Founded in 2010, Cranberry Analytics is a tech-enabled company operating in the water management space. Headquartered in Pune, the company was co-founded by Shishir Thakur, Amit Deshmukh and Onkar Gauridhar.
Cranberry Analytics was founded with the primary objective of measuring water efficiently. With the use of analytics, ML and IoT, the company augments government and large water infra companies with a tech stack to enable them to curb water wastage and plug revenue leakages.
In a conversation with Analytics India Magazine, Co-founder and CTO Shishir explained the technology behind Cranberry Analytics’ management system Recon, breaking down how AI and ML can facilitate the management of natural resources and what the future for AI in water management looks like.
Edited excerpts from the conversation:
AIM: What is your flagship product/service?
Shishir Thakur: Our flagship product is our water billing, budgeting and management system – Recon, deployed in PCMC (Pimpri Chinchwad Municipal Corporation) since 2012.
Recon integrates with all data sources related to:
- Water consumption
- Water distribution
- Demand management systems (billing)
- Revenue management systems (manual and digital)
- Customer experience and dispute resolution
It also provides a platform and dashboards to all the concerned personnel of the water department, citizens, and support providers.
AIM: How does Recon work?
Shishir Thakur: Recon has built-in capabilities to detect anomalies related to water usage and leakages, revenue leaks, automatic dispute resolution, tracking defaulters, predicting and forecasting water demand, tariff simulation, integrations with smart water meters, sensor management, accounts department and several other auxiliary functions. Using data visualisation in reports, charts and tables, Recon makes it very easy for top management to form policies and implement relevant changes that have the most impact.
AIM: Please explain the tech stack used by the team.
Shishir Thakur: For the Recon frontend, we use Angular.js, Charts.js, Tableau and Metabase. We primarily use node.js with some specialised algorithms in Golang and Python for anomaly detection and regression through past data to find patterns and forecasts for its backend.
- Databases include MySQL, MongoDB and Realm-DB.
- Mobile: Android (with Realm DB) integrates with onspot thermal bill printer and portable PoS machine for revenue collection.
- DevOps: Docker, Ansible, Jenkins, Nagios, Bitbucket.
AIM: How does Cranberry Analytics leverage AI and ML capabilities?
Shishir Thakur: We analyse all incoming data to find consumption related anomalies (similar to how a credit card fraud detection system works), and then alert the respective personnel. This has helped us identify many leakages, unauthorised consumption, geographic segmentation of faults based on geotags, and human errors.
Additionally, we leverage AI…