Stop running BI dashboards on scraped data
One API call pulls all your Synup data into the warehouse of your choice. The schemas are normalized across every directory and source. Looker, Tableau, BigQuery, Snowflake all hook up the same way. No more maintaining the ETL nobody wanted to own.
Analytics engineers at agencies serving multi-location SMB clients
BI team leads building reporting infrastructure for a parent agency
Data engineers at vertical SaaS products that surface local presence to their customers
Reporting consultants who specialize in agency dashboards and need a clean data layer
A daily sync from Synup to your warehouse
A scheduled job pulls interaction analytics, listings status, and rankings into the warehouse. Once it's there, every Synup data point is queryable in SQL. Point Looker, Tableau, or Mode at the warehouse and the dashboards work.
A Looker template for agency reporting
The dashboard ships pre-built. It covers the panels agencies actually use, including grid rank movement and review velocity per location. Plug in your API key, point at the right locations, and the dashboard renders.
An anomaly detection layer
When a location's review velocity drops 60% week-over-week, the layer fires a Slack alert. Big grid rank moves trigger the same alert. The team finds out before the client does.
A multi-tenant reporting layer
Workspaces map to clients. Each client gets their own view of their own data. White-label the dashboard with your agency's brand and ship it as a deliverable.
For analytics, the interaction-analytics endpoint returns sentiment, rating, response rate, and source breakdown over any time window. A rollup endpoint pre-aggregates the same data for fast dashboard queries. Rankings come back from a separate endpoint, with both standard and grid-rank data in the same response. The JSON uses normalized field names across every directory and source. Drop it all into your warehouse on whatever cadence works: hourly for live dashboards, daily for client reports.
An agency analytics team running 14 different scraper pipelines for 14 different directories, two of which broke each month when the directories changed their HTML. Three engineers were spending roughly 35% of their time on scraper maintenance. They replaced the whole stack with Synup. The engineers reclaimed roughly 6 engineer-quarters per year. Their boss let them ship two new features for the agency's product team instead of patching scrapers.
Stop maintaining 14 scraper pipelines
Get an API key and test against our sandbox in minutes. The docs include reference implementations for BigQuery, Snowflake, Redshift, and Databricks.