Generate Custom Proposals in Minutes, Not Hours

Many agencies rely on tools like PandaDoc or Proposify for templates and e-signatures. These platforms are effective for document assembly but do not write the actual scope. An account manager still spends 2-3 hours copy-pasting service descriptions and manually calculating pricing tiers from a separate spreadsheet. The core intellectual work remains entirely manual; the software only formats the final output.

Larger platforms like HubSpot Sales Hub or Salesforce CPQ attempt to solve this with product catalogs. They force agencies to define their services as fixed-price items, a model that fails for custom project work. A web development agency cannot easily quote a project with a unique blend of discovery, design, and backend work. The system forces them to choose from pre-defined packages, stripping the nuance from the proposal and making it feel generic.

Consider a 15-person marketing agency after a great discovery call. The account director has four pages of notes in a Google Doc. To build the SOW, they open a template and begin copying service descriptions from a master document. They open a complex Excel pricing calculator, input estimated hours for five different services, and paste the final numbers into the pricing table. The process takes three hours of a senior employee's non-billable time, delaying the proposal and risking the deal momentum.

The structural problem is that these tools separate document management from business logic. The proposal's intelligence, which includes the service scope and pricing rules, lives outside the system in disparate documents and spreadsheets. This manual gap slows down sales cycles, introduces errors from outdated source materials, and puts a hard cap on how many high-quality, custom proposals your agency can produce.

The first step would be a thorough audit of your sales documents. Syntora would analyze 20-30 of your past winning proposals and SOWs to map your service offerings, pricing logic, and unique voice. We would then correlate these documents with your discovery notes, whether they are in call transcripts, forms, or emails. You would receive a data-readiness report that identifies the core components and patterns for the AI to learn.

The technical approach uses Retrieval-Augmented Generation (RAG). The system would be a FastAPI service using the Claude API for document creation. Your library of past SOWs and service descriptions is converted into vector embeddings and stored in a Supabase Postgres database with the pgvector extension. When a user submits new discovery notes, the system finds the most relevant examples from your past work and provides them to the Claude API as context. This ensures the generated proposal uses your exact terminology and pricing, not generic boilerplate.

The deliverable is a simple web application where your team can upload discovery notes and receive a fully-drafted proposal in Microsoft Word or Google Docs format in under 90 seconds. The system can be configured to pull client data from HubSpot to pre-populate fields. You own all the code, the vector database, and the prompts. The system is deployed on AWS Lambda for low, pay-per-use hosting, typically costing under $20 per month.