An environmental consulting firm holds decades of project documents — each a PDF that can run to several hundred pages — produced by a large staff of expert consultants. When the firm responds to a new RFP, it needs to search this repository for relevant prior work, extract and summarize the pertinent details, and synthesize them into a fresh proposal.
Today that process depends heavily on the institutional memory of long-tenured employees to recall which past projects are relevant, followed by slow manual reading, extraction, and summarization. Every RFP is slightly different, so each one may demand its own variation of the same document-processing flow over the same underlying data.
The requirements of AI agent automation
Automating this work requires agents that can make a large, heterogeneous document repository genuinely searchable, retrieve the passages relevant to a specific RFP, and extract and summarize them accurately across documents of very different structure and length. Because each RFP can require a different flow, the automation also has to be easy to reconfigure in plain language rather than re-engineered each time.
Crucially, the outputs must be verifiable — every summary traceable to the source pages it came from — so that consultants can trust and defend what goes into a proposal. Thunk.AI's no-code, natural-language workflows and built-in verification make this kind of adaptable, auditable document automation practical.
The automated AI-agent-powered workflow
Instead of relying on memory and manual effort, AI agents built on Thunk.AI make the project archive searchable and automate the document-processing flow end to end — retrieving relevant prior work, extracting and summarizing key details, and assembling them into a draft for the consulting team to refine.
Related reading: see how agents extract structured data from public sources in credit research automation, how a research lab automated clinical-trial analysis at Fred Hutchinson Cancer Center, or read about the platform's approach to AI reliability.