0 %
Connect Now

Real Business Impact in Action

See how our platform and services have helped customers transform data into insight, enable AI, and drive business outcomes.

Predictive Maintenance for Water Treatment Plant

Business Problem

A leading water treatment plant faced significant operational challenges with its large fleet of industrial pump-motors. Unpredictable pump failures led to costly downtime and emergency maintenance.

Technical Approach

The plant lacked a centralized system to monitor pump performance in real-time, making it impossible to perform analytics and enable predictive maintenance. Proposed a Industrial IOT platform to collect the data and analyze and descriptive and predictive maintenance.

Technical Solution

Edge Gateway

Implemented gateway and deployed at the plant to securely collect real-time sensor data from each pump-motor, including vibration, temperature, and energy consumption.

Platform Backend

  • Implemented an AMQP endpoint to ingest the collected data from the gateway installed at the plant.
  • The data pipeline uses a Lambda architecture with hot path and cold path to process the ingested telemetry.
  • The cold path of the data is essentially a data lake which stores data in parquet format in Azure ADLS for offline data analysis and building predictive models.
  • Predictive models are deployed and supplied with the incoming telemetry. Dashboards are created for real time monitoring, alerts and notifications.

Technologies

  • PiiLink Edge Gateway
  • Azure, EventHub, ADLS, Azure Functions, Azure ML
  • ML Algorithms – Isolation Forest, NBEATSModel, Chronos
  • Azure AD, TimescaleDB, Grafana

Key Results

  • Predictive Analytics: The system now predicts pump-motor failure with 90% accuracy up to 14 days in advance, allowing for planned maintenance.
  • Reduced Unscheduled Downtime: Proactive maintenance has led to a 20% reduction in unplanned shutdowns.
  • Optimized Maintenance Costs: The plant has optimized spare part inventory and maintenance scheduling, leading to 15% lower operational costs.

Datalake + Chatbot for Mining

Business Problem

A leading mining customer produces various data like daily production, equipment log, maintenance records, quality control data etc. Customer lacked a unified view of their operations to optimize efficiency and performance. They also wanted to converse with the system using Natural Language.

Technical Approach

Proposed and implemented a centralized, cloud-native data lake using Trino as the query engine and MinIO for datalake storage. This architecture unifies data from all source systems into a single source of truth. Also enabled RAG pipeline for a GPT Chatbot.

Technical Solution

Centralized Data Lake

  • Designed and implemented a centralized, cloud-native data lake using Trino as the query engine and MinIO for datalake storage.
  • Automated daily ingestion pipelines, built with Apache Spark, perform ETL process to convert raw data (e.g., Excel sheets) into Parquet files.
  • Data is stored in MinIO, a scalable S3-compatible object storage. We implemented a Hive-style partitioning scheme (year/month/day) for optimized query performance.
  • Query Engine: Trino connects directly to the Parquet data in MinIO, enabling fast, ad-hoc SQL queries and powerful analytics
  • Implemented and connected a Text2SQL RAG pipeline with a chat interface for conversing in Natural Language.

Technologies

  • Kubernetes
  • Apache Spark, Apache Trino, MinIO
  • LangChain, RAG, LLMs, OpenAI
  • Azure AD, Grafana

Key Results

  • Accelerated Insights: Data that previously took days to gather is now available for analysis in near real-time.
  • Improved Efficiency: Enabled analysts to quickly identify high-downtime equipment, low-grade ore batches, and opportunities to optimize production.
  • Unified Reporting: Provided a single, consistent data foundation for all operational and executive dashboards.
  • Scalability: The architecture is scalable to handle future data growth from new sensors and operational systems.

Data Platform Modernization for Media Client

Business Problem

Client, a leader in media data and analytics, faced significant challenges with its legacy data platform. The current infrastructure struggled with data silos, growing data volumes, slower partner data integration and ineffective AI/ML utilization.

Technical Approach

Proposed and implemented an modern data platform based on Lakehouse data architecture. The proposed Medallion architecture ensures data quality and consistency as it moves from raw ingestion to a state of being ready for consumption.

Technical Solution
  • Phased Implementation: We structured the modernization into logical phases to ensure a smooth transition, mitigate risks, and deliver incremental business value at each stage.
  • Data Lakehouse Foundation: We implemented a multi-layered Medallion architecture to create a scalable and reliable data repository.
  • Intelligent Data Fabric Layer: We built an overarching, intelligent layer on top of the Lakehouse to unify data access and provide context.
  • Operationalized Data as a Product: We fostered a "data as a product" paradigm by designing data assets with end-users in mind.
  • Enabled Advanced Analytics and AI/ML: We provided a dedicated environment for advanced analytics and AI/ML model development and deployment. This included adopting MLOps practices to automate and streamline the entire machine learning lifecycle, from model training to continuous monitoring in production.

Technologies

  • AWS Glue, AWS API Gateway
  • Amazon S3, AWS Glue/EMR, Amazon Athena
  • AWS Glue Data Catalog, Amazon Neptune
  • Amazon QuickSight, Amazon SageMaker, AWS API Gateway (Data APIs)

Key Results

  • Unified Intelligence: Eliminates data silos to create a single source of truth, enabling holistic insights for media planning and consumer research.
  • Accelerated Innovation: Fosters an agile, data-as-a-product culture, accelerating time-to-market for new data offerings and partner integrations.
  • Operational Efficiency: Automates data pipelines and standardizes API exposure, reducing manual effort and lowering operational costs.
  • Enhanced AI/ML Capabilities: Provides a robust foundation for building advanced AI/ML models for hyper-personalization, dynamic ad pricing, and predictive analytics.