A start-up developing a pioneer Automated AI for Conversation Intelligence was hugely aware that ETL processes are the Heart of their product that can make or break its market traction.
Company’s founding idea is bold, and as remote work becomes increasingly popular, it also has a chance for a good market fit. The product’s core feature is to attend and transcribe Zoom and Google meetings automatically.
This bot can be delegated to any call as a “passive participant” and transcribe meetings for those who can’t be there.
Case Study Shortcut
Challenge
Lack of Mature and Standardized ETL Processes
The company was still in the early development stage, and many ETL pipelines were either incomplete or ad hoc. This made it difficult to perform a traditional audit and highlighted the need to restructure the dataflows from the ground up for long-term stability.
Scalability Concerns for Future Data Growth
The product’s success depended on processing large volumes of meeting data from platforms like Zoom and Google Meet. The existing ETL setup was not prepared to handle scaling, especially with the addition of multiple data sources or rapid user growth, posing a risk to product performance.
Technology and Tooling Decisions Could Become Bottlenecks
Without clear guidance, the choice of ETL tools, frameworks, and cloud infrastructure could lead to technical debt. The team needed strategic consulting to ensure that foundational decisions would support future flexibility, cost-efficiency, and extensibility.
Goal
The company’s goal was straightforward:
- The company had a great and very ambitious idea. To transform it into a functioning product - it wanted to double-check all core elements, which were, in this case, the ETL processes.
Outcome
After embracing the consultant approach, we dived deep into company’s business logic. It ran through the long-term strategic plans for further development of this product to detect whether some dataflow bottlenecks might stop or slow down its different implementation.
Before
- ETL processes were partially implemented, inconsistent, and not fully documented.
- The startup lacked clarity on whether its current data infrastructure could scale to support rapid growth and integration of additional data sources.
- No formal validation of core architectural assumptions had been made, leading to potential inefficiencies and risks.
- Decisions around tools and frameworks were made without a long-term scalability and cost-efficiency lens.
After
- A full ETL audit and consultation enabled the team to validate early architectural decisions and restructure pipelines for future growth.
- ETL processes were cleaned up, optimized, and redesigned to support larger data volumes and additional source integrations without major refactoring.
- The startup gained a clear roadmap for evolving its data infrastructure, reducing future risk and enabling cost-effective scaling.
- Addepto’s shift from auditors to consultants allowed for strategic guidance tailored to a dynamic startup environment.
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Case Study Details
Approach
Shifting from Audit to Strategic Consultation
- Initially brought in to audit existing ETL processes, Addepto quickly recognized that the product was still in a fluid, early-stage environment.
- The team pivoted from a traditional audit to a consultative approach, helping the client align its data infrastructure with future business goals.
Cleaning and Restructuring Existing ETL Pipelines
- Evaluated and refined the client’s current ETL pipelines, addressing inconsistencies, manual gaps, and potential failure points.
- Established standardized practices for data ingestion, transformation, and storage to ensure data consistency and reliability.
Designing Future-Ready Dataflows
- Developed a scalable architecture capable of handling growing volumes of data from multiple meeting platforms (e.g., Zoom, Google Meet).
- Created a modular ETL framework that allows easy integration of new data sources without disrupting existing operations.
Technology and Toolset Advisory
- Provided guidance on selecting appropriate tools, frameworks, and cloud services that support scalability, automation, and cost efficiency.
- Helped the startup avoid early-stage technical debt by aligning infrastructure decisions with long-term product strategy.
Establishing a Dataflow Roadmap for Scale
- Delivered a clear roadmap outlining future data infrastructure improvements and optimization opportunities.
- Empowered the client to confidently scale its product with a robust, future-proof data backbone in place.
