AXIOMATIC SYSTEMS
// SYSTEM STATUS: ONLINE
We tell you what's failing, why it's failing, and what to do next — backed by physics, not guesswork.
The operating system for the Software-Defined Asset.
One platform to monitor, diagnose, and maintain your entire fleet's health — from engine to drivetrain.
We are an engineering-first deep-tech startup building a Neuro-Symbolic Analytics Engine. We fuse Deep Learning with verification and policy to turn black-box guesses into evidence-backed decisions for critical infrastructure.
In plain terms: AI that doesn't just spot patterns — it checks every prediction against the laws of physics before it reaches you.
THESIS — Our philosophy | HORIZON — Consumer car health app | ACCESS — B2B fleet pilot
For Fleets & OEMs: Unplanned downtime costs millions. Traditional AI is a "Black Box" that guesses based on history. If a failure mode is new, the AI misses it.
For Consumers: "Check Engine" lights are vague. Mechanics upsell unnecessary repairs because you don't have the data to prove them wrong.
Neuro-Symbolic AI: We don't just guess — we verify. Our “Glass Box” engine checks each hypothesis against constraints, context, sensor trust, and twin-based plausibility when available — and records PASS / FAIL / INCONCLUSIVE with reasons.
“Glass Box” = unlike a Black Box that hides its reasoning, our system shows you exactly why each alert was raised — and the evidence behind it.
The Result: Fewer false alarms, higher operator trust. Alerts can be approved, annotated, downgraded, or blocked based on verification outcomes — each one shipped with an Evidence Bundle for auditability. When telemetry contains detectable precursors, we estimate time-to-threshold (with confidence) with uncertainty bounds and recommended next actions.
Evidence Bundle = the complete record attached to every alert — signals, checks, confidence scores, and recommended actions. Think of it as a lab report for your vehicle.
Built on the shoulder of giants
Enterprise-grade infrastructure for real-time processing at scale — from vehicle to cloud.
A unified control plane for Anomaly Detection, Root Cause Analysis support, and Prescriptive Maintenance. Architected for Zero Trust environments.
Vantage watches your fleet 24/7. It spots problems early, checks them against physics, watches for blind spots between its own systems, and tells you exactly what to fix — with evidence.
Available as Consumer subscriptions (Freemium / Pro / Pro+) via Project Horizon, and B2B fleet tiers (Fleet Starter / Fleet Pro / Enterprise) for commercial operators. Contact us for pricing details.
AI spots the anomaly
Physics confirms it
Blind spots are caught
You get the fix + evidence
Tap any card below to see the technical details
A 7-model ensemble — custom transformer, VAE, GNN, digital twin lite, and baseline models — fed into a meta-learner stacker to propose causal hypotheses from raw data.
Think of it as a panel of seven specialists, each examining your data from a different angle, then voting on what’s going wrong.
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The “Truth Layer.” High-fidelity engine and transmission physics models — thermodynamic simulation, torque-speed curves, gear ratio validation — stress-test every hypothesis before it becomes an alert.
Before any alert reaches your dashboard, we simulate the predicted failure against real physics — heat, torque, gear behaviour — to see if it’s physically possible.
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We assume sensors can be faulty, drifting, or adversarial. The platform computes per-sensor trust scores driven by consensus/outlier behavior.
No sensor is trusted by default — every reading is cross-checked against its neighbours before it’s used for a decision.
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Privacy-Native ML. Train on raw fleet telemetry without exposing IP or violating GDPR.
Your fleet data stays yours — it’s processed securely and never pooled with other customers.
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Run low-latency inference near the asset (vehicle, gateway, controller, or edge server), while improving models via silo-level federated learning across sites/fleets—without pooling raw data (deployment mode).
Analysis happens close to the vehicle, not in a distant data centre — so alerts arrive in real time, even with patchy connectivity.
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The "AI Mechanic." We translate complex vector mathematics into plain-English repair manuals.
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The referee between System 1 and System 2. Tracks disagreements between the AI ensemble and physics verification, promoting recurring conflicts to DEGRADED alerts.
DEGRADED = “we can’t confirm this is fine — get it checked.” It’s the system being honest about uncertainty rather than hiding it.
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The Vantage Platform is engineered as a High-stakes Neuro-Symbolic Analytics Engine. While our current deployment focus is Automotive, the underlying neuro-symbolic logic is domain-agnostic.
In simple terms: the same AI that protects a truck fleet can be adapted to protect factories, robots, or power grids — because the verify-before-you-alert approach works everywhere.
// CURRENT STATUS:
Primary validation underway in ICE (petrol/diesel) Commercial & Passenger Vehicles.
We are actively adapting our "Physics Core" to expand into high-stakes infrastructure where our System 1 (Discovery) + System 2 (Verification) architecture provides a unique advantage.
Current Deployment.
Application: Prescriptive maintenance for ICE Fleets (Trucking/Logistics) and Passenger Vehicles.
Capabilities: Detection of thermodynamic stress, piston ring flutter, turbocharger efficiency loss, and transmission slippage prior to failure code generation.
In Training & Validation.
Application: Battery health monitoring, thermal management, charging pattern analysis, and drivetrain diagnostics for electric vehicles.
Goal: Extending the Vantage verification architecture to EV-specific physics models — state-of-charge estimation, cell degradation tracking, and regenerative braking efficiency.
Architectural Target (late 2026).
The Fit: Precision Assembly Robots require exact kinematic alignment, similar to engine timing.
Goal: Detecting micro-deviations (drift) in servo motors to prevent batch quality failures.
Roadmap Expansion.
Note: Additional high-stakes sectors are in active research and partner-driven validation.
Status: "Coming Soon" domains are shared with qualified pilot partners and depend on signal coverage + constraints readiness.
"In a Software-Defined World, the 'Black Box' is a liability."
Axiomatic Systems was founded to solve the structural failure of modern Predictive Maintenance: Trust. Existing tools guess. In Automotive — and eventually Aerospace and Defense — guessing puts people at unnecessary risk. We believe you need Axiomatic-grade verification—grounded in physics, constraints, and evidence.
Today’s maintenance AI gives you a guess. We give you a verified answer with receipts.
Standard AI finds patterns. Vantage adds verification—so critical decisions are explainable, evidence-backed, and audit-ready.
Privacy and control are built into the system by design—deployment options, access boundaries, and cryptographic protections are part of the architecture, not an afterthought.
A dashboard that just shows "Red Lights" is a burden. A system that explains how to fix it is an asset.
Engineering First. Physics Always.
Axiomatic Systems is a deep-tech infrastructure company operating at the intersection of Physics-Based AI, Control Theory, and Verification.
The company was founded on a core realization: The Silicon Valley mantra of "Move Fast and Break Things" is functionally incompatible with critical infrastructure. When you are managing a commercial vehicle fleet, maintaining engine health across thousands of kilometres, or — in the future — overseeing autonomous trucks and industrial turbines, "breaking things" is not an option — it is a catastrophe.
The Mission: To solve the "Verification Gap" in Artificial Intelligence. Current AI models are black boxes that produce probabilistic outputs without sufficient operational guardrails. We are building the verification layer that checks model outputs against constraints, context, sensor trust signals, and physics-based plausibility where applicable, allowing the industrial world to adopt AI with axiomatic rigor—enabling high-confidence, evidence-backed decisions rather than unauditable alerts.
We do not just build software; we architect the safety rails for the next generation of software-defined assets.
FOUNDED
Pune, India
FOCUS
Automotive Predictive Maintenance
STATUS
MVP Nearing Completion
Headquarters: Pune, India
Vantage is a verification-gated analytics control plane for mission-critical systems: models propose hypotheses, then Vantage verifies them using constraints, trust scoring, and twin-lite simulation before any alert or action is allowed.
Standard tools rely on correlation and output a score. Vantage adds a verification gate and a policy decision layer, so outputs become auditable decisions rather than probabilistic guesses.
Propose: A 7-model ensemble generates hypotheses (risk, horizon, likely causes).
Verify: Constraints, operating context, sensor trust, and physics twin checks test whether the hypothesis is plausible.
Observe: The patent-pending Observation Layer tracks disagreements between Propose and Verify. Recurring conflicts are promoted to DEGRADED alerts.
Decide: Policies determine whether to permit, downgrade, annotate, or block the alert/action.
Typical checks include magnitude limits, rate-of-change, unit consistency, mode envelopes, sensor trust, and cross-signal coherence. Outcomes are labeled as PASS / FAIL / INCONCLUSIVE with reasons.
INCONCLUSIVE means the system cannot safely verify a hypothesis due to insufficient or conflicting evidence (e.g., low sensor trust, missing signals, contradictory patterns). Instead of guessing, the platform can downgrade and recommend inspection or additional data collection.
No system can guarantee that. Vantage is designed to significantly reduce false positives by suppressing or downgrading unverified alerts and making uncertainty explicit via PASS/FAIL/INCONCLUSIVE outcomes.
Twin-lite is a fast, constrained simulation model used for short-horizon plausibility checks. It is used to test: “Does this hypothesis reproduce the observed behavior under similar conditions?” It is a verification tool, not a replacement for full high-fidelity simulation programs.
An Evidence Bundle is an auditable record produced for key events, including: model outputs, verifier checks, trust scores, twin residuals (when used), policy decisions, correlation IDs, and model versions—so decisions can be reviewed and reproduced.
Zero Trust means no sensor is assumed trustworthy by default. Signals are evaluated for behavioral consistency, drift, and cross-signal coherence, and can be downweighted or isolated when evidence suggests malfunction or compromise.
The trust layer supports adversarial thinking (e.g., Sybil-style patterns and reputation updates). In deployments, trust scoring is implemented using practical consensus, drift detection, and configurable reputation updates governed by policy.
Not required for every deployment. Vantage is TEE-ready: in supported deployments, sensitive processing can occur inside protected execution boundaries. Otherwise, similar controls can be enforced using encryption, access policy, and audit logging.
Retention is policy-driven and deployment-configurable. Many deployments minimize raw retention and store derived features, aggregates, and evidence artifacts. Requirements vary by customer and regulation.
The architecture supports separation of an identity plane and a behavior plane. Identity access is restricted, while analytics can run on pseudonymous behavior streams. The link to identity is controlled by policy.
Yes. The Vantage Platform is containerized (Docker/Kubernetes). We can deploy to your private cloud, on-premise servers, or onto edge environments depending on security and connectivity constraints.
Usually, no. Vantage is "sensor agnostic" and ingests from existing streams (e.g., OBD-II/CAN, gateways, historians). Additional sensors are only recommended if a critical blind spot is identified for your verification constraints.
Yes—no rip-and-replace. We connect via existing SCADA/edge gateways (e.g., OPC UA) and/or MQTT streams, and can also read from historians/data platforms for backfill. Deployment is containerized at the edge or on-prem, with governance + evidence on top.
“MQTT today; OPC UA/Modbus via gateway connectors on roadmap.”
Vantage is currently operational for the automotive domain. The platform is designed to adapt to additional domains—such as flight data streams, SCADA/Industrial IoT gateways, and structured logs—through a schema + adapter layer, knowledge files and constraints, domain-specific model retraining and hyperparameter tuning, and OPA/policy rules that encode operating context and guardrails.
The key requirement is consistent timestamped signals with basic metadata. We are currently in training and validation for EV and Industrial IoT deployments.
4-6 weeks (typical). We utilize a "Shadow Mode" deployment strategy.
Week 1-2: Historical data ingestion + knowledge pack alignment + twin-lite calibration (where applicable).
Week 3-6: The system runs alongside your current stack, producing verification-gated outputs without interfering with operations. We present a pilot report at the end of the window to validate ROI and operational fit.
A pilot typically needs: sample telemetry, a basic system topology (components/signals), operating modes, and a starter knowledge pack (thresholds/envelopes). We then run the propose+verify loop and produce evidence-backed outputs.
Common metrics include: false positive reduction, earlier detection lead time, improved RCA precision, fewer escalations, reduced mean-time-to-diagnosis, and operational adoption (alerts acted upon).
Verification quality depends on sensor coverage, data quality, and the completeness of constraints/knowledge for the domain. Vantage is designed to communicate uncertainty explicitly rather than overclaim.
B2B Pilot Criteria: Currently prioritizing commercial fleets with 50+ assets, initially in Maharashtra and NCR. For consumer early access, see Project Horizon.
We are currently accepting pilot partners in Automotive Fleets.
See Vantage run on your fleet data within 2 weeks. We handle setup — you see results.
Email: partnership@axiomaticsys.com
HQ: Pune, India