Automate Your Proposal and SOW Generation

Many small firms rely on tools like PandaDoc or Proposify. These platforms are excellent for templating and e-signatures. They can pull structured data like `{client_name}` from a CRM, but their logic is limited. They cannot read unstructured discovery notes and dynamically select the three most relevant service descriptions or case studies. The intelligence to assemble the document still resides entirely with the user.

This leads most founders back to the manual process: copying a master Google Doc or Word file and editing it for each new client. A 5-person consultancy founder spends 90 minutes per proposal manually deleting irrelevant sections, rewriting scope details from call notes, and checking pricing calculations. Sending a proposal with a paragraph left over from a previous client is a common and embarrassing error that erodes trust before the engagement even begins.

The structural problem is that templating tools are built for static content replacement, not dynamic content assembly. Their architecture cannot interpret the intent within a page of meeting notes. To solve this, you need a system that can parse natural language, map concepts to your approved content library, and build a document from the ground up for each specific opportunity. This is an engineering problem, not a template management problem.

The first step would be to audit your 10-15 most recent proposals. Syntora would analyze these documents to identify and chunk your content into a reusable library of service descriptions, case studies, team bios, and legal clauses. This content library would be stored in a Supabase PostgreSQL database, where each block is tagged with keywords for easy retrieval.

We would build the core logic in a FastAPI service that uses the Claude API. The service would accept unstructured text, like a team's meeting notes from a CRM, as its input. Using Claude's `tool_use` capability, the system would parse the notes to extract key requirements, then query the Supabase database for the most relevant content blocks. We have built similar document processing pipelines for financial analysis, and the same pattern applies directly to proposal automation. The system assembles these blocks into a final document using a library like `python-docx` for Word or a PDF generator.

The delivered system provides a simple interface where you can paste notes or a CRM link. In under 60 seconds, it generates a draft proposal in Google Docs, ready for a 5-minute final review. The entire system would run on AWS Lambda, keeping hosting costs under $30 per month for typical volume. You get a purpose-built asset that turns your best content into new proposals instantly.