Real projects where we turned data into better decisions, greater efficiency and measurable growth for our clients.
Solutions with real impact
Each case shows how we combine technology, data and business context to solve concrete problems. From AI-driven process automation to data platform modernisation, this is what Pi Consulting looks like in practice.
AI & Development
A city government received over 40,000 citizen requests per month that had to be classified and routed to 63 different entities. The process was manual, costly and error-prone.
AI & Development
A national health superintendency received thousands of legal health claims every day, making fast classification and efficient resource allocation extremely difficult.
Analytics
A poultry company could not determine the optimal commercialisation day for its birds, relying on empirical decisions that created variability and economic losses.
AI & Development
A bank's legal department received hundreds of daily emails containing court notifications. Manual classification caused delays, errors and the risk of missing critical information.
AI & Development
Entrepreneurs managed sales through notebooks, photos and voice notes scattered across multiple payment channels, with no centralised view of performance.
AI & Development
Independent workers did not qualify under traditional credit scoring systems, losing access to financing despite having real repayment capacity.
AI & Development
Food court customers faced long queues and limited interaction with staff, creating dissatisfaction and lost sales to competitors.
AI & Development
A food company's procurement team could not anticipate global events that would affect commodity prices. News collection was manual and limited in scope.
AI & Development
A large business group needed to accelerate AI adoption while ensuring business relevance, feasibility and return on investment. Traditional innovation models were slow and disconnected from operations.
Analytics
Waste control in production was inefficient, with limited process visibility and data scattered across legacy systems.
AI & Development
Plant-floor operators lacked automated support, leading to inefficiencies and operational errors in machine handling.
AI & Development
Quality policies were not centralised or automated, making it difficult to maintain consistency, traceability and control.
Analytics
High data volumes and limited automation in taxpayer debt and payment processes led to manual reporting and dependence on outdated tools.
Data Management
The migration of a data warehouse from Teradata to GCP was at risk due to weak coordination, deadline pressure and potential service disruption.
Data Management
Teams had difficulty accessing data in the Data Lake and using it consistently across multiple services.
Data Management
There was limited visibility into the availability and governance status of datasets published in Unity Catalog.
Data Management
There was little visibility into Data Lake maintenance costs, and no capacity monitoring tools were in place.
Data Management
Internal consumers needed datasets to be available in their native services, including Kafka, PostgreSQL and EventHub.
Let's talk about how we can turn it into a concrete business outcome.
Contact us