Neural-Symbolic Reasoning Engine · helixor.ai

Beats OpenAI.
Beats Claude.
Beats Gemini.
On the problems where being wrong is unacceptable.

At 1/1,000th the cost.

Helixor is not an LLM. Not a wrapper. It is a fail-closed reasoning AI engine that computes answers, verifies them, and ships a proof — or says nothing at all.

Request Access Talk to the team
1,000×
Cheaper
85×
Faster
100×
Less Power Used
0
Hallucinations
NOT BUILT ON LLMs · NOT A WRAPPER · NOT AN APPROXIMATION · AIR-GAP READY · FAIL-CLOSED BY DESIGN · DETERMINISTIC · EMBEDDABLE · PROOF WITH EVERY DECISION · NOT BUILT ON LLMs · NOT A WRAPPER · NOT AN APPROXIMATION · AIR-GAP READY · FAIL-CLOSED BY DESIGN · DETERMINISTIC · EMBEDDABLE · PROOF WITH EVERY DECISION ·
// the architecture

Reasoning as a
structured systems problem.

Every frontier LLM is a sequence predictor — extraordinary at producing fluent language, structurally weak at the problems where fluency is not the same as correctness. Helixor was built around a different contract.

— Helixor
Symbolic execution. Reasoning runs through typed, composable operations — not next-token prediction. Same input, same output. Every time.
Proof with every answer. Every output carries a structured, auditable reasoning chain — produced by the same execution that generated the answer.
Air-gap native. No cloud required. Runs on consumer hardware, embedded ARM, and in environments where data cannot leave the device under any circumstance.
Dramatically lower cost. GPU-native tensor architecture on whatever hardware is present. No inference cluster. No token bill. A structural cost advantage that compounds as coverage grows.
— Frontier LLMs
Probabilistic by design. Confidence is proportional to language fluency, not factual correctness. Ask the same question twice, get different answers.
Cannot explain why. Regulators, auditors, and general counsel need justification. LLMs generate post-hoc narratives. That is not a proof.
Cloud-dependent. Data leaves your perimeter on every inference call. Air-gapped deployments are architecturally impossible.
The inference tax. Frontier reasoning models charge per token, per query, at every scale. At enterprise decision volumes, that cost compounds into a budget problem that prevents deployment into the operations that need it most.
// four properties

The contract that
dictates the architecture.

01
Deterministic
Same input. Same output. Every time. The reasoning engine does not sample — it executes. Compliance, regulated decisions, and repeatable audits require this. LLMs cannot provide it.
02
Fail-Closed
If Helixor cannot justify an answer, it produces no answer. No confabulation. No polished wrong answer. Explicit refusal on unsupported paths. The line that separates Helixor from every generative system.
03
Air-Gap Ready
No cloud round-trips. No external network calls. Problem content, reasoning, and outputs stay entirely on-device. Built for ITAR, CMMC, HIPAA, and FedRAMP High data-residency requirements.
04
Embeddable
GPU-native tensor architecture. Runs on a laptop GPU, Nvidia datacenter GPUs, or embedded ARM hardware in the field. Performance scales by adding reasoning families — not by adding hardware.
HALLUCINATIONS · UNAUDITABLE · THE INFERENCE TAX · BLACK-BOX DECISIONS · NO PROOF CHAIN · CLOUD DEPENDENCY · CONFIDENCE ≠ CORRECTNESS · HALLUCINATIONS · UNAUDITABLE · THE INFERENCE TAX · BLACK-BOX DECISIONS · NO PROOF CHAIN · CLOUD DEPENDENCY · CONFIDENCE ≠ CORRECTNESS ·
// the assertions

We solve problems
LLMs cannot.

Beats frontier models
on hard reasoning.
On the problem classes Helixor is designed for — multi-step scientific reasoning, exact mathematical reasoning, constraint-heavy decisions — we are not competitive. We are getting the right answer.// on supported families, outperforms o3 and Claude Opus
1,000× cheaper
per decision.
Frontier reasoning models charge per token, per query — costs that compound linearly with every decision at scale. Helixor's tensor architecture runs on whatever hardware is present. No inference cluster. No per-query tax. A structural cost advantage that makes deployment into core operations economically viable.// 1,000× cheaper on equivalent problems vs. frontier models
85× faster
response time.
The reasoning engine runs GPU-native tensor operations. On the problems where Helixor's families cover the domain, the LLM is not where the intelligence lives — and removing it removes the latency.// 147ms avg vs. 12,554ms LLM-backed on identical accuracy
Zero hallucinations.
By architecture.
Helixor never fabricates. Verification is a first-class stage in the pipeline. Unsupported or contradictory paths fail explicitly — they do not produce confident wrong answers at scale.// fail-closed is not a guardrail. it is the contract.
Auditable proof chain
with every decision.
Structured runtime telemetry on every execution path. Typed rule-activation diffs on every supported reasoning path. No generative model can produce this — generative models do not execute against an explicit constraint set.// built for regulators, CISOs, and general counsel
// decision intelligence

Where LLMs stall.
Where Helixor runs.

Decision Intelligence is the fastest-growing segment of enterprise AI — and it lives inside regulated industries where "same answer twice" isn't a feature. It's a legal requirement.

ITAR · CMMC · FedRAMP High
Defense & Government
Intelligence analysis, classified operations, autonomous systems. Cloud round-trips are a non-starter. Data cannot leave the installation. Helixor runs on-device, air-gapped, with no external dependency of any kind.
LLMs: Live in the cloud. We run on-premise.
FED · OCC · ECOA · FCRA · SR 11-7
Banking & Financial Services
Lending, AML, fair-lending, fraud. Every denial needs documented reasoning. A denied mortgage requires a legal explanation. Probabilistic models cannot produce one. Helixor generates the proof chain the decision was made on.
LLMs: Cannot satisfy SR 11-7 model risk governance.
State DOIs · NAIC
Insurance
Underwriting, claims, reserves, pricing models. State regulators require explainability and actuarial soundness for every consequential decision. The same structured proof that satisfies an auditor is the same chain Helixor produces operationally.
LLMs: Cannot satisfy state-by-state explainability rules.
Supply Chain · Manufacturing · Logistics
Optimization
Real-time fleet routing, production scheduling, customs and tariff intelligence, and constraint-enforced supply planning — reacting to live conditions in milliseconds. Finds the best answer among millions of possibilities with feasibility enforcement built into the solver, not bolted on after.
LLMs: Must call an external solver. We are the solver.
HIPAA · FDA AI/ML · CMS
Healthcare
Clinical decision support, care-pathway optimization, staff rostering, and resource allocation. Constraint-enforced decisions with full labor-rule and policy compliance. Deterministic outputs that meet FDA AI/ML guidance requirements — reproducible, validated, and auditable by design.
LLMs: Probabilistic outputs cannot satisfy clinical validation requirements.
Life Sciences · Pharma · Scientific R&D
Research & Discovery
Multi-step scientific reasoning, exact mathematical proof, and hypothesis verification across physics, chemistry, and biology. Helixor solves where a polished wrong answer is more dangerous than no answer — delivering verified results at a fraction of frontier model cost and latency.
LLMs: Generate plausible answers. We generate correct ones with proofs.
Hotels · Airlines · Restaurants · Events
Hospitality
The defining failure mode in hospitality is giving away something you don't have — confirming a reservation, booking a seat, or allocating a room that is already committed. Helixor's constraint-enforced availability engine verifies exactly what exists before any commitment is made. Every booking is a solved constraint problem, not an approximation. No overbook. No walk. No denial.
LLMs: Approximate availability. We verify it before the commitment is made.
Retail · E-Commerce · CPG
Retail
Dynamic pricing and inventory optimization across massive SKU catalogs, demand signals, margin constraints, and competitive conditions — computed in real time. Helixor finds the optimal price point and stock allocation across high-dimensional constraint spaces without guessing. Every pricing decision is verifiable. Every inventory commitment is constraint-enforced.
LLMs: Suggest a price range. We compute the optimal one.
// real-time intelligence

Learns from every decision.
No retraining required.

Helixor ingests live data streams, reacts to events in real time, and continuously refines its reasoning policy from operational outcomes — without changing a single model weight or triggering a retraining pipeline.

01 · INGEST
Live data stream in.
Real-time event feeds, sensor data, transaction streams, market signals — Helixor processes live inputs without waiting for a batch cycle or a model update.
02 · EXECUTE
Verified decision out.
Every decision is computed against the current constraint state — incorporating new facts instantly. The answer reflects what is true now, not what was true when the model was last trained.
03 · LEARN
Reasoning policy improves.
When a reasoning path fails, Helixor records it and suppresses retry on that signature. When a penalized path later succeeds under a verified outcome, the suppression is lifted. The engine accumulates what works. No weights change. No retraining required.
04 · REPLAN
New decisions use past learnings.
Each subsequent decision benefits from the full operational record. The system gets faster and more accurate on the problems it has seen — without any human intervention or model deployment cycle.
Supply Chain
React to disruption before it cascades.
Port closures, tariff changes, supplier failures — Helixor detects the event, re-runs affected constraint chains, and issues a replanned schedule in milliseconds. Not in the next batch cycle.
Financial Services
Detect fraud patterns as they emerge.
Fraud patterns change faster than model retraining cycles. Helixor's reasoning policy adapts from live transaction outcomes — tightening constraint signatures on patterns that have been verified as fraudulent without requiring a new model deployment.
Insurance
Replan exposure the moment an event lands.
A hurricane makes landfall. A wildfire changes direction. Helixor ingests the event signal and immediately re-evaluates affected policies, exposure limits, and reserve requirements — issuing updated constraint-verified positions in real time.
Defense
Mission replanning at the speed of the field.
Conditions change. Helixor recomputes constraint-verified mission parameters in response to live field events — on-device, air-gapped, without a round-trip to an inference server or a human in the planning loop.
THE DIFFERENCE
LLMs improve only when retrained — a process that takes weeks, costs millions, and still produces a probabilistic model with no guarantee of correctness. Helixor improves from every decision it executes, updating its reasoning policy from verified operational outcomes in real time. The engine learns while it runs.
// the contract

Validated input → Structured process → Verified output, or explicit failure.

That contract dictates the architecture. Not built on LLMs. Not a prompt wrapper. A proprietary neurosymbolic AI engine purpose-built around a single principle: if it cannot be verified, it will not be returned.

Input Validation
→
Semantic Compilation
→
Symbolic Execution
→
Verification
→
Verified Output + Proof
NOT A PROMPT WRAPPER
A proprietary neurosymbolic AI engine purpose-built from first principles — not a layer on top of an existing foundation model.
PERFORMANCE THAT COMPOUNDS
Coverage grows by adding reusable reasoning families — not by adding GPU hardware. The more problems we cover, the faster and cheaper every decision becomes.
// embedded intelligence

Imagine PhD-level reasoning in
every machine that matters.

Better than PhD-level expertise in Math, Physics, Biology, and Chemistry — running on-device, in milliseconds, with no cloud, no network, no infrastructure. Not in a datacenter. In the machine itself.

AUTONOMOUS VEHICLES
Every car, a physicist.
Real-time constraint reasoning across trajectory physics, collision dynamics, and traffic optimization — running in-vehicle, air-gapped, with no cloud round-trip between the decision and the road.
ROBOTICS
Every robot, a chemist.
Multi-step reasoning over physical constraints, materials science, and process chemistry — embedded in the robot's own compute. No latency to an inference server. No dependency on an internet connection.
DEFENSE SYSTEMS
Every system, a strategist.
Autonomous decision-making under strict constraint sets, with full determinism and verifiable outputs — on hardware that never touches an external network. Designed for the environments where cloud AI is simply not an option.
SATELLITES & SPACECRAFT
Every satellite, a mathematician.
Orbital mechanics, energy budgeting, fault detection, and mission constraint reasoning — running on the satellite's own GPU. No ground-station round-trip. No latency measured in seconds when the answer is needed in milliseconds.
MEDICAL DEVICES
Every device, a diagnostician.
Clinical reasoning at the point of care, on the device, without patient data leaving the room. FDA-compliant deterministic outputs from hardware that runs on battery power. The intelligence lives where the patient is.
INDUSTRIAL & INFRASTRUCTURE
Every plant, an engineer.
Constraint-enforced process optimization, safety reasoning, and predictive maintenance — embedded in industrial control systems that operate in air-gapped environments by regulatory requirement.
147ms
Avg Solve Time
Fast enough for any real-time system
~20W
Power Draw
Laptop. No H100 farm.
0
Cloud Dependencies
Fully on-device. Air-gap by design.
Any GPU
Hardware Target
Laptop · Nvidia · embedded ARM · edge

"Frontier LLMs require a datacenter. Helixor requires whatever compute is already there. That is not an optimization. That is a different class of technology."

// the team

Builders first.
The engine exists.

Atlanta & Research Triangle Park — strong AI/ML talent pools anchored by Georgia Tech, Duke, UNC, and NC State.

Michael Ingardia
Michael Ingardia
Founder
Atlanta, GA
Three successful exits. C-Level and startup experience. Prior AWS Principal SA, prior IBM STSM. Deep domain expertise in operations research, AI architecture, and logistics. Built the Helix Core engine from first principles.
Erik Burckart
Erik Burckart
Co-Founder
Raleigh, NC
Two successful exits. C-Level & startup experience. Prior IBM STSM, Top Patent Creator at IBM. Cross-domain technology executive with 20+ years building enterprise SaaS and AI platforms across B2B, supply chain, and decision intelligence.
// adaptive reasoning. verified.

The question has moved past
"is there anything here?"

Request early access. Active pilots across insurance, banking, accounting, and supply chain — all inbound. No sales team yet.

Insurance
Banking
Accounting
Supply Chain
Healthcare
Defense
Logistics
Manufacturing
Pharma & Life Sciences
Scientific Research
Autonomous Systems
Hospitality
Retail