Solve CRM Integration Problems Off-the-Shelf Connectors Can't

Teams often start with off-the-shelf connectors for platforms like HubSpot or Salesforce. These tools are great for basic A-to-B syncs. But they fail when logic gets complex. For example, a connector might sync a new contact but cannot handle a three-way merge: if a contact exists in HubSpot AND a separate billing system, update both with data from a third source. The connectors lack conditional merging logic.

A regional insurance agency with 6 adjusters tried to automate claim intake. They needed to pull claim data from a partner portal's API, create a record in their Salesforce CRM, and generate a task list in Asana based on the claim type. The connector they used could create the Salesforce record but failed on the Asana part. It lacked the ability to parse the claim's nested JSON data to create conditional subtasks, forcing adjusters to spend 10 minutes per claim manually creating tasks. At 200 claims per week, this was 33 hours of lost productivity.

The fundamental issue is that pre-built connectors operate on a fixed schema. They expect data in a specific format and can only perform predefined actions. They cannot handle custom data objects, run asynchronous checks against multiple systems before committing a transaction, or implement custom retry logic for fragile third-party APIs. They are built for the 80% use case, not for your specific business process.

Syntora would start by mapping your entire workflow, including every edge case and potential point of failure. This involves detailed discovery sessions to understand your data structures and operational requirements. Syntora would use httpx to make direct, authenticated calls to each system's API, whether it is Salesforce, a custom ERP, or a legacy platform with a SOAP endpoint. This approach provides granular control over every request and response, with each step logged using structlog for full traceability.

The core logic would be built as a Python service using FastAPI. For example, a workflow might ingest a partner portal's JSON payload. A Pydantic model would validate the incoming data structure. The logic would then make an async call to Salesforce to create a claim record and a second async call to Asana to generate a task list. State and logs would be stored in a Supabase Postgres database. We've built similar document processing pipelines using Claude API for financial documents, and the same architectural patterns apply to complex data transformations in CRM contexts.

This FastAPI service would be deployed as a container on AWS Lambda, triggered by an API Gateway endpoint. This serverless architecture offers cost-efficiency at low volumes, often under $20 per month for handling up to 50,000 transactions, and automatically scales to handle spikes in traffic without manual configuration. A deployment of this kind would typically be provisioned and tested within the engineering phase of the project.

Syntora would configure CloudWatch Alarms to send Slack alerts if the API error rate exceeds 1% or if latency spikes above 2 seconds. The structured logs from structlog would be piped to CloudWatch Logs, allowing for pinpointing the exact request that failed and why. This proactive monitoring allows for rapid identification and resolution of potential issues. Deliverables would include the deployed and tested system, source code, and comprehensive documentation.