Verity Deck — Credasis AI Inc.

Powered by VERITY

The Credibility Layer.

Accuracy ≠ Credibility

Accuracy is being correct on average. Credibility is being correct right now, when you need it.

Delaware C-Corporation · Founded 2025 · Pre-seed · Patent Pending

TL;DR · the 30-second version

One engine that tells you when data can be trusted.

If it's generated, it has to be verified. VERITY measures whether any output is faithful — and says REFUSE when it can't certify — on hardware you already own.

~$27B+

Combined addressable market · growing

$2–4M+

Modeled ARR by Year 3

3

Industries with working proof

Patent

Pending · USPTO Feb 2026

What it is

One geometric engine — verdicts: COMMIT / CAUTION / ESCALATE / REFUSE
Label-free, CPU-only, air-gapped — no GPUs, no cloud, no signatures
Deterministic, with a 28-byte audit signature per measurement

Proven in three industries

Quantum: 156-qubit IBM processor assessed on a Mac Mini
Network / OT security: 14/15 attack classes, zero labels, 560 KB
Brain-computer interfaces: #1 in Few-Shot Unsupervised on the public FALCON H1 benchmark (R² 0.29, #8 overall)

Why care: a confident wrong answer is the most dangerous output in security, medicine, finance, and infrastructure. We sell the instrument that catches it. Read on. →

The problem

Everyone can generate data. Almost no one can tell if it's faithful.

It's like a GPS that quietly loses signal but keeps calling out turns with total confidence. The danger isn't just being lost — it's also not knowing that you are.

AI, quantum hardware, and networks all produce outputs faster than anyone can check them.
Most tools always return an answer — a score, a probability, a label.
In security, medicine, finance, and critical infrastructure, a confident wrong answer is worse than no answer.

The bottleneck moved from making outputs to trusting them.

The missing layer is not a better model. It is a measurement instrument.

Same failure mode · three industries

Data fidelity breaks the same way everywhere.

Different domains. Same question: does this output still match what "normal" looks like?

Network security

Analogy: A bank vault alarm trained only on Tuesday's foot traffic won't catch Wednesday's heist dressed as maintenance.

Attackers change tactics daily. Signature lists and labeled training lag behind.

Quantum computing

Analogy: A piano that sounds fine note-by-note but goes out of tune the moment you play a chord — and no one has a reference recording for chords this complex.

Qubits drift. Classical simulation stops scaling past ~50 qubits.

Brain-computer interfaces

Analogy: A voice assistant that understood you yesterday but mishears you today because the microphone shifted — and you can't relabel every new morning.

Neural signals drift day to day. Implants can't run daily supervised recalibration.

One problem: fidelity to a trusted baseline — not classification accuracy on average.

What the industry tried

Bigger models. More labels. More compute.

The usual playbook

Simulate everything — classical tomography / full circuit simulation. Works until the problem outgrows any supercomputer.
Train classifiers on labeled attacks — needs endless labeled examples; misses zero-days by design.
Deep-learning decoders — GPU clusters, PyTorch runtimes, retraining pipelines — hard to ship inside implants or air-gapped plants.
Anomaly scores — always emit 0.87 "confidence" even when evidence is thin.

The wall

GPU wall: verification tied to cloud GPUs and vendor APIs — can't run where data must stay local.
Label wall: every new attack, qubit drift, or neural day needs fresh labeled data.
Scale wall: full simulation / tomography grows exponentially; impractical at production scale.
Honesty wall: probabilistic systems rarely say "I can't certify this."

No one shipped a portable, label-free instrument that says REFUSE when the evidence isn't there.

Where Verity fits

A credibility layer — not another classifier.

VERITY sits between the producer and the decision. It learns what "normal" looks like from benign baseline data — no attack signatures, no behavior labels, no simulated golden answer. Then it measures whether each new output still matches that baseline.

COMMITsafe to rely on CAUTIONpartial match — review ESCALATEweak match — re-run REFUSEcannot certify

REFUSE is intentional. When evidence is insufficient, VERITY declines to certify rather than guessing.

Label-free calibration · Deterministic verdicts · Same input → same output

How it works

Calibrate · Measure · Decide.

One pipeline for quantum circuits, network flows, and neural recordings. The sensor changes; the measurement logic does not.

1

Calibrate on normal. Minutes of benign traffic, healthy hardware behavior, or baseline neural activity — no labels required.

2

Measure fidelity. Each new output is scored against that baseline from four independent checks — not averaged into one opaque number.

3

Return a verdict. COMMIT only when all checks pass. Otherwise the posture downgrades — including REFUSE when certification isn't possible. Every measurement leaves a 28-byte audit signature.

Runs on a consumer CPU, air-gapped, in under a millisecond per observation — no GPU, no cloud dependency.

Why different

The instrument, not the classifier.

Everyone else

Trained on labeled data
Emits a probability / score
Needs GPUs and the cloud
Always returns an answer
Opaque "anomaly score 0.87"

VERITY

Zero labels for calibration
Deterministic geometric verdict
Consumer CPU, air-gapped
Says REFUSE when unsure
28-byte signature per measurement

Air-gapped, CPU-only, deterministic — consequences of measuring fidelity geometrically, not features bolted on.

Proof · three domains · one engine

We didn't just theorize — we measured real systems.

Quantum

156-qubit IBM processor assessed on a Mac Mini. Monotonic COMMIT → REFUSE as circuits scale. IBM job IDs independently verifiable.

Network security

14/15 attack classes ≥0.93 recall (Detection mode) on CICIDS-2017. 11/15 in Precision mode. Zero attack labels for calibration. 560 KB engine, ~1 ms/flow, CPU only.

Brain-computer interfaces

#1 in Few-Shot Unsupervised on the public FALCON H1 leaderboard — R² 0.29 held-out, #8 overall. 0.2 MB model, ~7 ms/timestep, CPU only — per-timestep confidence posture.

Same invention, three independent proof surfaces — with published limits on each (next slides).

Proof · Quantum computing

We independently assessed a 156-qubit IBM processor — on a Mac Mini.

ibm_kingston (Heron R2, 156 qubits). 179 circuits, 803,726+ shots.
Per-computation postures: monotonic COMMIT → REFUSE (2–12 qubits).
Complementarity: QFT-4q — 0.993 on one perspective, 0.000 on another. Single-metric verifier certifies; VERITY refuses.
Four calibration discrepancies vs IBM published data.
365-day fleet prediction — three backends, 1,095 snapshots, zero QPU consumed.

Circuit	Q	Posture
Bell	2	COMMIT
GHZ	4	COMMIT
GHZ	6	CAUTION
GHZ	8	ESCALATE
GHZ	10	REFUSE
GHZ	12	REFUSE

Every IBM Quantum job ID independently verifiable · Real hardware, externally checkable

Quantum · what it means

In plain English, the problem, and the money.

In plain English

Past ~50 qubits, no computer on Earth can re-check a quantum answer. VERITY measures whether the chip stayed faithful to its own physics — on a laptop — and refuses to certify when it didn't.

The problem it solves

Companies are starting to pay for quantum runs they cannot verify. A wrong result that looks confident drives bad science and wasted spend. Today there's no portable trust check.

In investor terms

Buyers: hardware vendors, national labs, quantum-cloud marketplaces.

Market: ~$150–300M emerging — grows with every new qubit shipped.

Price: $50K–$500K / yr per lab or per-job attach.

Cleanest, externally verifiable proof we have (IBM job IDs) — the credibility wedge that opens the other two markets.

Proof · Network security

Zero-day detection by construction. No labels. No signatures. No GPU.

14/15

Detection mode ≥0.93 recall · CICIDS-2017

0.963

Friday F1 · Precision mode · α=0.05

560 KB

Engine · ~1 ms/flow · CPU only · air-gapped

Zero attack labels for calibration or detection. Labels used only for post-hoc benchmark scoring.
Transfers to CSE-CIC-IDS2018 without retuning. BCCC-IoT F1 = 0.887; CIC-DoH F1 = 0.716 at α=0.10.
SQL Injection (n=13) at R=0.077 — reported honestly. Payload-level, below flow-metadata resolution.

Per-class results published including weak spots.

Network · what it means

In plain English, the problem, and the money.

In plain English

Teach it what normal traffic looks like; it flags anything that drifts — including attacks no one has seen yet — with a plain verdict, in a 560 KB CPU footprint, fully offline.

The problem it solves

Signature tools miss zero-days. ML tools need labels, GPUs, and the cloud, then emit a score an analyst can't act on. Critical infrastructure often can't send data out at all.

In investor terms

Buyers: SOCs, OT / critical infrastructure, security-platform OEMs, defense.

Market: ~$25B / yr global spend on detection tooling.

Price: $40K–$120K / yr per site · OEM $250K–$1.5M / yr + royalty.

Near-term revenue path — biggest installed market, lowest integration friction (drop-in, air-gapped, no GPU).

Proof · Brain-computer interfaces

#1 in Few-Shot Unsupervised on FALCON H1.

FALCON — Few-shot Algorithms for Consistent Neural decoding

#1

FSU category · public EvalAI

0.29

Held-out R² · #8 overall

0.2 MB

Model · CPU only · air-gapped

~7 ms

Per-timestep · real-time capable

Tops all few-shot unsupervised entries — ahead of SPINT (also 0.29) on consistency, with confidence posture per timestep.
Barely degrades from held-in (0.30) to held-out (0.29); SPINT drops 0.47 → 0.29. No GPU, no internet, no trial structure exploited.
Documented bottleneck: DOF 5 (hand open/close) at R² = 0.21 — 31.6% of score weight.

Metric	VERITY	SPINT (FSU #2)	Note
Held-out R²	0.29	0.29	Tied — we rank higher on lower variance
Held-in R²	0.30	0.47	SPINT degrades 38% to held-out; we don't

EvalAI leaderboard · public FALCON H1 · Jun 2026

BCI · what it means

In plain English, the problem, and the money.

In plain English

The implant adjusts to each new day from brain data alone — no daily calibration chores — and tells the clinician how much to trust each predicted movement. Runs in 0.2 MB on implant-class power.

The problem it solves

Brain signals drift daily and break decoders overnight. Today's decoders lean on GPUs and supervised recalibration a patient can't realistically perform every morning.

In investor terms

Buyers: BCI / implant OEMs, neuro-rehab, medical-device makers.

Market: ~$2–5B by 2030 (implant + rehabilitation).

Price: $500K–$3M license + per-device fee.

Highest ceiling, longest runway — FDA-path licensing. Public-benchmark proof (FALCON) de-risks the OEM conversation.

The opportunity

One engine. Three markets. Sequenced for proof.

Combined addressable market

Market	TAM	Timing
Network / OT security	~$25B / yr	Now
BCI / neural decoding	~$2–5B by 2030	Mid
Quantum verification	~$150–300M, scaling	Early

Sequence: quantum proof earns credibility → security pays the bills → BCI & AI-infra is the long-term ceiling. One engine, amortized across all three.

How the cash compounds

Stage	Motion	ARR
Year 1	5 pilots @ $50K + 2 site licenses @ $80K	~$410K
Year 2	+ first OEM platform deal @ $500K/yr	~$1M+
Year 3	+ BCI OEM license + royalty stream	$2–4M+

Revenue: engine license + per-deployment subscription — not GPU-hour resale.
Margin: tiny CPU-only runtime, no cloud bill to pass through — gross margin stays high as deployments scale.

TAM figures illustrative · ARR ramp is a modeled path, not a forecast · pricing per-market detailed on the domain slides

What customers get

Trust you can deploy — not rent from a GPU farm.

Deploy anywhere

560 KB–70 MB footprint. Runs on a Mac Mini, plant-floor appliance, or implant-adjacent edge node. No cloud exfiltration required.

Actionable verdicts

COMMIT / CAUTION / ESCALATE / REFUSE — plain language an operator or compliance officer can act on, not a probability to interpret.

Audit trail

28-byte signature per measurement. Deterministic replay. Built for regulated environments that need to prove what was known when.

Zero-day by construction in network mode — novel attacks surface without signature updates.
No retraining tax — calibrate on minutes of normal behavior, not months of labeled data.
Low cost to serve — no GPU bill behind the product; gross margin follows from the architecture.

How we package it

License the engine. Subscribe per deployment.

Delivery models

Embedded sensor — library inside switches, BCI firmware, or security appliances
Server appliance — air-gapped box on a span port or quantum job queue
Analyst workstation — offline verification for labs and regulated SOCs
OEM / platform license — white-label fidelity layer inside a vendor product

Commercial motion

Design-partner pilots — 90-day proof on customer data under NDA
Annual license + per-node fee — scales with deployment count, not GPU hours
Tiered SKUs — Personal / Professional / Enterprise with feature gating via VSEL
Sequenced GTM — quantum proof → security OEM → BCI / AI infra attach

Real-life deployment

Example: OT plant network sensor (air-gapped).

A water-treatment facility cannot send traffic to a cloud SOC. VERITY runs locally on a span port.

1

Day 0 — Install. Deploy 560 KB sensor on an industrial appliance. No GPU, no internet. Total footprint under 70 MB with calibration state.

2

Day 1 — Calibrate. Operator runs 10 minutes of known-good plant traffic. VERITY learns "normal" — zero attack labels, zero signature feeds.

3

Day 2+ — Monitor. Every flow scored in <1 ms. Benign SCADA traffic → COMMIT. Each verdict writes a 28-byte signature to local logs.

4

Day 47 — Alert. Novel lateral movement pattern → ESCALATE. Analyst reviews posture breakdown — not a opaque 0.87 score. If evidence is thin → REFUSE, not a false green light.

Same deployment pattern applies to quantum job queues (verify before trusting results) and BCI calibration sessions (adapt without behavior labels).

Who benefits

Five companies this unlocks value for.

Company	Why VERITY	Entry point
IBM Quantum	Independent fidelity assessment without classical simulation; job-ID-verifiable postures for customers	QFVL design-partner / OEM
Palo Alto Networks	Label-free zero-day detection as an OEM module inside next-gen firewalls — CPU-only, no cloud dependency	Security platform license
Neuralink	Per-timestep confidence posture and 0.2 MB decoder — implant-scale compute, no daily supervised recalibration	BCI decoder OEM (FALCON proof)
Schneider Electric	OT / critical-infrastructure sites that cannot exfiltrate traffic — air-gapped fidelity on the plant floor	Embedded appliance pilot
Amazon Braket	Per-job fidelity verdict before customers pay for unreliable quantum results — trust layer for the marketplace	Quantum-cloud attach

Illustrative targets — not partnerships or endorsements. Shows category breadth from one engine.

What's novel

One invention — not three products stitched together.

Geometric fidelity measurement — a single method that projects outputs into a shared space and scores trust from four independent checks, not one learned classifier.
REFUSE as a feature — most systems optimize for always answering; we optimize for certifying only when evidence supports it.
Cross-domain without retraining — quantum, network, and neural recordings use the same decision logic; only the sensor adapter changes.
Label-free by construction — calibration from benign behavior eliminates the attack-label / behavior-label treadmill.

Patent pending (USPTO, Feb 2026) on the core method. Seven additional quantum-specific filings under attorney review.

Novelty is in the measurement geometry — not bigger models.

Defensibility & validation

Hard to copy. Easy to verify.

1

VSEL deployment envelope — encrypted runtime, signed verdict provenance, integrity checks, kill-switch. IP protected in the field, not just on paper.

2

Evidence discipline — IBM job IDs preserved, per-class tables published (including weak results).

3

Independent review in progress — materials shared with IBM Quantum researchers (dialogue, not endorsement); #1 in Few-Shot Unsupervised on the public FALCON H1 leaderboard (R² 0.29, #8 overall); technical paper in preparation.

Early stage: we win trust by showing our work — including where we REFUSE to certify.

Market & business model

Three wedges. One engine. Capital-efficient by design.

1 · Quantum

Buyers: hardware vendors, national labs, quantum-cloud operators.

Cleanest proof · externally verifiable job IDs.

2 · Network / OT

Buyers: SOCs, critical infrastructure, security OEMs.

Near-term revenue · air-gapped deployment.

3 · Neural / AI infra

Buyers: BCI OEMs, cloud providers, regulated AI deployers.

Largest TAM · follows proof in quantum + security.

Revenue: engine license + per-deployment subscription — not GPU-hour resale.
GTM: founder-led design partners → OEM licensing → platform attach.
Margin: tiny runtime, CPU-only — cost to serve stays low as deployments scale.

Team

Deliberately small. IP-first.

Marty Oelrich — Co-Founder & Chief Scientist. Architect of the fidelity engine. Forensic examination and behavioral measurement background.
Tony Mangnall — Co-Founder & CEO. Operations, business development, market strategy, and partnerships.

Two co-founders by design — protect IP first, publish reproducible evidence second, add headcount only when work is repeatable.

Advisory pool reserved for industry insiders.

The ask

Lean pre-seed — amount and duration under discussion.

Use of funds

Patent / IP counsel — core invention + seven quantum filings
Validation hardening — reproducible public benchmarks
Design-partner pilots — quantum (IBM track active), network security
One technical hire — once repeatable work is scoped

Principle

Every dollar maps to a proof point. Optimizing for capital efficiency and a defensible moat — not a land-grab.

Pre-seed · credasis.ai

Vision

One engine. Many fidelity surfaces.

We built one thing: a portable way to measure whether data is still faithful to a trusted baseline.
Quantum hardware, network traffic, neural recordings — same pipeline. The sensor changes. The verdict logic doesn't.
As everything gets generated, the scarce thing becomes verification — and the ability to say REFUSE when it isn't faithful.

VERITY is the credibility layer for the generation era.

One engine. Many fidelity surfaces. · The geometry measures. We built the instrument.

LET'S TALK

Marty Oelrich — Co-Founder & Chief Scientist
marty@credasis.ai

Tony Mangnall — Co-Founder & CEO
tony@credasis.ai

Full validation reports and reproduction guides available under NDA.

Credasis AI Inc. · Delaware C-Corporation · Founded 2025 · Patent Pending