Complere Infosystem

Building a data pipeline for bulk Data Migration to Salesforce

Building a Data Pipeline For Bulk Data Migration to Salesforce



Siloed Data: The company had data scattered across different departments, making it hard to access and share.

ETL Complexity: Managing Extract, Transform, Load (ETL) processes was complex, as the company lacks in-house expertise in handling these processes.

Maintainence & Monitering: ETL pipelines and data integrations required ongoing maintenance and monitoring t ensure they keep working smoothly. 

Resource Constraints: The company had limited resources, including skilled staff and technical infrastructure, which made it challenging to manage the onboarding and meeting timelines effectively. 


Complere create a metric for self-evaluation of team members. For example, we created a scripted email with giving metrics below:
By asking these questions to their team members, each member can analyze daily targets and work progress. After sending feedback, all data is directly sent to the owner. This automated email system built a strong connection between employer and employee to keep track of team members’ performance, goals, and productivity.

The Scope of the engagement included the following initiative.

Complere Solution benefited in many ways:


Complere builds an easy but effective automated system for team members to self-evaluate and send complete reports to the owner to track the performance and productivity. When team members do a self-evaluation and share the work performance with the owner, it results in high productivity. This automated system helps clients track the performance of their team members and increase the team’s productivity. We’ve built this system, so it is easy for clients to send a scripted SMS with given metrics to the team members and their feedback directly forwarded to the owner to track the daily targets, performance, goals, and productivity in work.


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Puneet Taneja
CPO (Chief Planning Officer)


years on market


of handled transactions


markets served


Germany, Cologne


Partnership period:

April 2018 - present


Machine Learning models




MLOps best practices

as part of the development process

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