware by country, product, and issue type. Version-controlled prompt library for experimentation and governance.

Layer 2

Retrieval-Augmented Generation (RAG)

Knowledge ingestion from internal KBs, product documentation, troubleshooting guides. Country guidelines, agent training material, and high-quality historical transcripts. Context injection into prompts for grounded, policy-compliant reasoning.

Layer 3

Data Infrastructure

Flume-based ingestion pipelines with partitioning, windowing, and parallel processing. High-throughput batch-ready architecture for enterprise-scale evaluation volumes.

Layer 4

LLM Serving & Inference

Custom serving infrastructure on internal systems. Asynchronous batching, token bucket rate limiting, semaphore-based concurrency control. API throttling safeguards with load balancer and Vertex AI saturation mitigation.

Layer 5

Experimentation & Optimization

A/B testing framework vs. manual reviews. Model size experimentation, quantization, and caching strategies. Synthetic data augmentation for edge-case coverage.

Key Innovations

First production-grade LLM evaluation system for nuanced human conversation QA at this scale
Dynamic prompt generation handling 32 products × 76 workflows × multiple languages simultaneously
Cost-optimized inference via batching, caching, and quantization — dramatically reducing per-evaluation cost
Reliability-first design preventing API saturation under high batch throughput

Outcomes & Impact

From Vendor Dependency to Platform Capability

$3.7M → $30M Verified initial impact · Scalable potential

The initial verified economic impact reached $3.7M, with a scalable potential of ~$30M as the platform expands globally. The system eliminated dependency on manual vendor reviews entirely, enabling productization of LLM-based QA at enterprise scale. It established a foundational capability for global rollout while improving both API reliability and cost efficiency under high-volume production conditions.

My Role

Architecture, Execution, and Team Leadership

Led full architecture design and production rollout end-to-end
Guided L4 engineer on design and implementation of the pre-processor system and rules functionality
Drove experimentation strategy and owned the optimization roadmap across all model and infrastructure experiments

Explainer Video

Project Overview

Tech Stack

Tools & Technologies

LLMs / AI

Gemini RAG Chain-of-Thought Prompting Few-shot Learning Synthetic Data Generation

Data

Flume Async Batch Processing

Infra

Vertex AI APIs Custom LLM Serving Infrastructure Token Bucket Rate Limiting Semaphore Concurrency Control

Relevant Capabilities

Expertise Demonstrated