99% Reduction in manual effort | Pillar Engine
Discover how we built an AI-powered document intelligence platform for Pillar Engine, reducing manual effort by 99%, processing 10K pages in 2 minutes, and delivering 85%+ accurate insights using AWS Bedrock and LLM automation.
Project typeDocument Intelligence & Automated Insight Generation Platform
IndustryE-commerceTech stack
About the Client
Pillar Engine is a Document Intelligence Platform that transforms how teams interact with large-scale data. By allowing users to search, understand, and review extremely large documents, the platform reduces manual effort from days to minutes. It provides direct answers to user questions and pinpoints the exact page where information exists, ensuring accuracy and reliability across thousands of pages of information.
*All names and logos have been changed to respect the NDA
About the project
Overview
GeekyAnts partnered with Pillar Engine to develop the "Mobility – Toolkit AI," an automated system designed to generate and serve role confirmation insights for employees, teams, and VPs. The project involved building an end-to-end AI-driven pipeline that extracts performance data and converts it into human-readable summaries.
The primary challenge was automating the creation of role confirmation insights from Snowflake data across different hierarchy levels while maintaining strict AWS compliance and scalability. By implementing a custom "Strand Agent" and leveraging AWS Bedrock, the solution successfully replaced a manual, inconsistent process with a secure, automated workflow. This transition has dramatically improved speed, consistency, and decision-making for VP operations within the internal MyBOS platform.
99%
Reduction in manual effort2 mins
To process 10K Pages85%+
Accuracy in responses
BUSINESS REQUIREMENT
The client’s vision was to eliminate the manual creation of text summaries from raw data, which previously led to delays and inconsistencies across business units. The goal was to establish a consistent, insight-driven process for role confirmations on the internal MyBOS platform.
The client needed a scalable solution that could:
- Automate the creation of role confirmation insights from Snowflake data.
- Handle insights for different hierarchy levels, business units, and scopes.
- Generate contextual, narrative text using LLMs within a compliant AWS environment.
Key Requirements
- Creating a fully automated AI-driven insight generation pipeline.
- Delivering weekly refreshed insights across organizational hierarchies.
- Ensuring data-backed summaries using secure, AWS-native components.
CHALLENGES IN EXECUTION & SOLUTIONS
In building the platform, we addressed the challenge of large data extraction for multiple hierarchy levels by implementing parameterized Snowflake queries with pagination and staged fetch. To resolve Bedrock cross-account access, we configured IAM trust policies between roles to allow secure LLM communication.
We also managed ETL runtime constraints by shifting ETL logic to ECS (Fargate) instead of Lambda for better resource control over long-running jobs. Finally, to ensure insight consistency, we added prompt templates and structured output parsing in the Strand Agent to ensure uniform results.
Accurate Personalization from Limited Data
Cultural Relevance of AI Recommendations
Handling Unstructured Food Inputs
Scalability of AI Workflows
OUR SOLUTION
GeekyAnts implemented a robust ETL pipeline and AI agent architecture to automate the insight lifecycle.
- Automated Data Extraction: Built an ETL pipeline on AWS ECS (Fargate) triggered by EventBridge to extract data from Snowflake via complex multi-unit queries.
- AI-Powered Transformation: Developed a "Strand Agent" integrating AWS Bedrock (Claude) to transform raw data into contextual, human-readable narrative text.
- Low-Latency Serving: Created an Insight Serving Lambda to retrieve generated summaries from DynamoDB and serve them to managers and dashboards instantly.
OUR APPROACH
To ensure predictable delivery and strong technical foundations, we followed a milestone-driven, discovery-first approach:
- Product discovery & requirement deep-dive
- UX/UI design & rapid prototyping
- AI architecture & recommendation logic design
- MVP development & integration
- Testing, iteration, and launch readiness
- Conducted detailed discussions to understand hierarchy levels, business units, and internal role confirmation scopes.
- Evaluated AWS Bedrock (Claude) for managed LLM access to ensure enterprise-grade IAM integration and compliance.
RESULTS
The Mobility project established a fully automated AI-driven insight generation pipeline for the MyBOS platform. It ensures weekly refreshed insights across organizational hierarchies and provides consistent, data-backed summaries using secure AWS-native components.
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