Case Study

Foundation AI

Bedrock Migration for Scalable Document Intelligence

Industry Legal Tech

Basis Services: AI Migration to Bedrock | AI Strategy, Assessment, & Roadmap | Cloud Migration & Modernization

Problem

Foundation AI processes over 90 million document pages monthly across legal, insurance, and logistics workflows. As adoption of its OpenAI-powered LLMs rapidly increased, the company needed a more scalable and cost-predictable AI foundation that could support long-term growth without compromising performance or security.

As part of AWS’s AI Roadmap (AIR) Program, Foundation AI partnered with Basis to improve the scalability and cost efficiency of its AI infrastructure.

Challenges

Rapid growth in LLM-powered document processing
Rising inference and operational costs
Heavy reliance on OpenAI- based workflows
High-volume document processing workloads
Long-term platform scalability concerns

Needs

Scalable AI infrastructure
More cost-predictable architecture
Greater model flexibility
Improved throughput and latency
AWS-native AI modernization

The Solution

Basis designed and implemented Foundation AI’s infrastructure migration from OpenAI to Amazon Bedrock, focusing on scalability, flexibility, and cost reduction. The new AWS-native architecture improved throughput, reduced operational costs, and established a more scalable foundation for long-term document intelligence growth.

Key Solution Components

Primary inference engine for high-volume document classification and extraction workflows with lower latency and reduced operational costs.

Fine-tuning of models using Foundation AI’s proprietary document corpus to improve extraction accuracy and reduce prompt complexity.

Centralized storage for raw document uploads, processed outputs, and training artifacts used across AI workflows.

Event-driven orchestration for triggering inference jobs, processing workflows, and forwarding outputs to downstream systems.

Real-time monitoring of token usage, latency, and model performance across production AI workloads.

This architecture consolidated Foundation AI’s AI workloads into a scalable AWS-native environment optimized for long-term growth. By improving observability, reducing infrastructure costs, and enabling future model customization, Foundation AI established a more predictable and scalable AI foundation.

The Results

Migrating onto Amazon Bedrock helped Foundation AI scale its AI-powered document intelligence platform more efficiently while reducing operational costs and improving throughput. By modernizing its LLM infrastructure on AWS, the company gained greater flexibility, improved observability, and a more predictable cost structure for future growth.

The new architecture also created a stronger foundation for future AI optimization initiatives, including model fine-tuning and workflow automation across high-volume document processing operations.

Before After

OpenAI-dependent infrastructure AWS-native Bedrock architecture

HIgher LLM operational costs 30–40% reduction in LLM-related costs

Limited scalability predictability Scalable and predictable unit cost structure

Manual optimization challenges Improved observability and monitoring

Lower throughput capacity 20% increase in document throughput

30–40% reduction in LLM-related costs

10–20% uplift in extraction and classification accuracy

~$0.02 to ~$0.005 target unit cost reduction per page

20% increase in document throughput

Technology Leveraged:

Amazon Bedrock
Amazon S3
AWS Lambda
Amazon CloudWatch