Automated Insight Extraction
Strategic Intent
Enable analysts to extract, structure, and compare key information from real estate documents quickly and reliably, reducing manual effort and improving clarity across workflows.
Context
Analysts often work with long, complex documents filled with critical financial and operational details. Extracting these manually is repetitive, prone to error, and disrupts the pace of analysis. Docs needed a way to turn unstructured information into consistent, comparable insights.
Understanding the Need
Through user interviews, early Chat with Docs usage, and observing patterns across document-heavy workflows, we saw that users were spending significant time reading, searching, and formatting information manually. These insights made clear that automation could reduce cognitive overload and accelerate decision-making.
Pain Points
Manual extraction required extensive reading and repeated effort.
Insights were scattered across PDFs, spreadsheets, notes, and personal files.
Formatting extracted data was slow and inconsistent.
Difficulty cross-referencing multiple documents at once.
Low trust in AI when users couldn't easily validate results.
Approach
We designed a structured extraction flow that transforms documents into organized tables. The process allows users to upload a file, define or select a table template, and let the system generate structured fields. Users can edit prompts, validate data instantly, and create custom fields using natural language.
Core Capabilities
Fast Upload & Autodetection: Identify document type and prepare the extraction process.
Automated Table Generation: Extract key values into a consistent, structured table.
Field Creation via AI: Generate new fields and subfields from a short instruction.
Editable Prompts: Adjust extraction logic directly inside the table.
Dual-View Layout: Document and table shown side-by-side for real-time validation.
Outcome
Data Extraction introduced a faster, clearer, and more reliable way to understand and compare information across documents, reducing friction, increasing trust in AI-driven outputs, and strengthening the role of Docs as the central workspace for analysis.