Build vs. Buy: AI Proposal Automation for Professional Services

Many professional services firms rely on tools like PandaDoc or Qvidian for proposals. PandaDoc is excellent for templating, but its content library consists of static blocks. It cannot dynamically generate a scope section by analyzing a Gong call recording; a human must still listen to the recording, type the summary, and paste it into the template.

Consider a consulting firm that produces 50 SOWs a year. For each one, a partner spends 3-4 hours after the final sales call trying to reconcile their CRM notes, the client's MSA, and the call recording. They manually check if the payment terms discussed on the call conflict with the MSA's standard Net 30 clause. This post-production work is a non-billable, single-person bottleneck that delays sending the final document and introduces risk from manual errors.

The structural problem is that these off-the-shelf tools are document editors with mail-merge features, not data processing systems. Their architecture is designed to fill a template with data from CRM fields. They are not built to ingest unstructured data like a call transcript, identify contradictions with a separate legal document, and then generate the document structure itself based on the findings.

An engagement starts with a data audit. Syntora maps every source of truth in your sales process, from call recordings in Gong to account details in Salesforce and legal clauses in your MSAs. This audit confirms what data is available, identifies gaps, and produces a data flow diagram that serves as the blueprint for the automation. You approve this plan before any code is written.

We built our own system using the Claude API to process transcripts and a FastAPI service to orchestrate the workflow. For a client, the approach would be similar: a Python-based AI agent retrieves the latest call transcript and CRM data. It extracts key entities like scope items, deliverables, and pricing, structuring them into a JSON object. This process handles transcripts over 200,000 tokens long and can cut a 4-hour manual review down to a 30-minute verification step. The JSON object then drives a separate process that generates a print-ready HTML SOW, hosted on Supabase for under $30/month.

The delivered system plugs directly into your existing workflow. A user can trigger the SOW generation from a button in Salesforce. The system creates the document, flags any detected contradictions between the SOW and the MSA for human review, and stages it for sending. You receive a system that eliminates the manual bottleneck and ensures every discussed item makes it into the final agreement.