Build a Custom AI-Powered CRM Workflow From Scratch

Most small businesses rely on the native automation features within their CRM, like HubSpot Workflows or Salesforce Flow. These tools are effective for simple, linear tasks based on structured data, such as sending a follow-up email when a deal stage changes. However, they fail when the workflow requires external logic or involves unstructured data, which is common in client onboarding.

Consider a 15-person services firm. A new client fills out a web form, which creates a contact in the CRM. Then they email a signed 10-page PDF contract. A project manager must open that PDF, find the project start date, total budget, and key contacts, then manually copy-paste over 20 fields into the CRM and a project management tool. This 15-minute manual process, repeated for every new client, introduces errors and delays the project kickoff.

The core architectural problem is that built-in CRM automations cannot natively call a powerful Large Language Model to perform entity extraction on a PDF. They are not designed to handle multi-step validation logic that might require checking data against an external system before creating a record. They operate within a closed system, forcing your team to perform the critical, error-prone work of bridging the gap between unstructured documents and structured CRM fields.

The first step is a discovery call to map your exact client onboarding process, from the first form submission to the final project setup. Syntora audits the fields you collect, the format of contracts or SOWs, and the specific records and objects that need to be created in your CRM. This produces a clear architectural plan before any code is written.

The technical approach would use a Python application, built with the FastAPI framework and deployed on AWS Lambda for efficient, serverless execution. When a new contract is received, the application uses the Claude API to parse the PDF, extracting specific entities like client name, project scope, and billing terms. We have built similar data extraction pipelines for financial documents; the same pattern applies directly to client agreements. Pydantic schemas then validate the extracted data to ensure it matches the format your CRM requires.

The delivered system connects directly to your CRM's API. After parsing and validation, the application automatically creates a new deal, populates all required fields, and uploads the original contract as an attachment. The entire process from receiving a document to having a complete, accurate CRM record takes under 10 seconds. You receive the full source code and a runbook for maintenance.