Poka Labs
The modern operating system for chemical manufacturing.
Poka Labs is developing an AI platform to automate operations tasks in chemical manufacturing, beginning with production scheduling. Traditionally, these tasks are performed manually using spreadsheets. Our software seamlessly integrates with existing data sources such as data historians, emails, and PDFs to automate analytics, scheduling, changes, and communication within a single platform. Malay and Andrew met while pursuing their MBAs at Harvard Business School. Malay previously worked as an engineer in the specialty chemical industry, saving his employers over $100 million across US, China, and German plants. Andrew worked as a software engineer on data infrastructure at Meta and optimization problems at a seed-stage startup. He also completed a Masters degree at Harvard specializing in Machine Learning.
Jobs at Poka Labs
Active Founders
Andrew Bass, Founder
Andrew is the Co-Founder and CTO of Poka Labs. Before Poka, he worked on critical data infrastructure systems at Meta, as well as optimization software at a Series A startup (acquired) that helped mobility companies boost their revenue 40%. He has both an MBA and MS degree at Harvard where he focused on ML systems.
Malay Shah, Founder
Malay is the Co-Founder and CEO of Poka Labs. He earned degrees in Chemical Engineering and Economics from NC State and an MBA from Harvard Business School. With a background in the specialty chemicals industry, Malay has held various positions in operations and engineering working across plants in US, Germany, and China. His work in deploying chemical expertise with advanced analytics has saved his previous employers tens of millions of dollars.
Company Launches
Summary:
- We help chemical manufacturers automate their production planning and scheduling without the need for spreadsheets or complex implementations
- Schedule a demo - https://www.pokalabs.com/
Hi everyone! We’re Andrew and Malay — the founders of Poka Labs.
❌ Problem
Production planning is the “brains” of chemical operations. Yet even today, the $5.6 trillion chemical industry relies on spreadsheets and humans to manage the process.
This leaves plants to constantly fight fires in order to meet customer orders on time and maximize manufacturing margins. Any change, such as inventory delays, labor shortages, or unexpected maintenance, further stresses operations. Existing systems, such as ERPs, are too rigid in their data inputs to adapt to these types of changes.
✅ Our Solution: Adaptive Production Planning with AI
Poka Labs integrates with existing information (data historians, emails, PDFs, ERPs, etc) to put chemical production scheduling on autopilot. Our software automates analytics, scheduling, changes, and communication within one platform that anyone can use.
With Poka Labs, chemical plants can:
- Maximize revenue
- Minimize transition costs
- Eliminate messy spreadsheets
- Increase visibility
🎉 Our Story
Malay is a chemical engineer who has worked in chemical plants across 3 continents. He experienced these issues firsthand and saved his prior employers over $100+ million on process improvement and production planning projects.
He met Andrew while they were pursuing their MBA at Harvard Business School. Andrew worked as a software engineer on data infrastructure systems at Meta before working on optimization problems at a seed-stage startup. He also completed a Masters degree at Harvard specializing in ML.
Together, we’re passionate about deploying modern software in the chemical industry.
❓ The Ask
- If you work in chemical manufacturing, we’d love to chat! Contact malay@pokalabs.com
- Share this post! Is there anyone in your network that deals with planning for chemicals? We’d love to chat with them
- Copy & paste blurb: A team of Harvard grads created an AI-powered production planning platform designed for the chemical industry. It automates scheduling, changes, and analytics in one platform. Put your plant on autopilot with Poka Labs. Contact malay@pokalabs.com to see a demo from the founders.
