500+ regex rules struggled with messy text. AI agents expanded coverage 20x, cutting processing from 24 hours to one.
A market intelligence provider for institutional investors processes millions of daily transaction records, ranging from credit card feeds to web-scraped receipts.
Analysts attempted to encode human judgment into over 500 regex statements to map messy vendor records to specific businesses. This manual approach...
“Before Agent Bricks, our regex‑based pipelines were brittle, manual, and hard to scale. Now agentic reasoning lives inside our ETL, giving us 20X more coverage with better accuracy and faster delivery to customers.”
Alternative data and market intelligence platform for institutional investors.
Databricks is a Big Data company that offers a unified analytics platform for data science, engineering, and analytics teams.
Related implementations across industries and use cases
Seven analysts manually wrote notes for 200 products. AI agents now digest fact sheets to draft compliant commentary instantly.
Vendor tags missed key stock data. CFM now uses LLMs to train compact models, cutting costs 80x with 93% accuracy.
Advisors hunted data across five systems. GenAI now builds traceable meeting packs, saving 4,700 hours a year.
A single provider left 60% of leads blank. AI agents now scan earnings and sites, doubling coverage to 80% with systems built in hours.
Traders worked late nights parsing unstructured emails and PDFs. Now, non-tech staff build AI apps to extract prices and dates instantly.
Central IT couldn't reach the long tail of daily tasks. Now, 50% of staff use opt-in AI to scan contracts and prep for meetings.
Seven analysts manually wrote notes for 200 products. AI agents now digest fact sheets to draft compliant commentary instantly.
Hundreds of pages per board book slowed director prep. Now, isolated AI securely condenses sensitive materials into actionable briefs.
Custom hardware bottlenecked imaging. A switch to software-defined GPUs now renders photorealistic 3D hearts in real time.
500+ regex rules struggled with messy text. AI agents expanded coverage 20x, cutting processing from 24 hours to one.
A market intelligence provider for institutional investors processes millions of daily transaction records, ranging from credit card feeds to web-scraped receipts.
Analysts attempted to encode human judgment into over 500 regex statements to map messy vendor records to specific businesses. This manual approach...
“Before Agent Bricks, our regex‑based pipelines were brittle, manual, and hard to scale. Now agentic reasoning lives inside our ETL, giving us 20X more coverage with better accuracy and faster delivery to customers.”
Alternative data and market intelligence platform for institutional investors.
Databricks is a Big Data company that offers a unified analytics platform for data science, engineering, and analytics teams.
Related implementations across industries and use cases
Seven analysts manually wrote notes for 200 products. AI agents now digest fact sheets to draft compliant commentary instantly.
Vendor tags missed key stock data. CFM now uses LLMs to train compact models, cutting costs 80x with 93% accuracy.
Advisors hunted data across five systems. GenAI now builds traceable meeting packs, saving 4,700 hours a year.
A single provider left 60% of leads blank. AI agents now scan earnings and sites, doubling coverage to 80% with systems built in hours.
Traders worked late nights parsing unstructured emails and PDFs. Now, non-tech staff build AI apps to extract prices and dates instantly.
Central IT couldn't reach the long tail of daily tasks. Now, 50% of staff use opt-in AI to scan contracts and prep for meetings.
Seven analysts manually wrote notes for 200 products. AI agents now digest fact sheets to draft compliant commentary instantly.
Hundreds of pages per board book slowed director prep. Now, isolated AI securely condenses sensitive materials into actionable briefs.
Custom hardware bottlenecked imaging. A switch to software-defined GPUs now renders photorealistic 3D hearts in real time.