Quorate
An AI document-intelligence platform that reads proxy statements and investor voting guidelines, then flags governance compliance risk, built for a US corporate-governance analytics startup.
Product preview
Representative interface, rebuilt with fictional data. The client's production UI is not shown.
The challenge
Every proxy season, a public company's governance team has to answer the same question for each of its largest institutional shareholders: does our proxy statement comply with your voting guidelines, item by item? Each investor publishes its own dense policy PDF, and cross-referencing them against a 100+ page proxy statement across dozens of policy topics is weeks of manual analyst work, repeated for every investor, every year, under filing-deadline pressure. Quorate needed that entire workflow automated, without sacrificing the traceability that governance professionals require to trust an answer.
Our approach
Three-service architecture, a Next.js app, a FastAPI core, and a Python AI worker, coupled only through AWS SQS so model work ships independently of API work
Multi-stage document pipeline: OCR, ballot-item extraction, question answering, guideline extraction, and compliance flagging, with every answer citing source-PDF pages
Four LLM providers (Gemini, Claude, GPT, Mistral OCR) behind a single abstraction, using context caching to reuse each parsed proxy across every analysis batch
Ownership-weighted risk scoring via a nightly Snowflake sync of shareholder positions, so compliance flags reflect each investor’s actual stake
Results
API Endpoints Shipped
LLM Providers, One Abstraction
Policy Categories Analyzed
This engagement is published under NDA. “Quorate” is a pseudonym, and the interface above was rebuilt with fictional data. The architecture, pipeline, and scope described here are real.
Client confidentiality
References available on request
Capabilities used
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