SaaS Connector Development

400+ Connectors Delivered

We help you drive the adoption of your product by building integrations at scale. We deploy Integration PODs - a team of expert product analysts, application engineers, data integrators, and QAs - to rapidly scale your integrations and lower the time to market.

SaaS Connector Development

Integrate Systems. Build Intelligence. Automate Workflows.

With the experience of enabling over 400 integrations, we build connectors at scale to enable accelerated adoption of your platform or service.

Change Resilient

Change Resilient

We enable comprehensive integration tests to conduct data integrations, which assures consistent health, status, and failure check taking place due to system changes.

Self Contained

Self-Contained

Empower your enterprise teams to deploy self-contained microservice, serverless package, or library/module. You can even integrate it with your platform or an independent component.

Auto Deployable

Auto Deployable

We ship integrations ready - typically as a Docker container - to be plugged into the CI/CD pipeline for automated build and deployment.

Well Documented

Well Documented

Be assured of your business requirements, interfaces, schema, mappings, and transformations to be documented and maintained throughout the data integration lifecycle.

Looking to build connectors at scale?

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Customer Stories

Learn how we helped a Forbes Fastest 500 MarketingBICo to scale its integrations to 300+ in 12 months. We conceptualized and built a connector development framework comprising of Canonical Schema, Auth Handling, Transformation Layer, and Integration Test Suits to enable rapid development, release, and marketing of the connectors.

A Forbes 2000 performance marketing agency tasked us to build a marketing data warehouse to stitch together disparate marketing data sources. We built data pipeline orchestration, data connectors, and cloud data warehouse in 4 months to enable Adops teams to leverage the data sources in their campaign planning and analysis.

We helped an AI-first EHRCo to tap into the existing client EHR and PACS (Imaging) data sources by building a scalable and extensible integration architecture capable of integrating with Batch (historical data), Event (HL7 over MLLP), and Streaming (Healthcare IoT devices) data sets.