01. Problem

A San Francisco startup building a custom machine learning system fed by data stored in Salesforce invited us to build a custom integration to regularly move high volume Salesforce data to Amazon AWS to populate machine learning jobs.

02. Solution

A high-performance MuleSoft integration was created to identify any new data in Salesforce apply required transformations and incrementally move any new chunks to Amazon AWS S3 system being a load area for Amazon Elastic MapReduce service. The integration was deployed in the cloud with an option to scale up and down depending on the actual input volumes.

03. Results

Custom integration built in MuleSoft enabled us to create a fast data loading solution that identifies, transforms and sends to AWS only the newly updates entries in Salesforce. The process incrementally populates the machine learning algorithms on regular basis keeping the artificial intelligence model and resulting reports always up-to-date. It also limits the required resources and cloud costs as only incremental updates. not full snapshots, are executed.

Testimonials

They have a very good company culture of their own, which gives them a real edge compared to other providers.

CEO

Leading UK system integrator

They're very skilled technically and are also able to see the bigger picture.

Managing Partner

Scalable SaaS for healthcare IoT built on Salesforce platform

They've been consistently able to deliver work on time and within budget.

CTO

High performance SaaS for financial insitutions

We are seriously impressed by the quality and broader picture of anything they do for us.

CEO

Gold trading platform

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