For enterprise testing teams, accessing fast and accurate insights from technical documents is critical. Doppelio is a leading provider of testing and test automation solutions that help enterprises validate and optimize their digital experiences. As part of their AI expansion strategy, Doppelio aimed to develop a scalable conversational chat agent that integrates seamlessly into enterprise workflows.

But Doppelio’s original OpenAI-based solution couldn’t consistently process domain-specific data or scale under heavy usage. Performance degraded, users waited, and insights stalled. To fix this, Doppelio partnered with GoML to develop a scalable, multi-tenant AI chat agent for enterprises, powered by Claude 3.5 via AWS Bedrock.

The problem: inconsistent responses, manual processing, and scaling issues

Doppelio’s early AI deployment struggled to meet the needs of enterprise-scale automation. The OpenAI-based solution delivered inconsistent responses, especially with domain-specific documents. Teams were forced to manually extract and analyze information from PDFs, Word files, and structured test data, slowing decision-making and introducing errors.

As adoption grew, so did the pressure. The system couldn't reliably support concurrent sessions across multiple enterprise users. Performance degraded under peak loads, and security became a concern with increasing data volume. Doppelio needed a reliable, secure, and context-aware AI chat agent for enterprises, one that could scale intelligently while maintaining data segregation, speed, and precision.

The solution: a high-performance enterprise AI chat agent

GoML re-architected Doppelio’s solution with AWS-native services and Claude 3.5, creating a powerful AI chat agent tailored for enterprise testing and automation teams. It supports document intelligence, real-time multi-modal input, secure multi-tenant access, and enterprise-grade scaling.

OpenAI to AWS Bedrock migration

GoML replaced Doppelio’s OpenAI setup with Claude 3.5 on AWS Bedrock, improving performance, latency, and domain-specific accuracy, while aligning with enterprise cloud strategy.

Multi-modal Interface

Supports both text and voice-based queries, enabling flexible user interactions with real-time feedback and high comprehension accuracy.

Scalable Infrastructure with FastAPI

Built with FastAPI and Python, the agent handles over 1,000 concurrent sessions without latency spikes.

Document intelligence

Understands and extracts structured data from complex documents (PDFs, Word, images) to support test automation and analysis workflows.

Enhanced Retrieval-Augmented Generation (RAG)

Optimized RAG pipelines tuned specifically for Claude 3.5 deliver contextual answers drawn from enterprise documentation.

Multi-tenant secure architecture

Utilizes AWS S3, DynamoDB, Lambda, and API Gateway with strict access controls and encryption to protect user data and ensure tenant-level separation.

Outcomes: measurable enterprise-grade improvements

• 67% improvement in information extraction accuracy across structured and unstructured documents

• 99.95% uptime, even during peak concurrent sessions

• 50% increase in domain-specific response accuracy using Claude 3.5

• Better cost optimization and latency after migrating from OpenAI to AWS Bedrock

Lessons for platform teams and AI integrators

Common pitfalls to avoid

• Assuming general-purpose AI can handle domain-specific workloads

• Overlooking performance degradation under concurrency

• Ignoring tenant-level data segregation in SaaS AI platforms

Tips for building enterprise AI chat agents

• Use Claude 3.5 or similar models tuned for industry documents

• Optimize retrieval pipelines with enhanced RAG

• Invest in secure, multi-tenant architecture early on

• Monitor usage patterns and auto-scale with CloudWatch + Lambda triggers

Ready to deploy a secure, scalable AI chat agent for your enterprise?

Let GoML help you build an AI chat agent for enterprises, capable of real-time document processing, secure team collaboration, and intelligent, domain-aware responses at scale.

Reach out to build your own Gen AI-powered conversational assistant.