Choose an AI Partner for CRM Integration and Data Unification

Marketing teams often start with their CRM's built-in reporting, like the dashboards in HubSpot. These are useful for viewing contact and deal data, but they cannot join that information with external sources. You can see how many deals closed, but not how much you spent on Google Ads to acquire those specific customers. The data lives in separate silos, making true ROI calculation impossible.

To bridge this gap, teams turn to connector tools. These tools move data from point A to point B, but they do not create a unified source of truth. A workflow might push Google Ads cost data into a Google Sheet, and another pushes HubSpot deal data into a different tab. The marketing analyst is still left doing VLOOKUPs to connect a GCLID to a deal ID, a process that is slow, manual, and breaks easily. This architecture is stateless; it cannot build a historical record or handle complex joins.

A business intelligence tool like Looker Studio seems like the next logical step. You can connect multiple data sources and create dashboards. However, the data blending is often fragile and performs poorly with large datasets. Trying to join 12 months of HubSpot deal data with millions of Google Analytics events will time out or rely on sampled data, leading to inaccurate insights. If a custom property name changes in your CRM, the entire dashboard can break without warning.

The structural problem is that these tools are designed for visualization or simple data transport, not data unification. They lack a central, structured data store where cleaned and transformed data can live. Without a proper data model in a real database, you are perpetually stuck rebuilding reports and questioning the accuracy of your numbers.

The first step is a data audit. Syntora connects to your CRM, ad platforms, and analytics tools with read-only API access. We map out the key objects and fields required to answer your business questions, identifying the common identifiers (like UTM tags or user IDs) that will be used to join the data sets. You receive a data model diagram and a report on data quality issues before any build work begins.

The technical approach involves a set of Python scripts deployed on AWS Lambda. These scripts run on a schedule, typically every 6 hours, to pull fresh data from each source API. We use Pydantic for data validation to ensure schema consistency and prevent bad data from entering the system. The cleaned, transformed, and joined data is loaded into a dedicated Supabase Postgres database, which becomes your marketing team's single source of truth. A simple FastAPI service is deployed to provide a secure access point to this unified data for any dashboarding tool.

The delivered system is a production-grade data pipeline that you own completely. It includes a custom dashboard built on Vercel that displays key metrics and can answer natural language questions about performance using the Claude API. The dashboard loads in under 2 seconds, even with over 12 months of data. The entire system typically costs under $50 per month to run on AWS and Supabase, and the build is completed in 3-4 weeks.