Implement Custom AI for Proposal & SOW Automation

Many agencies start with tools like Proposify or PandaDoc. These platforms offer templates and e-signatures but fail at the most time-consuming step: writing the actual scope of work. They can pull a client's name from HubSpot, but they cannot read 3 pages of discovery notes and synthesize a tailored project plan. Your team still spends hours manually summarizing client needs, defining deliverables, and calculating prices, defeating the purpose of automation.

Consider a 10-person agency that just finished a discovery call. The account executive has detailed notes in a HubSpot deal record. To build the proposal, they open a Google Docs template, copy-paste the client details, and manually write several pages of scope based on their interpretation of the notes. This process takes 2 hours of a senior person's time. A week later, another AE does the same for a similar client but phrases the scope differently and prices it 15% lower, creating inconsistency.

The structural failure is that document templating tools are not logic engines. They operate on static placeholders, not the unstructured text where the real project context lives. They cannot be trained on your past successful proposals to learn what a good scope description looks like for a specific service. This forces your most experienced people into low-value administrative work, slowing down your sales cycle and introducing a high risk of manual error.

The engagement would begin with a discovery and audit phase. Syntora would review your last 20-30 proposals and SOWs to understand your service offerings, pricing structure, and tone. We'd map the decision logic from your CRM data (e.g., specific client requests in call notes) to the sections that appear in your final documents. This audit produces a clear data requirements document before any code is written.

The technical architecture would use the Claude API to read unstructured notes from a HubSpot or Salesforce deal and generate structured proposal content. A FastAPI service would manage the logic, pulling data from the CRM, passing it to Claude with specific instructions based on our audit, and receiving back a JSON object containing the full proposal text. We have built similar document processing pipelines for financial services, and the same pattern of using a large language model to parse and structure text applies directly to proposals.

The final deliverable is a lightweight system that plugs directly into your existing workflow. For example, a button in your HubSpot deal view labeled "Generate Proposal" would trigger the process. Within 60-90 seconds, a link to a fully drafted document appears in the deal's notes. You receive the full Python source code, hosted in your own AWS account, ensuring you have no vendor lock-in.