Blendr.io case study
AI-driven HR Tech company dramatically improves the quality of its recommendations by pulling data from multiple sources
About Blendr.io
Blendr.io is an embedded integration platform for SaaS companies.
Blendr.io helps AI companies to add a wide range of integrations to their platform. These integrations can either capture data from multiple data sources (cloud applications) to feed the AI engine, or they can send results from the AI platform to other cloud applications. Since AI relies heavily on data, these integrations are a key element of the AI solution.
About the Blendr.io customer
The Blendr.io customer is a fast-growing AI company in the HR Tech space. The company applies AI on employee data to improve the productivity of Fortune 500 enterprises.
The integration challenge
The company was feeding their AI engine with data gathered from their own productivity tool. The company was looking for a solution to collect more data. The goal was to pull new data from other cloud applications as well, such as productivity tools, CRM and HR / ATS software etc. in order to provide more learning data to their AI engine.
To build all these integrations, the customer needed to engage its internal development team. “Classic” one-off custom development turned out to be very costly, hard to maintain, and impossible to customize for individual customers. Further more, limited knowledge on the various destination platforms, and no readily available test accounts significantly delayed the creation of these integrations.
AI integration scenario and requirements
The AI company needed to extract large amounts of data from various cloud tools, including CRMs, HR and ATS software etc. The requirements included:
Support for a large number of cloud applications
Ability to customize an integration for a specific enterprise customer
Support for custom fields
Ability to add custom logic to the data flows
How the integrations were built on Blendr.io
Step 1: Training and support by Blendr.io
Step 2: Creation and testing done by the customer’s solution engineers
Step 3: Test environments provided by Blendr.io
Step 4: Integrations go live. Lead time: 4 to 6 weeks
Integration activation per end-customer
The SaaS company applies the “full-service delivery model” to activate integrations for individual customers.
This means that the Customer Success Team of the AI Tech provider takes care of the activation of the integrations for each enterprise customers. A Customer Success Engineer goes through a user-friendly Setup Wizard in Blendr.io, to activate a new integration in 4 steps:
Who does what
Step 1: Send a secure “Invite” email to the end-customer, to request credentials to access the CRM, productivity tools and other platforms (e.g. Hubspot, Salesforce, MS Dynamics, Zendesk, etc.). Once the customer authorizes access, the new data sources are automatically connected.
Step 2: Apply specific “field mapping”: link custom fields in the AI platform to custom fields in the CRM.
Step 3: Choose the “matching” behavior in the AI platform and the CRM: e.g. match employees based on email, or a combination of name & email, etc.
Step 4: The integration is live and new data is constantly flowing into the AI engine.
Results
Avoiding the “cold start” problem when onboarding new customers. The training data is pulled from existing data sources to feed the AI engine.
3-5 months lead time saved per customer by building integrations with Blendr.io instead of doing custom development.
A dramatic increase in the quality of the outcome of the AI engine.
The company now wishes to deploy a large number of connectors to various cloud applications.
The AI tech provider closes more deals by delivering enterprise-grade integrations that prospects are asking for.
