When you can’t afford to make mistakes.

Intelsat 804 Satellite
“We have a concerning sensor reading. Can you tell me the cause of this failure and indicate corrective action?”
LLM
“Determining the exact component failure would typically require expertise in satellite engineering and operations…I can provide some general insights…"

You need an AI model with exact, verified solutions—determined in milliseconds.

Intelsat 804 Satellite
“We have a concerning sensor reading. Can you tell me the cause of this failure and indicate corrective action?”
Pilogic
DIAGNOSIS: “The most likely explanation is a stuck-open failure in relay 5 with probability 0.941."
AUTOMATED ACTION: "Open Relay 4 and close Relay 2 to bypass Relay 5 … rerun diagnostics."

“We’re building Targeted AI.
Efficient AI. Precise AI.

We don't do what LLMs do. You can ask an LLM virtually any question, and it’ll give back an intelligent answer. The tradeoff is they are computationally expensive, and their answers to precise questions are sometimes not very precise.

That’s where we’re focusing.  When we build a model, it answers just the questions for which it was built, in a particular domain. Our models are orders of magnitude less computationally expensive than something like an LLM. And the models are precise.

What this means is that they produce answers that are mathematically grounded in probability theory. It also means that domain experts can interact with the models in powerful ways, like injecting knowledge to enhance training effectiveness, setting hard constraints on model behavior, and analyzing such behavior. And we do all this in a way that scales. ”

Mark Chavira

Founder & CTO

Former director of AI at Google

Current AI does not address the accuracy, speed, or computational efficiency needed in key industries.

  • LLM's

    Deep neural networks with extensive parameters, trained on massive data sets of human produced text.

    Key Industries:

    Marketing
    Sales
    Graphic Design
    Research

  • PiLogic

    Fast, mathematically grounded AI. Trained on specific problems by industry experts to solve the hardest problems.

    Key Industries:

    Cybersecurity
    Aerospace
    Data Centers
    Finance

When you can’t be wrong

  • Accurate

    Precise Answers with zero hallucinations, utilizing Probabilistic Inference.

  • Fast

    Up to 10,000x faster than current market leading models.

  • Efficient

    Minimal compute required, allowing for operation at the edge on all relevant devices.

PiLogic solves the world’s most critical problems, orders of magnitude faster…

…converting previously unmanageable problems into routine industry standards.

PiLogic solves the world’s most critical problems, orders of magnitude faster…

…converting previously unmanageable problems into routine industry standards.

A Purpose Built,
Standalone AI Platform

Informed decisions under uncertainty using mathematical principles of probability.

  • Problem

    PiLogic starts with a problem requiring precision, like diagnosing faults in a spacecraft.

  • Probabilistic Model

    The model defines variables, logical relationships, and probabilistic relationships and leverages domain knowledge to maximize training effectiveness.

  • Computational AI

    PiLogic applies its state-of-the-art probabilistic and logical AI to transform the probabilistic model into an efficient and exact computational model, solving in an instant.

Press Inquiries

Leadership

Mark Chavira

Founder & CTO

Mark has worked for over 30 years building real-time systems and AI solutions and managing large teams of engineers. He earned a PhD from UCLA in probabilistic inference, a subfield of AI. During this time, he published numerous advances, incorporated the work into an inference engine called Ace, and collaborated with NASA to apply these techniques to diagnosis of electrical system failures in spacecraft. Mark spent 12 years at Hughes Aircraft and Raytheon designing and building real-time avionics operating systems from scratch, which gave Mark expertise in systems that are fast, predictable, and reliable.

Mark spent 13 years at Google, where he became an Engineering Director, leading a team of engineers and linguists to build probabilistic inference and ML systems that control many aspects of ad targeting, ad blocking, and more. Mark lives in Los Angeles with his wife and seven children.

Johannes Waldstein

Founder & CEO

Johannes has co-founded and led five fast moving startups over the past 20 years that developed new markets for data science and AI innovations in Europe and North America, growing these companies from initial idea to scalable product market fit. He founded Fan.AI, an AI driven adtech company, and led the VC fundraising for the company, raising over $15m from venture investors.

He has a deep interest in automated reasoning and has worked for years with mathematically grounded AI models. He is an endurance athlete, ironman triathlete, and ultra-distance trail runner. He lives in Los Angeles with his wife and 5 children. 

Geoff Bough

Co-Founder & CRO

Geoff has spent over 15 years in business development, sales and marketing. He cut his teeth in the startup world as the first US hire for FanDuel, overseeing business development and helping the company secure game-changing partnerships that were the building blocks for what is today the largest online gaming company in America.

Additionally he has held executive roles at Flutter, Caesars and Triller, the latter of which he helped complete a $4B merger. Geoff has also always had an entrepreneurial streak, from investing in early stage companies to running his own branding and marketing agency that secured over $500M in strategic partnerships for clients. Geoff lives in the Bay Area with his wife and two children.

  • Mark Chavira

    Founder and CTO

    Mark has worked for over 30 years building real-time systems and AI solutions and managing large teams of engineers. He earned a PhD from UCLA in probabilistic inference, a subfield of AI. During this time, he published numerous advances, incorporated the work into an inference engine called Ace, and collaborated with NASA to apply these techniques to diagnosis of electrical system failures in spacecraft. Mark spent 12 years at Hughes Aircraft and Raytheon designing and building real-time avionics operating systems from scratch, which gave Mark expertise in systems that are fast, predictable, and reliable. Mark spent 13 years at Google, where he became an Engineering Director, leading a team of engineers and linguists to build probabilistic inference and ML systems that control many aspects of ad targeting, ad blocking, and more. Mark lives in Los Angeles with his wife and seven children.

  • Johannes Waldstein

    Founder and CEO

    Johannes has co-founded and led five fast moving startups over the past 20 years that developed new markets for data science and AI innovations in Europe and North America, growing these companies from initial idea to scalable product market fit. He founded Fan.AI, an AI driven adtech company, and led the VC fundraising for the company, raising over $15m from venture investors. He has a deep interest in automated reasoning and has worked for years with mathematically grounded AI models. He is an endurance athlete, ironman triathlete, and ultra-distance trail runner. He lives in Los Angeles with his wife and 5 children.

  • Geoff Bough

    Founder and CRO

    Geoff has spent over 15 years in business development, sales and marketing. He cut his teeth in the startup world as the first US hire for FanDuel, overseeing business development and helping the company secure game-changing partnerships that were the building blocks for what is today the largest online gaming company in America. Additionally he has held executive roles at Flutter, Caesars and Triller, the latter of which he helped complete a $4B merger. Geoff has also always had an entrepreneurial streak, from investing in early stage companies to running his own branding and marketing agency that secured over $500M in strategic partnerships for clients. Geoff lives in the Bay Area with his wife and two children. 

FAQ

  1. How is PiLogic different than LLMs?

PiLogic and Probabilistic Inference employ probability theory to compute probabilities of events. The answers are mathematically grounded and therefore give very exact and precise answers. This is ideal when you can’t afford to get things wrong. Aerospace, cybersecurity and many other verticals have many use cases that need exact answers fast. A Large Language Model (LLM) is produced using a massive dataset. LLMs can respond to and generate natural language text, which makes them useful for a wide range of natural language processing tasks such as text summarization, question answering, and more.

  1. How is the PiLogic Model so much more efficient?

PiLogic has built an inference engine using structure in novel ways, upon which models are built for major use cases in specific market verticals. This distinctive approach has been independently verified as the fastest technique for performing probabilistic inference. PiLogic does not need enormous amounts of training data and can run on standard CPU hardware. This is because the model is so computationally efficient. The methods pioneered by PiLogic are a real breakthrough in speed, efficiency and ability to solve complex problems exactly.

  1. Will you expand beyond the focus beyond aerospace and cybersecurity?

The initial focus is on aerospace and cybersecurity. The platform and engine have capability well beyond those two verticals including, finance, healthcare, energy, data centers and more.

  1. Are you taking on new clients?

Yes. We are taking on new clients in aerospace and cybersecurity.