One of the most common problems I see as companies grow isn’t a lack of data

One of the most common problems I see as companies grow isn’t a lack of data — it’s too many disc...One of the most common problems I see as companies grow isn’t a lack of data — it’s too many disc...

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Cesar Ramos

• Jan 8

One of the most common problems I see as companies grow isn’t a lack of data — it’s too many disconnected reports.

In this project, leadership relied on spreadsheets and ad hoc reports. Numbers didn’t always line up, and answering basic questions took too long.

What I did:

Centralized reporting in Power BI

Defined clear, decision-driven KPIs

Modeled data so metrics reconciled across teams

Designed dashboards focused on clarity

The result:

Leadership gained a trusted, real-time view of performance they could rely on week after week.

Business Workflow Automation Apache Airflow Snowflake

ANIMESH SINGH

• Mar 4

Problem: Many organizations still process invoices manually by reading PDF documents and entering key details (invoice number, vendor, amount, etc.) into systems. This process is slow, error-prone, and difficult to scale, and it also makes it harder to detect duplicate invoices or incorrect totals.

Solution: This project builds an automated invoice processing pipeline that converts uploaded invoice PDFs into structured data. It uses OCR to extract text, LLMs to identify invoice fields, validation checks to ensure correctness, and Kafka-based event streaming to manage the processing pipeline. The extracted data is stored in PostgreSQL and visualized through a dashboard, enabling faster, scalable, and more reliable invoice processing.

AI Engineer Apache Airflow Kafka Python Data Engineering

Abhiram Tinguria

• Mar 4

Very real problem. Manual invoice processing is still surprisingly common and becomes a huge bottleneck as companies scale. Automating extraction and validation with AI can save a lot of operational time and reduce errors significantly.