Automate Proposal and SOW Generation with Custom AI

Many professional services firms rely on tools like PandaDoc or Qwilr for proposals. These platforms offer excellent templating and e-signature capabilities but are fundamentally passive. They require a human to manually listen to a Gong call, read through Salesforce notes, and type every scope item, deliverable, and timeline into the template. The tool formats the document, but it does not reduce the 3-4 hours of high-skill administrative work.

Consider a 30-person digital agency. A partner closes a deal on a call, promising a specific set of deliverables and a performance guarantee. A project manager is then tasked with creating the SOW. They spend an hour relistening to the call recording, another hour cross-referencing notes in HubSpot, and a final hour ensuring the language aligns with the master services agreement stored in a separate folder. This workflow is slow, error-prone, and creates a single-person bottleneck. If a key deliverable is missed or the SOW contradicts the MSA's payment terms, it creates client friction and legal risk.

Off-the-shelf proposal tools cannot solve this because their architecture is template-based, not data-driven. They are designed to format information, not extract and validate it. They lack the connections to unstructured data sources like call transcripts and the logic to compare clauses between two different legal documents. The core problem is not formatting the SOW; it is the manual, high-risk work of gathering and verifying the information that goes into it.

The first step is a discovery audit of your sales-to-project-handoff process. Syntora maps every source of truth for a new project, from call recordings in Gong to deal data in Salesforce and legal language in your MSAs. This audit identifies where scope is defined, where it gets lost, and what validation rules are required to produce a contract-ready document. You receive a process map showing exactly how data will flow from discovery call to final SOW.

Syntora has direct experience building these systems for its own operations. We built a proposal pipeline where Claude Sonnet 4 extracts client requirements from Fireflies call transcripts, generates a structured `proposal.json`, and publishes it to a viewer via Supabase. For a professional services client, this approach would be extended. An AI agent would connect to your Salesforce and Gong APIs, detect contradictions between the MSA and the SOW, and ensure all discussed scope items make it into the final document.

The delivered system is a production-ready FastAPI service that integrates directly into your existing workflow. When an opportunity stage is updated in your CRM, it triggers the generation process. Minutes later, a link to a draft SOW and a list of flagged inconsistencies appear in the opportunity record. The system cuts the 3-4 hours of manual post-production work down to a 30-minute review, removing the bottleneck and eliminating transcription errors.