TL;DR

Praxis AI builds manufacturing-specific agents capable of reasoning and processing complex situations. Our engineering copilot predicts and prevents machine failures in real-time by solving problems the same way an engineer would. This results in proactive maintenance which helps prevent unplanned downtimes and quality defects.

Launch Video:

The Problem

We have over 10 years of experience working in the manufacturing industry between the two of us. During that time we have noticed that manufacturing engineers are constantly being pulled in every direction due to production issues popping up in multiple parts of the plant.

This reactive approach results in:

• Large amounts of unplanned downtime, resulting in lost throughput
• Unnecessary wear and tear on expensive manufacturing machinery
• Quality defects requiring non-value add rework prior to customer delivery

From unplanned downtimes alone, US manufacturers lose over $50 billion dollars per year. 

Unfortunately, existing solutions are:

• Complex and required large amounts of data science/computer science knowledge
• Focused on dashboarding and KPI metrics
• Extremely costly to implement and maintain
• Not tailored for manufacturing use cases

The Solution

Praxis AI provides manufacturing engineers with an end-to-end solution for identifying and root-causing machine issues by following a 5-step reasoning process. This mimics exactly how an engineer would solve an issue in the real world. Our platforms core competencies include:

1. SOTA AI architectures for manufacturing - Our models have been specifically designed to work with time series data coming from the manufacturing shop floor.
2. No code tools for manufacturing engineers - The Praxis AI platform can be used without ever touching a Jupyter notebook.
3. Explainable AI for root cause analysis - Our UI gives users full visibility into the reasoning behind the output of our models, and our copilot Max.e.
4. Operationalized machine learning models - Praxis ensures that every model that is built is able to be utilized every single day to drive actual changes on the shop-floor. It's not just a pretty dashboard to look at.

The Praxis platform provides the tools needed for engineers to make changes on their shop-floor which can lead to reductions in unplanned downtimes, reduced scrap/waste, improvements on the efficiency of their machines, and so much more. We’ve built the platform in a way that as we continue to work with more customers, we will be able to encompass more and more use cases. We’ve already begun preliminary work on adding production order optimization and maintenance backlog management to our solution suite.

The Team

We have been working in the manufacturing industry for our entire professional career, and are extremely passionate about being on the cutting edge of manufacturing technology.

Ransika has worked at multiple manufacturing companies like GM, Tesla, and Rockwell Automation. During this time he has worked a variety of foundational manufacturing roles like manufacturing engineer, quality engineer, controls engineer, and production planner. During this time he managed multiple projects building and implementing various digital manufacturing projects globally which resulted in millions of dollars in cost savings per year. For the last two years he has been working at Deloitte as a product lead for Deloitte’s Intelligent Ops platform.

Aditya worked at Palantir for three years, ending his career there as a technical lead. During this time he specialized in converting pilots in the manufacturing vertical with a 100% success rate and a TCV of over $15 million dollars. Aditya has had a passion for ML/AI in manufacturing since early college, first excelling in the space by building a predictive model for General Motors and winning first in a data challenge of over 50 teams of PhD and masters students.

Our Ask

If you are interested in testing our solution out, we would love to be in touch to do a free factory assessment! We are looking to test our solution in a variety of different manufacturing environments, but have a particular affinity to continuous processes that can be found at paper mills, oil refineries, and chemical plants. Feel free to reach out at founders@praxis-tech.ai. Thanks!