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Production AI Engineering
Project 01

Generative AI for Quality Automation

Generative AI for Quality Automation — portrait visual

Replacing manual vendor reviews at enterprise scale with LLM-powered evaluation infrastructure.

The Challenge

Replacing an Entire Vendor Workforce with LLMs

Adityagen.ai 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. Adityagen.ai 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."

Impact Metrics
$3.7M
Initial verified economic impact
~$30M
Scalable potential globally
32 × 76
Products × workflows in production
Engagement
Company Google
Year 2023–2024
Role Tech Lead Architect
Context Metrics
~20
Quality attributes evaluated
~150
NLP patterns across product and policy signals
Multi
Language and session coverage
System Architecture

Five-Layer LLM Evaluation Infrastructure

GenAI QA Architecture Overview
GenAI QA Architecture Overview
Five-Layer Architecture
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.
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.
Flume-based ingestion pipelines with partitioning, windowing, and parallel processing. High-throughput batch-ready architecture for enterprise-scale evaluation volumes.
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.
A/B testing framework vs. manual reviews. Model size experimentation, quantization, and caching strategies. Synthetic data augmentation for edge-case coverage.
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

GenAI QA Impact
Evaluation & Impact — Economic outcomes and platform scalability

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.

  • 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
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
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