Automate Proposal Generation with Production-Grade AI

Most small agencies use tools like PandaDoc or Proposify for proposals. These platforms provide templates and content libraries, but they do not generate content. A senior strategist or account director must still manually find, copy, and edit every text block for a new client, creating a bottleneck that ties up the most expensive people in the agency.

Many teams still rely on Google Docs or Word templates, which are dangerously prone to error. In practice, this means an old client's name gets left in a new SOW, or an outdated price for an SEO audit makes it into the final version. Without a direct connection to a CRM like HubSpot or an accounting tool like QuickBooks, every client detail and price point requires manual entry, leading to duplicated work and embarrassing mistakes.

Consider a 10-person agency. An account director spends three hours crafting an SOW, copying sections from a recent Google Doc. They manually pull service pricing from a spreadsheet and ask the founder for review. The founder finds an outdated service description and the wrong price. This process consumes 4-5 hours of senior-level time for every single proposal.

The structural problem is that these tools are built for document assembly, not content intelligence. Their architecture cannot ingest a new client brief, parse it for intent, and dynamically generate relevant case studies and pricing from a knowledge base of past successful proposals. They treat your proposals as static files, not as a structured dataset to learn from.

The first step would be a content audit. Syntora would analyze your last 25-50 winning proposals and SOWs to map out service descriptions, case studies, team bios, and pricing logic. We would connect to your HubSpot instance to understand how deal data maps to proposal content. This audit produces a clear data model for the AI to use.

The core system would be a FastAPI service using the Claude API for content generation. When you input a new client brief, the service converts your past proposals into a vector database using Supabase's pgvector. The system performs a similarity search to find the 3 most relevant past SOWs, then feeds that context to Claude to draft a new proposal. Pydantic models validate the output structure, ensuring every SOW includes the required sections.

The delivered system would be a simple web interface where your team inputs the client name and a brief description of their needs. The system generates a draft in Google Docs format within 60 seconds, pulling client data from HubSpot and service pricing from QuickBooks. You receive the full Python source code, deployed on AWS Lambda for a hosting cost under $50 per month, and a complete runbook for maintenance.