Case Study: Generative AI & Agentic Platform Development
About Client:
Client is operating a data-intensive business model centered around corporate registrations, investment structures, and multi-source news intelligence.
Industry: Corporate Data Intelligence & Investment Analytics
Location: United States, San Francisco, California
We partnered with the client to build a fully scalable Generative AI & Agentic Platform from the ground up, enabling automation, intelligence, and self-service analytics across the organization.
Business situation:
The client's rapid growth exposed several operational bottlenecks:
- Manual extraction of ownership, share classes, and funding details from Certificates of Incorporation.
- Slow, analyst-driven investment waterfall modeling.
- Noisy, unstructured news ingestion lacking deduplication and relevance scoring.
- Limited access to enterprise datasets for non-technical teams.
- No unified AI platform to support agentic workflows, RAG systems, or scalable GenAI integrations.
These challenges hindered research velocity, slowed decision-making, and increased dependency on engineering teams.
Technical situation:
A system evaluation revealed critical limitations:
- Unstructured document processing requiring human review.
- Fragmented ETL pipelines without entity extraction or quality scoring.
- Snowflake datasets inaccessible to non-technical users.
- No agentic or RAG infrastructure for automated validation or workflow orchestration.
- Architecture lacked modularity for future AI integrations.
The client needed a robust, scalable AI foundation capable of automating document processing, enhancing research workflows, and enabling natural-language analytics.
Technology Platform used:
- AWS Textract
- Large Language Models (LLMs)
- LangChain
- Apache Airflow
- SerpAPI
- Snowflake Cortex Agents
- Custom RAG Systems
- MCP Servers
- AI Platform Components
Openmind Services:
- AI Architecture Consulting
- Agentic Workflow Engineering
- Data Engineering & ETL Development
- Enterprise AI Deployment
- User Experience & Adoption Strategy
Solution
Openmind Technologies delivered a comprehensive Generative AI & Agentic Platform, designed end-to-end with modularity, scalability, and enterprise-grade reliability.
1. Certificates of Incorporation Reader
- Built an LLM-powered document processing system using AWS Textract to extract registration and investment details.
- Developed AI bots to analyze funding rounds, share classes, ownership structures, and voting rights.
- Implemented LangChain-based agentic workflows for validation, discrepancy detection, and webhook triggers.
- Automated investment waterfall modeling, generating visual charts for capital distribution and investor returns.
2. News Intelligence Pipeline
- Designed Apache Airflow ETL pipelines using SerpAPI for multi-source news ingestion.
- Implemented a Medallion architecture for structured ingestion, processing, and delivery.
- Built deduplication and entity extraction workflows to eliminate duplicates and identify key entities.
- Developed LLM-based article evaluation pipelines using custom relevance and impact scoring.
3. Snowflake Data Agents
- Built Snowflake Cortex Agents enabling natural-language querying of enterprise datasets.
- Designed domain-specific semantic views defining business metrics and relationships.
- Tuned agent instructions and prompts to improve accuracy, relevance, and self-service analytics.
4. AI Platform & Agentic Architecture
- Designed and implemented RAG systems, agentic frameworks, MCP servers, and a unified AI Platform.
- Ensured modular integrations, scalability, and cross-team adoption through strong architectural foundations.
Benefits :
Operational Efficiency
- Up to 90% reduction in manual document review time.
- Investment modeling reduced from hours to minutes.
Research Acceleration
- Enriched, ranked, and deduplicated news datasets improved research velocity.
- Actionable signals enabled faster decision-making.
Self-Service Analytics
- Non-technical teams gained direct access to Snowflake data via natural-language agents.
- Reduced dependency on data engineering teams.
Scalable AI Foundation
- Architecture supports future GenAI and agentic initiatives.
- Modular components allow rapid integration of new data sources and workflows.
Next Steps
The client plans to expand the platform across compliance, operations, and investor relations, integrating additional real-time data sources and extending agentic workflows throughout the organization.