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Chemical
Reliable, cost-effective automation for critical infrastructure
A new innovation in machine intelligence is bringing reliable, cost-effective automation to the critical infrastructure sectors.
These are the domains the technology was built for — mission-critical operations where a wrong answer costs an irreplaceable asset, a life, or the entire mission.
16 sectors so vital their loss would be debilitating
The US Cybersecurity & Infrastructure Security Agency has identified 16 critical infrastructure sectors whose assets, systems, and networks, whether physical or virtual, are considered so vital to the United States that their incapacitation or destruction would have a debilitating effect on the physical security, economic security, national public health or safety, or any combination thereof.
Some of these sectors are actively under threat by external bad actors, others are at risk due to under capacity, deferred maintenance and rising costs. Cost-effective and rapidly deployable automation for these systems reduces operating costs, virtually eliminates human error and makes them internally robust to component failures and external threats.
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Commercial Facilities
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Communications
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Critical Manufacturing
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Dams
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Defense Industrial Base
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Emergency Services
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Energy
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Financial Services
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Food and Agriculture
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Government Facilities
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Healthcare and Public Health
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Information Technology
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Nuclear Reactors, Materials, and Waste
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Transportation Systems
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Water and Wastewater Systems
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Human-in-the-loop judgment — with or without the human
Applications of our technology, called sapiens, can provide human-in-the-loop judgment and expertise — with or without the human.
Like highly trained human experts, sapiens make decisions that can be trusted, explained and verified. Unlike human experts, sapiens never miss a data point, never get distracted, and never forget — anything.
How it works →
Operations photography
Plant or mission operations, wide, human in frame.
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CISA critical infrastructure sectors a sapiens can serve
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Patents held collectively across the leadership team and board
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Data centers required. A sapiens runs on a mobile device processor
This is not research-stage technology
The approach has been delivered in production before, on earlier generations of the same architecture, for NASA, Boeing, and the U.S. Navy. Real operational use, not simulations.
Mission operations
Hubble-era mission control, operators at telemetry consoles.
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NASA / Goddard
Hubble Space Telescope
45 → 2 minto interpret spacecraft telemetry, down from 45 minutes
The approach originated with founder Bryant Cruse’s work on the Hubble Space Telescope at NASA/Goddard, where the first AI system for spacecraft telemetry analysis reduced interpretation time from 45 minutes to under 2 minutes.
Delivered on prior-generation architecture
Launch pad
Launch vehicle on the pad, or launch control room during a countdown.
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Launch control
Conestoga Launch Vehicle
2 catcheserrors found that human operators missed
The system twice detected errors overlooked by human operators. It automatically held a countdown after detecting an explosive hazard operators had missed. On a later attempt it correctly identified an anomaly as a non-critical sensor failure, clearing the vehicle to launch on schedule and saving days of delay and over $1M in recycling costs.
Delivered on prior-generation architecture
Satellite operations
Satellite ground station or constellation operations floor.
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U.S. Navy
UHF Follow-On Constellation
1 laptopbeside a room of IBM mainframes
Deployed for the U.S. Navy’s UHF Follow-On satellite constellation, it ran on a single laptop beside a room of IBM mainframes. Air Force personnel repeatedly came over to watch its displays because they made spacecraft status so much easier to read.
Delivered on prior-generation architectureThis isn’t machine learning. This isn’t symbolic AI.
It is synthetic intelligence — and the difference is not a matter of degree.
Machine Learning
Machine Learning uses statistical patterns to simulate intelligence the way a video of fire simulates fire: it’s a realistic likeness, but the video is not on fire. What’s missing is precisely what’s essential.
If you want rationally competent machines, you don’t need a bigger, better camera; you need a different kind of instrument.
Sapiens operationalize human understanding of how the world works without a natural brain, just as an airplane’s wings replicate the abstract structure of aerodynamics without any bird.
Symbolic AI
Symbolic AI applications like expert systems or semantic network ontologies are based on collections of rules with no principled account of how knowledge is structured. This is why they are brittle and difficult to scale beyond small applications: a heap of rules is not a model of a domain.
A sapiens holds a world model grounded in a robust epistemology; it’s a dynamic map of how the domain is and behaves rather than a list of heuristics that only work inside it, under static conditions.
Synthetic Intelligence
New Sapience has developed a platform for scalable, deterministic, world-model-based applications in which knowledge is achieved as non-symbolic, directed graph structures.
Our IDE enables commonsense and expert knowledge derived from the customer’s domain and requirements to be reconstructed in computer memory as synthetic knowledge.
Synthetic Intelligence is real knowledge but not contained in a natural mind — in the same way a synthetic diamond is a real diamond but not created by natural processes.
Security
Quantum Secure Communications
External communication between sapiens or between a sapiens and any similarly equipped application can be secured with mathematically unbreakable encryption™ using one-time pad symmetric keys.
This technology collapses the attack surface with true randomness and secures data in motion and at rest: every file, financial transaction, healthcare record, email, text, voice, and video communication — transmitted over the internet.
✓One-time-use symmetric keys make any intercepted data worthless
✓Replaces long-lived cryptographic trust with Continuously Expiring Trust™
✓Secures data in motion and at rest
✓Holds today and in the coming quantum computing era
Compare probabilistic and deterministic. Then decide.
A deterministic system will give the same response to the identical question every single time. Run the test more than once and observe which system changes its answer.
Loop B coolant pressure fell 4.2 psi over 90 seconds. Is this a leak?
Conventional model
Answer sampled from a distribution
This could indicate a leak, though thermal contraction is also plausible. I’d recommend monitoring loop B and checking for corroborating indications. Confidence: moderate.
Fingerprint 8f21…c47dChanges each time
Sapiens
Answer derived from a world model
Not a leak. Pressure decay is consistent with thermal contraction: coolant temperature fell 6.1 °C over the same interval. Predicted pressure at measured temperature 412.8 psi; observed 412.6 psi, inside instrument tolerance. Loop B mass inventory unchanged.
Fingerprint 4c9e…81abNever changes
See how the sapiens reached that answer
- 01Read instrument: loop B pressure Δ −4.2 psi / 90 s
- 02Query world model: pressure–temperature relation, closed coolant loop
- 03Retrieve co-timed instrument: coolant temperature Δ −6.1 °C / 90 s
- 04Derive expected decay from thermal contraction: −4.0 psi ± 0.3
- 05Compare observed to derived: within tolerance → mass inventory conserved
- 06Conclude: not a leak — derived, not estimated. No confidence score, because none is required.
Tell us about your system that cannot afford to be wrong
We’ll show you how a sapiens can be your solution.