We're on a mission to make
explainable AI accessible.

For People.
People make better decisions when the truth is obvious. We already know that the utility of data is directly correlated to one's ability to explain that information. What's missing for model decisions is the ability to uncover the whys and hows, to give people an opportunity to make better decisions. Apres is built to be the interface layer between people and machines. A way to simply explain model-based decisions.
People
People
For Businesses.
The next phase of business success is predicated on incorporating and executing an AI strategy. AI applications require unique coordination between developers, data science and core operations. Today, it's hard to manage AI projects and even harder to gain insight into their success early and often. Apres adds transparency into this process, giving companies a much needed view into their data, wrapped in management tools that accelerate development. Helping companies succeed with AI helps everyone succeed.
People
For Machines.
For every critical system, we have an audit trail that explains who, what, when, where and why, a decision was made. What not for machine learning models? While the impact of AI-based decisions grows, the need for sustained and continuous audit grows with it. Apres unlocks this single-source of truth with the ability to monitor, validate and explain decisions continuously, from training to production. Creating a safer environment for unexplored possibilities.
People
We're on a mission to make explainable AI accessible.
Machine learning has created the most important technology shift happening over the next decade. Everything from our businesses to our daily lives will fundamentally change because of decisions that we charge to machine learning models. And it's already happening.

If machines will help make some of life's most critical decisions, there isn't a future where we can't ask why a decision was made.
Explainability is not an obvious or widely accepted strategy. Frankly, it's brushed aside believing that we will eventually trust models as much as any other technology. That the data for how or why a decision is made will matter very little.

When critical decisions are made, there's accountability. Where there's accountability, there must be explanation. We've built Apres to make models accountable. To give you a real view into machine decision making. To create a safer, more reliable future.
Our Founders
Our team comes from backgrounds in artificial intelligence and analytics - from Financial Services to Enterprise SaaS. Most importantly, we've collectively spent decades understanding model data to help make critical decisions.
Matt Waite
Founder & CEO
Subbu Balakrishnan
Founder & CTO
Mihovil Kovacevic
Founder & VP Engineering
Rahul Kumar
Founder & Chief AI Scientist
Our Advisors
We're supported by industry veterans.
Joshua Krammes
VP, Stackpath
Aditya Sarawgi
Engineer, Ampleforth
Randeep Bhatia
Data Science, Twitch
Rahul Kumar
Founder & Chief AI Scientist
Request a demoContact us
x