How Streamlined Data Integration & Automated their Business Process with

In this case study, we will explore how, a real-time image transformation, optimization and digital assets management, was able to streamline its data integration and automate its business processes with the help of By leveraging's powerful capabilities, overcame its data integration challenges and achieved greater efficiency and productivity in its day-to-day operations. is an AI-based image transformation platform and digital asset management tool that provides simple tools for storing and transforming high-quality images. You can use this platform for multiple purposes, such as resizing images, applying filters, sharpening images, removing watermarks, removing backgrounds, bulk transformation, and much more. also offers real-time image transformations, optimizations, and digital asset management to deliver one-of-a-kind visual experiences and better engagement on the web. You can easily integrate with your existing system using its extensible APIs and leverage its AI technology to enhance image transformations for the best visual outcomes. A large user community trusts and has a flexible pricing plan to suit your needs. had a complex data integration process that required manual intervention, resulting in delays and errors. The company had to deal with data from multiple sources, including databases, applications, and systems. This made it difficult to consolidate data, leading to inconsistencies and inaccuracies. needed a solution to streamline its data integration process, improve accuracy, and automate its business process. is a powerful data integration platform that enables businesses to streamline their data integration process and automate their business operations. It is a cloud-based solution that seamlessly connects different data sources, including databases, applications, and systems, into a single data warehouse in real time. is compatible with various data destinations, including Google BigQuery, Amazon Redshift, and Snowflake, allowing businesses to choose the platform that best suits their needs.

Challenges faced by

Challenges faced by

1. Manual data entry and error-prone process

They had a manual data entry process to import customer data from various sources, like web forms, CRM, etc., into their database. This was time-consuming and led to errors and data duplication.

2. Lack of data integration: 

They needed a proper data integration process to sync data between their CRM, marketing, support, and billing tools. This resulted in data silos and inconsistent information across systems.

3. Tedious workflows: 

Many of the internal processes at, like onboarding a new customer, billing, issuing refunds, etc., involved multiple manual steps across departments. This made the workflows long, tedious, and inefficient.

4. Limited scalability: 

The manual and disjointed processes at were challenging to scale as the company grew. They needed to automate and streamline their data and workflows for future scaling.

5. Dashboarding challenges: 

They found it challenging to get a single view of their key metrics and KPIs to track the health and growth of their business. They needed a unified dashboard.

6. Reporting hassles: 

Creating reports around their customer, sales, and financial data took time due to a lack of centralization and integration of their data from various sources. Daily and monthly reporting was painful.

7. Lack of business insights: 

Due to challenges in aggregating and analysing data spread across multiple tools and systems, they needed to gain data-driven insights into their business. They needed advanced analytics and reporting capabilities.'s previous data integration systems & processes were not adequate for the following reasons:

  • They relied on manual data entry, which is error-prone and time-consuming. This led to data duplication, incomplete data, and data inaccuracy.
  • They lacked a proper data integration platform to automatically sync customer data between their CRM, marketing, billing, and other tools. This resulted in data silos, with each system having some information but no single source of truth.
  • Their data was unorganised and scattered across many tools, spreadsheets, and databases. This made getting a holistic view of their business and customers difficult.
  • Their internal processes spanned many disconnected tools and required manual intervention. This increased the scope for errors, made the workflows inefficient, and reduced productivity.
  • They did not have a unified dashboard to monitor key metrics and track their business performance. Tracking KPIs was difficult without centralised data and reporting.
  • Creating reports was tedious due to the lack of data integration and depended on manual reporting methods. This made decision-making and planning challenging.
  • They could not gain valuable insights into their customers, sales, marketing, and operations without aggregating and analysing their data from different sources.
  • Their data infrastructure and systems were not scalable and would eventually be unable to handle growth in data volume and complexity as the business scaled.
  • Their legacy data and process architecture posed security and compliance risks due to a lack of controls and auditing capabilities.

In summary,'s existing data systems needed to be more cohesive, manual, and able to meet the company's growing data and process needs. They needed a modern data integration and automation platform to address these challenges.

The implementation of helped solve its data & business process challenges in the following ways:

  • Automated data integration: provided an automated platform to integrate customer data from their CRM, marketing, billing, and other tools into a central data warehouse. This eliminated manual data entry and ensured clean, complete, and accurate data.
  • A single source of truth: By integrating their data across systems into, gained a single source of truth about their customers and business. This solved the problem of data silos and conflicting information.
  • Streamlined workflows: automated many internal workflows like customer onboarding, billing, refunds, etc., across's departments. This made the processes fast, efficient, and error-free.
  • Unified dashboard: used's dashboard to monitor key metrics, such as new customers, MRR, churn, LTV, etc., in one place. This gave them visibility into the health and performance of their business.
  • Advanced reporting:'s reporting features allowed to create financial, sales, marketing, and customer reports with a few clicks. Daily, weekly, and monthly reporting became easy.
  • Data-driven insights: With their data integrated and centralised in, was able to take advantage of features like data visualisation, analytics, and predictive modelling to gain valuable insights into their business.
  • Future-proof scalability: Using, implemented a data infrastructure that could easily handle increasing data volumes, new data sources, and additional use cases as the company scaled.
  • Enhanced security and compliance: allowed to strengthen its data security and governance through features like access control, encryption, audit logs, etc.
  • Productivity and cost gains: With process automation and reduced manual effort, was able to lower costs, improve productivity, and increase the speed of its operations.

How Boltic helps was a fast-growing tech startup allowing people to transform images with advanced AI tools quickly. While their product was gaining popularity, internally, the company needed help with many data and process challenges. Their data was scattered across many systems, and most of their internal processes were manual, requiring much time and effort. The data team at knew that to scale the company, they needed to streamline their data integration and automate their workflows. After evaluating many solutions, they found that was the perfect platform for their needs. was a robust data integration solution that could consolidate data from various sources and automate business processes. The team was very impressed with its capabilities, like data warehousing, reporting, analytics, dashboarding, and workflow automation. They decided was the ideal solution to tackle their challenges and future-proof their data infrastructure. implemented and started reaping the benefits quickly. automated integration of their customer data from tools like their CRM, marketing, and billing systems into their BigQuery data warehouse. This eliminated manual data entry, ensuring accurate and up-to-date data.

With data integrated into one place, gained a single source of truth about their business and customers. Data silos and conflicting information became a thing of the past. They set up automatic workflows in that handled processes like customer onboarding, billing, and refunds across departments. This reduced errors, improved efficiency, and lowered costs. Using's advanced dashboarding and reporting features, monitored key metrics and generated reports with just a few clicks. Daily reporting and performance tracking became fast and straightforward. Their executives and managers gained a holistic view of business performance and data-driven insights for better decision-making. proved to be a perfect solution for, solved their present needs, and ensured they had a scalable data foundation for future growth. By streamlining their data integration and process automation with, was able to improve productivity, reduce costs, increase the speed of operations, and provide an exceptional experience to their customers and employees., the one-stop data integration and automation platform, was instrumental in accelerating's success.

A detailed overview of the above case study

A detailed overview of the above case study

How Boltic helped at multiple points:

1) Use Case 1: Multiple Sources Integration needed help integrating data from their CRM, marketing, billing, and other tools into their BigQuery data warehouse. The manual process was tedious, time-consuming, and error-prone. automated the integration of data from's different sources into their BigQuery data warehouse. This eliminated redundancy and ensured that the data in BigQuery was accurate, consistent, and up-to-date. With a unified data warehouse in BigQuery, gained a single source of truth about their business and customers. Data silos were removed, and different departments had access to the same information.'s simple data integration interface made configuring new data sources and destinations accessible without technical complexity.

2) Use Case 2: Reporting had to manually compile reports from their data in BigQuery and send them to clients regularly. This manual reporting process could have been more efficient, tedious, and prone to errors. automated's reporting process. It could generate scheduled reports from the BigQuery data warehouse and distribute them to clients directly via email, Slack, and Gecko. had to configure the report details, scheduling, and delivery channels in With automated reporting, eliminated the hours spent on manual reporting each week. Report generation and distribution happened instantly without any errors. Clients received their reports on time through their preferred channels.'s reporting became faster, cheaper, and more scalable with

3) Use Case 3:Dashboard Creation.'s team had to manually create dashboards to share data insights from their BigQuery data warehouse with clients and executives. The process was challenging and time-consuming.'s dashboarding feature automatically created pixel-perfect dashboards from data in BigQuery that could be shared instantly with the required users. had to choose the metrics, charts, and filters they wanted to include, and rendered an interactive dashboard with that information.'s automated dashboard generation saved's team hours of effort. Data-sharing through dashboards became fast, and clients could self-serve their data insights on demand. Executives and managers also received data-rich dashboards to track key metrics and performance. Dashboard creation was no longer a bottleneck, thanks to

4) Use Case 4: Data Transfer needed to frequently transfer customer data from their BigQuery data warehouse to their Freshsales CRM tool. However, the manual data export and import process was prone to errors and time-consuming. automated the transfer of customer data from BigQuery to Freshsales. It securely extracted the required data from BigQuery and imported it into Freshsales without any manual work needed. Schedules and transformations could be set up once, and the data transfer would keep running without issues. With,'s data transfers between systems became fast, accurate, and reliable. Their data across tools was synchronised and up-to-date. Employees could spend their time on high-value tasks instead of mundane data transfers. streamlined's data operations and improved productivity.

Benefits of using for's data integration

Benefits of using for's data integration

1. Automated data integration from multiple sources: automatically integrated data from's CRM, marketing, billing, and other tools into their BigQuery data warehouse. This eliminated manual data entry and ensured accurate, up-to-date data.

2. A single source of truth:

By consolidating data into BigQuery, gained one single source of accurate information about their business and customers. Data silos and inconsistencies were removed.

3. Scalable infrastructure: provided a flexible data integration platform to efficiently handle new data sources and higher data volumes as's business grew.

4. Enhanced productivity: allowed's team to focus on high-value tasks by automating data transfers and eliminating redundant manual effort. This improved productivity and reduced costs.

5. Tightened security:

Through its robust security features, strengthened data security, access control, logging, and compliance for

6. Simple interface: provides an intuitive self-service interface to configure and manage data integrations without technical complexity. Both business and IT users found it easy to set up and operate.

What results have they achieved

What results have they achieved

Quantitative results:

  • 80% reduction in manual data entry and transfers
  • 70% decrease in time spent on reporting
  • 50% cost savings from improved productivity and efficiency

Qualitative results:

  • Clean, consistent, and accurate data across systems
  • Streamlined internal processes with automated workflows
  • Data-driven decision-making from advanced reporting and analytics
  • Improved customer experience from faster responses and rich data insights
  • Future-proof scalable infrastructure to support growth

Explanation of how each use case helped improve's data integration:

  • Use Case 1: Automated integrating data from multiple sources into BigQuery. Gained a single source of truth and overcame data silos.
  • Use Case 2: Automated the reporting process. Reduced time spent on manual reporting by 70% and improved report accuracy. Faster client report distribution.
  • Use Case 3: Automated dashboard creation from BigQuery data. Made data sharing fast and interactive through self-serve dashboards. Saved hours of manual effort.
  • Use Case 4: Automated the transfer of data between BigQuery and Freshsales. Eliminated manual data export and import while ensuring accurate, up-to-date data across systems. Improved productivity.

Comparison of the previous data integration system and the current one with

Previous system:

  • Manual data transfers between systems leading to high effort, errors, and data issues
  • Siloed customer data across tools with no single source of truth
  • Tedious manual reporting and dashboarding processes that were difficult to scale
  • Lacked audit trails, compliance, and robust security for data
  • Required extensive technical resources to operate and manage


  • Automated data integration provides clean, consistent data across systems
  • Consolidated data warehouse in BigQuery forms a single source of truth
  • Automatically generated reports, dashboards, and data transfers minimise manual work
  • Compliant and secure platform with rich access controls and audit logs
  • Intuitive self-service interface is easy for both technical and non-technical users

How helped improve overall business operations

  • Accurate, up-to-date data across systems improved decision-making and planning
  • Streamlined processes, optimised costs, increased the speed of operations, and reduced errors
  • Scalable data infrastructure enabled fast responses, rich data insights, and supported growth
  • Advanced analytics and reporting provided data-driven insights to gain a competitive edge
  • Dashboards made vital metrics and performance visible instantly to facilitate action
  • Less time spent on mundane tasks allowed more focus on high-value activities
  • Enhanced customer experience from quick, personalised responses and self-serve options
  • Tightened security and compliance to ensure the protection of data and business interests

In summary, transformed's data operations with automation and integration. It enhanced productivity, optimised costs, and unlocked new opportunities for innovation and progress. was the one-stop solution that accelerated's success.


By leveraging for its data integration needs, Pixelbin was able to streamline its business processes and gain significant efficiencies. allowed Pixelbin to automate data ingestion from various sources into their data warehouse, reducing manual effort and ensuring high data quality. Pixelbin plans to expand its use of to additional use cases. The self-service interface and robust functionality of lend themselves to handling more complex data pipelines and automation. Pixelbin is eager to use to optimise its data-driven decisions and gain a competitive advantage. has proven to be a scalable and robust solution for Pixelbin's data integration challenges. With handling most of their ETL processes, the Pixelbin team can focus on high-impact and strategic work rather than mundane data processing tasks. The partnership with is set to grow even more vital as Pixelbin's data and analytics needs evolve.


What was the previous data integration system used by previously used a combination of manual data processing and scripts to handle their data integration needs. They needed a reliable data integration tool or platform, leading to inefficient processes and poor data quality.

How did help improve data integration for provided with a robust, self-service data integration platform that automated their ETL process. ingested data from multiple sources, mapped, cleaned, transformed, and loaded it into their data warehouse. This eliminated the need for manual data handling and ensured timely, high-quality data for analytics.

How long did it take to implement for

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The initial implementation of for the core ETL processes took around three days for This included connectivity to data sources, mapping data fields, setting up transformation logic, and loading the data into the warehouse. Fine-tuning and optimizations are ongoing to improve the ETL pipelines further.

What were the challenges faced during the implementation process?

The biggest challenges revolved around connecting to some data sources and handling inconsistencies in data formats and schemas. The team worked closely with to address these challenges through custom connectors and data cleansing tools. Some workflow automation also took a few iterations to perfect.

What kind of technical expertise is required to implement for data integration? is designed to be used by both technical and non-technical business users. However, some technical resources are helpful during the initial implementation. Resources with SQL skills, data profiling and mapping experience, and workflow automation knowledge will help get the most out of the platform. also provides excellent support and documentation to assist customers.

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