Automate Proposal and SOW Generation with a Custom AI System

Most professional services firms rely on a combination of their CRM, like HubSpot, and a document tool like PandaDoc or Proposify. The CRM stores client data, but proposal creation is a manual export and copy-paste process. Document tools offer templates and e-signatures, but they are fundamentally mail-merge systems. They can insert a client's name but cannot reason about the content of the proposal itself.

Consider a 15-person consulting firm that gets an inbound lead. The partner spends 30 minutes on a discovery call, taking notes in a Google Doc. To create the SOW, an associate opens a template, copies the client info from HubSpot, writes a custom project summary, and then pulls in three standard service descriptions. They must then manually select the two most relevant case studies from a separate library and calculate the project fee in a spreadsheet. This 90-minute, multi-application process is repeated for every prospect and is filled with opportunities for error.

The structural problem is that these tools treat documents as static containers. They lack a semantic understanding of your services, pricing logic, or client needs. They cannot dynamically construct a document from component parts based on complex rules or unstructured input like discovery call notes. A template can't decide which case study is most relevant or combine two services into a custom package with calculated pricing. This forces your team into high-cost, low-value administrative work.

The first step is a discovery process to audit your existing proposals from the last 12 months. Syntora would map every service component, pricing rule, case study, and team member bio into a structured knowledge base. This audit also analyzes the format of your client intake notes to determine the best way to parse them. You receive a clear outline of this knowledge base for approval before any code is written.

The technical approach would use a Python-based FastAPI service as the core. Unstructured client notes would be fed to the Claude API, chosen for its large context window and strong instruction-following ability. A detailed prompt would instruct the model to identify the required services, select appropriate case studies, and draft key sections like the executive summary. Pydantic models enforce a strict JSON output, ensuring every generated SOW has the correct structure before it is rendered into a document. This pattern of using an LLM for parsing and structured Python for business logic is one we have used successfully to process complex financial documents.

The delivered system is a simple web interface where your team can paste discovery notes and click 'Generate'. Within 60 seconds, it produces a fully formatted Microsoft Word or Google Doc. This system can connect to HubSpot to automatically pull company data and push the final SOW to the correct deal. You receive the full source code deployed on AWS Lambda, a runbook for updating your services, and complete documentation.