We build CAIBT — Cascade AI Brain Theory — an architecture that verifies its own perception at every time scale, from a one-millisecond reflex to a hundred-year fleet.
Four perception stacks fielded at TRL 6. 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).
Self-Verification, Correction and Learning (S‑VCL) operates across twelve orders of magnitude of time
At the long end, the architecture holds a queryable record of events that recur perhaps once in a few years.
The reliability ceiling
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 three-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.
CAIBT
Cascade AI 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. CAIBT decomposes the monolithic world model into verifiable contexts and conditions every inference on them. Entropy collapses toward a determinate answer because the question changed.
Condition every action on verified context, then embed Self-Verification, Correction and Learning at every time scale — from millisecond reflex loops to lifetime model evolution, a span of 3.16 × 1012 milliseconds.
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.
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:
Recover the scene physics behind raw EO/IR data, stripping environmental artifacts before anything is detected.
Reject detections that violate physical law across past and future frames — checked in both directions of time.
Erase what was confidently found with verified inpainting, then ask what remains. That is where the hard cases hide.
A primate-inspired visual pathway, built to fail independently of conventional detectors.
Perception that predicts its own errors, so people adjudicate only the contested cases.
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
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.
| Task | Metric | Result |
|---|---|---|
| Driving scenes, initial sAGI-Certify build | ||
| Vehicle detection | AP | 0.993 |
| Pedestrian detection | AP | 0.973 |
| Traffic-light detection | AP | 0.977 |
| Drivable-region segmentation | IoU | 0.997 |
| Lane-marking segmentation | IoU | 0.995 |
| Vehicle instance segmentation | IoU | 0.995 |
| Key-point detection | RMSE | 0.80 px |
| Monocular 3-D distanceUp to 50 m | Mean Absolute Relative Error | 0.5–3% |
| Monocular 3-D orientationUp to 50 m | RMSE | 1–3° |
| Monocular 3-D sizeUp to 50 m | RMSE | ~5 cm |
| LiDAR–camera fusion depthUp to 200 m, 1–5 LiDAR points per object | RMSE | ~10 cm |
| Aerial imagery and geospatial analysis | ||
| Power-line detectionIn point clouds | Precision / Recall | >0.95 |
| Building footprintFrom drone imagery | IoU | 0.9998 |
| Ground-control-point detectionIn aerial images | Precision / Recall | >0.98 |
| Road segmentationFrom drone LiDAR | IoU | 0.997 |
| Power-line inspectionDrone-based | Precision / Recall | >0.98 |
| Scene understanding for AR/VR and mobile robots | ||
| People and upper-body segmentsHead, face, arms, hands; objects to 10 × 10 cm | Precision / Recall | ≥0.995 |
| Human action recognition | Accuracy | ≥0.95 |
| Hand-gesture detection | Precision / Recall | ≥0.95 |
One core, nine doors
The two lower layers license into beachhead markets today. Their revenue funds the three above them, where the durable platform value sits.
Licensing revenue today 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 under 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 50 milliseconds — evidence, blind spots and distractors — with no access to model weights, no re-certification and no AI 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 license into 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
The fielded 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.
OS-Edge executes the verification kernel inside size, weight and power limits. CERTAINTY is designed to run entirely offline on standard tactical hardware.
Physics-validated perception across maritime, aerial and terrestrial domains, feeding the labeling and tracking engines.
Inverse-rendered scenes and five adversarial generators produce the long-tail cases that train the correction loops.
sAGI-ADL is designed IL5-native for AWS GovCloud, so controlled unclassified information never leaves the enclave.
Labels ship in open formats with per-label confidence and provenance, so the output outlives any single toolchain.
Verification across twelve orders of magnitude asks for things no accelerator provides yet. Proposals go out next year.
Scheduling compute and memory against a twelve-order span 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
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.
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.
Pay for proof now, recurring revenue later. Fixed-price prototypes mature each solution; each then moves to an as‑a‑service contract, priced by volume, richness or assurance tier depending on the solution, while Inspector and OS-Edge license commercially.
The record
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.
| Status | Count | Detail |
|---|---|---|
| Fielded | TRL 6 | Four perception stacks. The same core ships commercially as CASU, with pilot buy-ins from two Tier-1 automotive OEMs. |
| Under contract | 1 | AFRL SBIR Phase I, FA864926P0046, generating revenue today. |
| Rated by federal evaluators | 9 / 7 / 2 | Awardable on CDAO Tradewinds; Favorable from AFRL Rome, 2026; Excellent and Selectable on USAF SBIR proposals. |
| Under review | 7 | Six SBIR Phase I proposals — three Navy, two Air Force, one MDA — on counter-UAS, maritime anomaly detection, space-based activity characterization, interceptor navigation and low-SWaP neural architectures; one IARPA Solutions Marketplace submission, sAGI‑ID‑NIR. |
| Not selected | 1 | sAGI-ADL, for a CDAO special project. |
| Entering federal work | TRL 3 | The military adaptation modules, riding on the four stacks at TRL 6. |
| Target, not yet shown | >100× | Reliability gain from decorrelated self-correction. |
Contract FA864926P0046 is signed, in execution and generating revenue today. 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.
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.
Prototype to production
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.
Under 10 U.S.C. 4022(f), a prototype Other Transaction converts to follow-on production when three conditions hold:
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.
Commercial OEM revenue carries the TRL 6 foundation, about 83% of the work, so the Government funds only the 17% 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
CAIBT sits where four disciplines meet, and one founder has worked in all four.
Founder, Chief Executive and Principal Investigator
This class of core has shipped to production four times, at Aerial Insights, uSens, AEye and Inceptio. CAIBT 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.
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.