PiLogic

Resolve tracks targets from radar returns using exact probabilistic inference. Post-mission analysis today; real time is on the roadmap.

The doctoral thesis behind the 2008 method. Its title names the problem the rest of this record addresses: exact inference that is not bounded by treewidth, the structural limit that stalled the field.

The paper that scaled compilation. Better logical encodings of a network's local structure cut compile times by an order of magnitude, compiled networks that had never been compiled before, and sped up online inference by orders of magnitude against jointree.

One of the three papers the 2008 reference builds on. How a network is written into logic decides how well a model counter can break the problem into independent parts.

Defines what it means to compile a Bayesian network together with its evidence, and shows the payoff in maximum-likelihood estimation, sensitivity analysis and MAP, with results from genetic linkage analysis.

A two-year CRADA applies Exact AI to diagnosing and predicting electrical and power failures on spacecraft platforms.

Forbes contributor Thomas Coughlin on PiLogic’s exact probabilistic inference, and why it needs neither huge data sets nor GPUs.

NASA will test Exact AI for detecting and diagnosing failures across critical onboard spacecraft systems.

Scout Ventures and Seraphim Space led the round, joined by Sovereign’s Capital, Flex Capital, FN Fund, and angel investor Gokul Rajaram.

PiLogic was founded in 2024. The founders come from Raytheon avionics, Google engineering, and NASA-funded research on probabilistic inference.

Manifest diagnoses faults in spacecraft systems. It returns ranked diagnoses with a probability attached, running onboard or beside ground monitoring.

Contact PiLogic about Manifest, Resolve, research access, or careers. Messages go to the founders.

PiLogic runs exact probabilistic inference over a first-principles model of a system, on a single CPU core. Two models: Manifest and Resolve.

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