sAGIverse MobilityFremont, California Contact

Super intelligence that can prove what it just saw.

We build CSIBT — Cascade SI Brain Theory — an architecture that verifies its own perception at every time scale, from a one-millisecond reflex to a hundred-year fleet.

A federal SBIR Phase I signed and in execution. Nine solutions rated Awardable by the Department of War’s Chief Digital and Artificial Intelligence Office (CDAO).

Physical SI

guessingverifying

The operating system for the physical world

A first-principles reset of how machines perceive. CSIBT fuses theoretical physics, systems neuroscience, and human cognition into one architecture that moves SI from guessing to verifying. Every output is checked against physics and its own predicted errors, and corrected if needed, before anyone acts on it. Four perception stacks are at TRL 6, tested on large real-world datasets. Adapted for defense, the system enters federal work at TRL 3 through five CDAO Tradewinds Awardable solutions and six SBIR proposals in missile warning, uncrewed systems, maritime awareness, and assured navigation.

Platform layers L1–L3

  • CERTAINTY
  • UNMASK
  • sAGI-HyperTrack
  • sAGI-ADL
  • sAGI-Certify

Agentic SI and human-SI teaming

take controlcheckrely

The operating system for human-SI teams

The same architecture extends from perceiving the world to acting in it. CSIBT agents verify their own plans and actions, predict where they may fail, and correct course before an error compounds, in digital and physical settings alike. Every decision carries its evidence and a calibrated confidence, so people know when to rely on an agent, when to check its work, and when to take control. Four of our nine CDAO Tradewinds Awardable solutions and one of our seven SBIR proposals build on this foundation, with more planned.

Platform layers L4–L5

  • sAGI-Cognition
  • AWE
  • sAGI-Symbiosis
  • CADRE

Beneath both, Self-Verification, Correction and Learning (S‑VCL) operates across twelve orders of magnitude of time

  • 1 msReflex
  • 1 sControl
  • 1 minTactical
  • 1 hrMission
  • 1 dayCampaign
  • 1 yrFleet
  • 100 yrLifetime

At the long end, the architecture holds a queryable record of events that recur perhaps once in a few years.

TRL 6Four perception stacks, tested on large real-world data
9Solutions rated Awardable by CDAO Tradewinds
1Federal contract signed, in execution, earning revenue
$435BBeachhead markets gated by the reliability crisis

The reliability ceiling

The industry bought capability. It cannot buy trust.

Roughly $5.2 trillion has gone into AI infrastructure. It produced models that write persuasively but cannot be trusted to secure a power grid or command a drone swarm.

Around 95% of enterprise and defense AI pilots never reach production. The failure is not capability. It is auditability. No large model in deployment today offers verifiable determinism for a safety-critical or lethal system — and adding parameters does not change that.

The reason is structural. Text is a lossy shadow of the physical world, so reconstructing four-dimensional reality from it is an ill-posed problem. The resulting posterior over candidate actions is flat and high-entropy: the correct action becomes statistically indistinguishable from a plausible but wrong one. Scale does not separate them, because the information was never in the input.

In text, an error is a typo. In the physical world, an error is a collision. That difference is why the same architecture cannot serve both.

95% never scale Fail to scale Reach production
Enterprise and defense AI pilots, by outcome. The gap between $5.2T of infrastructure investment and realized enterprise value now exceeds $1T.

CSIBT

Condition the inference. Then check it eight ways.

Cascade SI Brain Theory fuses theoretical physics, systems neuroscience and human cognition into a single architecture — not a larger model, a different one.

A flat posterior is not a bug to be trained away; it is what you get when you ask an ill-posed question. CSIBT decomposes the monolithic world model into verifiable contexts and conditions every inference on them. Entropy collapses toward a determinate answer because the question changed.

P(Action | Data)  →  P(Action | Context, Data) + S‑VCL(t)
(1)

Condition every action on verified context, then embed Self-Verification, Correction and Learning (S‑VCL) at every time scale — from millisecond reflex loops to lifetime model evolution, a span of 3.16 × 1012 milliseconds.

P(Context | Task, Data)  →  ∑ P(Unmovable Space, Movable Space, Environment, Material, Light | Task, Data)
(2)

Context is itself a vast mixture model — over unmovable space, movable space, environment, material and light. We will build a model and a knowledge base for each component of the mixture: the foundational step to overcome intractability.

1.00.75 0.50.250 024 68 correction layers applied additive decorrelated
Illustrative of the mechanism, not measured performance. Checks that share failure modes stall at a floor after a layer or two; checks that fail independently keep multiplying — which is the whole argument for the >100× reliability target.

Eight complementary self-error-correction mechanisms run in parallel and in cascade over every inference. No two of them fail for the same reason, which is why their effects compound instead of adding. Among them:

  • Inverse rendering

    Recover the scene physics behind raw EO/IR data, stripping environmental artifacts before anything is detected.

  • Newtonian physics gate

    Reject detections that violate physical law, checked across both past and future frames.

  • Subtractive residual search

    Erase what was confidently found with verified inpainting, then ask what remains. That is where the hard cases hide.

  • Bio-vision connectome

    A primate-inspired visual pathway, built to fail independently of conventional detectors.

  • Self-predicted error

    Perception that predicts its own errors, so people adjudicate only the contested cases.

  • Tail-case synthesis

    Context-aware generative models synthesize severe long-tail cases for continuous retraining.

A second subsystem of seven modules addresses the Symbiosis Wall and the protocol, interoperability and scale hurdles that gate the $16T US AI economy.

Measured, not projected

A fractional build already beats published leaders.

On the KITTI Vision Benchmark, an initial build of sAGI-Certify — a few percent of its full potential — scores 0.993 AP on vehicles against a published leader’s 0.979, and 0.973 on pedestrians against 0.813. Measured as residual error, that is three and seven times lower.

Perception results from CSIBT builds. Vehicle and pedestrian AP are measured on the KITTI Vision Benchmark; all other rows are internal measurements.
TaskMetricResult
Driving scenes, initial sAGI-Certify build
Vehicle detectionAP0.993
Pedestrian detectionAP0.973
Traffic-light detectionAP0.977
Drivable-region segmentationIoU0.997
Lane-marking segmentationIoU0.995
Vehicle instance segmentationIoU0.995
Key-point detectionRMSE0.80 px
Monocular 3-D distanceUp to 50 mMean Absolute Relative Error0.5–3%
Monocular 3-D orientationUp to 50 mRMSE1–3°
Monocular 3-D sizeUp to 50 mRMSE~5 cm
LiDAR–camera fusion depthUp to 200 m, 1–5 LiDAR points per objectRMSE~10 cm
Aerial imagery and geospatial analysis
Power-line detectionIn point cloudsPrecision / Recall>0.95
Building footprintFrom drone imageryIoU0.9998
Ground-control-point detectionIn aerial imagesPrecision / Recall>0.98
Road segmentationFrom drone LiDARIoU0.997
Power-line inspectionDrone-basedPrecision / Recall>0.98
Scene understanding for AR/VR and mobile robots
People and upper-body segmentsHead, face, arms, hands; objects to 10 × 10 cmPrecision / Recall≥0.995
Human action recognitionAccuracy≥0.95
Hand-gesture detectionPrecision / Recall≥0.95

One core, nine doors

Five layers, and a business built to fund itself upward.

The two lower layers are built to license into beachhead markets first. Their revenue is planned to fund the three above them, where the durable platform value sits.

Agentic SI and human-SI teaming

L5Multiverse Where people debug and team with autonomous systems. sAGI-Symbiosis · CADRE
L4Protocol The language in which autonomous agents negotiate space and time. sAGI-Cognition · AWE

Physical SI

L3OS-Cloud Library of trust: curates ground truth and certifies third-party models. sAGI-ADL · sAGI-Certify
L2OS-Edge The verification kernel, running on the kinetic edge. sAGI-HyperTrack · UNMASK
L1Inspector Diagnostic MRI that forces a black box to reveal its causal logic. CERTAINTY

First to license Funded by the layers below

We are not building nine products. We are building one core and entering nine doors with it. Each of the nine solutions rated Awardable on CDAO Tradewinds maps onto a layer of this stack — the same architecture seen twice, once as engineering and once as a route to market.

The Inspector layer is also the operator’s view into any model. CERTAINTY is designed to wrap a certified perception system and return a ranked, plain-language causal diagnosis in under a few seconds — evidence, blind spots and distractors — with no re-certification and no SI expertise needed at the console. At the top of the stack, sAGI-Symbiosis is designed to sit between the operator and existing C5ISR data backbones such as Lattice and Maven.

Commercially, the same two lower layers are aimed at autonomous vehicles, industrial and retail automation, medical AI and assisted living, and test, inspection and maintenance — sectors where a wrong answer is already unacceptable and auditability is a purchase requirement rather than a preference.

Where it runs

Compute-bound by nature. Portable by design.

The four perception stacks run on GPU-accelerated pipelines. The architecture above them is written to run where the mission is — on standard tactical hardware at the edge, on premises, or in accredited government cloud.

  • Edge runtime

    Standard tactical hardware

    OS-Edge is built to execute the verification kernel within size, weight and power limits, and CERTAINTY to run entirely offline on standard tactical hardware.

  • Imagery, video and 3-D sensing

    Multi-sensor, multi-domain

    EO/IR, hyperspectral and satellite imagery; LiDAR and time-of-flight (ToF) 3-D; event data and multi‑stream video — physics-validated across maritime, aerial and terrestrial domains.

  • Physics and synthetic data

    Simulation, inverse rendering, world models

    Inverse-rendered scenes and five adversarial generators produce the long-tail cases that train the correction loops.

  • Cloud-scale training and labeling

    Accredited commercial and government cloud

    sAGI-ADL is designed IL5-native for AWS GovCloud, so controlled unclassified information never leaves the enclave.

  • Open formats, no lock-in

    COCO, MOT, DOTA and BOP

    Labels are designed to ship in open formats with per-label confidence and provenance, so the output outlives any single toolchain.

  • What comes after

    An open design question

    Verification across twelve orders of magnitude asks for things no accelerator provides yet. Proposals go out next year.

The hardware question our architecture keeps producing

Scheduling compute and memory across twelve orders of magnitude is the hard part, not raw throughput. Each correction loop consumes the world model the last one produced, and a rare-event record — an event that recurs perhaps once in a few years — has to survive for years and stay queryable. Today that state is rebuilt every pass; it should persist and be searchable in place. And Newtonian gates prune candidates at the edge, which is constrained search under a physics prior rather than a matrix multiply.

Our working hypothesis is that this is not one technology but a combination — conventional accelerators, quantum computing and neuro-computing, coordinated by a scheduler that treats time scale as a first-class resource. We will submit proposals on it next year, and we would welcome the view of anyone building the silicon.

Forward-looking, and nothing here is built, funded or promised. We publish it because it is the design question our own workload keeps producing, and we would rather develop the thinking in the open than around it.

Where we enter

Six sectors where failure is already catastrophic.

We do not claim the AI economy as addressable. We enter where the cost of being wrong is already unacceptable, which is where auditability gets purchased.

Industrial & retail automation Medical AI & assisted living Public safety & smart cities Autonomous vehicles Defense Test, inspection & maintenance $115B$100B$70B $60B$50B$40B
Each sector selected on one criterion: the cost of a wrong answer there is already catastrophic, so verifiability is a purchase requirement. Defense enters under Applied AI, one of the six Critical Technology Areas. Sector figures as sized in our market analysis.

The six sectors total $435B. One percent of that is $4.35B. Against the $16T US AI economy those are small numbers, and deliberately so — the credibility of a market claim lives in how narrow the door is, not how wide the room behind it.

Five Tier-1 automotive OEMs hold open invitations for pilots, joint development and investment — including L4/L5 safety certification — all conditioned on TRL 6+. Nine Awardable solutions cannot become programs of record until a prototype is demonstrated. Both markets are gated on the same technical milestone, and they open at the same moment.

How it earns

Pay for proof now, recurring revenue later. Fixed-price prototypes mature each solution, which then moves to an as‑a‑service contract priced by volume, richness or assurance tier. Inspector and OS-Edge are designed to license commercially.

The record

Reviewed by federal evaluators. Now under contract.

Awardable is not an award. We draw that distinction ourselves, and we report the evaluations that went against us alongside the ones that did not.

What exists, what is under contract, and what is still a target.
StatusCountDetail
Tested at scaleTRL 6Four perception stacks, tested on large real-world datasets. None is commercially deployed yet.
Founder’s track record4This class of core taken to TRL 6+ in production by the founder at prior employers: Aerial Insights, uSens, AEye and Inceptio.
Under contract1AFRL SBIR Phase I, FA864926P0046, signed and in execution, with milestone payments under way.
Rated by federal evaluators9 / 7 / 2Awardable on CDAO Tradewinds; Favorable from AFRL Rome, 2026; Excellent and Selectable on USAF SBIR proposals.
Under review8Seven SBIR Phase I proposals — three Navy, three Air Force, one MDA — on missile warning, counter-UAS, maritime anomaly detection, space-based activity characterization, interceptor navigation and low-SWaP neural architectures; one IARPA Solutions Marketplace submission, sAGI‑ID‑NIR.
Commercial interest5Tier-1 automotive OEMs with open invitations for pilots, joint development and investment upon TRL 6+.
Not selected1sAGI-ADL, for a CDAO special project.
Entering federal workTRL 3The military adaptation modules, riding on the four stacks at TRL 6.
Target, not yet shown>100×Reliability gain from decorrelated self-correction.

AFRL Phase I, converted and running

Contract FA864926P0046 is signed and in execution, with milestone payments under way. Phase I exists for a single purpose: to reach Phase II, and then Phase III — the phase that carries the work into defense and commercial markets and can be awarded sole-source.

sAGI-ADL, evaluated and not selected

The CDAO Enterprise Autonomy Division advanced sAGI-ADL to next-phase evaluation using government data, in a government IL5 environment, under government scoring. It was not selected for funding for that special project. Tradewinds is one-to-many, so the solution keeps its Awardable status and remains available to other program offices.

Standing and compliance

  • Certified Nontraditional Defense Contractor, 10 U.S.C. 3014
  • CMMC Level 2 Conditional in SPRS; NIST 800-171 compliant from day one
  • Ready for controlled unclassified information (CUI); ITAR assessment planned for Phase II
  • US incorporated, headquartered in Fremont, California
  • Advisers across legal, intellectual property, Other Transaction structuring and DCAA audit-readiness

Prototype to production

Two statutory routes, and we are on both.

Startups die in the gap between a prototype and a program of record. Both routes are written to cross it, and both are dual-use.

Route A — Small Business Innovation Research

Phase I exists to reach Phase III

  1. Phase I, in executionAFRL contract FA864926P0046: the feasibility phase, signed and earning revenue today.
  2. Phase II, the objectiveFull R&D maturation of the same core against the Phase I result.
  3. Phase III, the pointDefense and commercial production. Derived from Phase I and II, so it can be awarded sole-source with no SBIR funding ceiling.
Route B — Other Transaction authority

Prototype to production without recompetition

Under 10 U.S.C. 4022(f), a prototype Other Transaction converts to follow-on production when three conditions hold:

  1. Competitive procedures were used to award the prototype.
  2. The prototype is successfully completed against its exit gates.
  3. Follow-on production is written into the original transaction.

The third is the one most founders discover too late. We negotiate it in from the outset.

Each prototype is a firm-fixed-price Other Transaction — typically about $2.5M over twenty-four months — maturing on kill gates from TRL 3 to between TRL 5 and TRL 6, depending on the solution. The Government pays only for capability demonstrated against a defined exit gate, so a missed gate stops one module rather than the company, and several prototypes mature in parallel from the same core.

The TRL 6 foundation, about 65% of the work, is already built, so the Government funds only the 35% military adaptation delta.

Two doors are already open. Nine solutions are Awardable on the CDAO Tradewinds Solutions Marketplace, and as a certified Nontraditional Defense Contractor we qualify for Other Transaction prototype awards under 10 U.S.C. 4022 without a cost-share requirement.

Who is building it

A convergence almost no one has.

CSIBT sits where four disciplines meet, and one founder has worked in all four.

Zhiyong Yang, PhD

Founder, Chief Executive and Principal Investigator

30+years of R&D
78peer-reviewed publications
4TRL 6+ deployments

The founder has taken this class of core to TRL 6+ in production four times, at prior employers Aerial Insights, uSens, AEye and Inceptio. CSIBT is wholly owned by sAGIverse Mobility.

Candid on the gap: the technical depth is deep and the commercial depth is thin. The first two hires on first award are capture and contracts, then a commercial lead.

  • Theoretical physicsFirst principles
  • Systems neuroscienceNeural architecture
  • Cognitive scienceExecutive function
  • SI commercial executionFour TRL 6+ deployments, at prior employers
The next fifty years will not be defined by who holds the most data, but by who can prove that what they hold is true.

We welcome conversations with federal program offices, prime contractors, automotive OEMs, platform partners and investors working on verifiable autonomy. The fastest way in is email.

zhiyong@sagiversemobility.com