Automate the Entire Sales Cycle with Custom AI

Most sales teams at service-based businesses rely on template tools like PandaDoc or Qwilr. These platforms are excellent for e-signatures and managing a static content library, but they fail at dynamic assembly. A sales rep still manually copies the client's business needs, project scope, and custom pricing from the CRM into the PandaDoc template. The tool's dynamic fields only handle basic mail-merge for names and dates, not conditional logic for service bundles or tiered pricing.

Consider a 20-person digital marketing agency using HubSpot. To create a new proposal, a rep copies last week's Google Doc, opens the HubSpot deal record in another tab, and spends 45 minutes manually transferring the client's name, address, key contacts, and the six specific deliverables discussed on the discovery call. They then pull relevant case studies from a separate folder and calculate pricing on a spreadsheet. This manual process introduces frequent errors, like sending a proposal with the previous client's name still in paragraph three.

Enterprise-grade CPQ (Configure, Price, Quote) software from Salesforce or Oracle is designed to solve this, but it is built for companies selling complex physical products with SKUs, not services. The licensing fees are prohibitive for a 20-person team, and a full implementation requires a six-month project and a dedicated administrator. These systems cannot easily parse a sales rep's discovery call notes to extract the nuanced project scope required for a service-based SOW.

The fundamental issue is that off-the-shelf tools are architected for either simple text replacement or rigid product catalogs. They lack a middle layer capable of interpreting the semi-structured and unstructured data of a services sales process. This forces teams into a choice: tedious manual work or enterprise software that does not fit their business model, leaving a persistent gap in their sales workflow.

The first step is a discovery process focused on your documents. Syntora would analyze 10-15 of your recently won proposals and SOWs to create a master data map. This map identifies every variable field, the business logic behind it (e.g., how pricing tiers are calculated), and its source of truth, whether in a CRM field or embedded in a sales rep's call notes. You receive this data map for review, ensuring the logic matches your exact sales process before any code is written.

The technical system would be a Python service using FastAPI, deployed on AWS Lambda for cost-effective, on-demand processing. When a sales rep triggers the action from your CRM, the FastAPI service retrieves deal data from the CRM's API. For unstructured text like call notes, the system uses the Claude API's tool_use feature to extract specific deliverables, timelines, and client objectives. We have used this same architecture to process complex financial documents; the pattern directly applies to understanding sales-related text.

The final output is a fully-formed Google Doc, created via the Google Docs API, that is 95% complete and ready for the sales rep's final review. The rep gets a notification in Slack or email with a link to the draft. The system takes under 60 seconds from trigger to document delivery. You receive the complete source code, a deployment runbook, and a system that integrates into your existing CRM without requiring your team to learn a new piece of software.