Industrial diagnostics
Find root cause, severity, and remaining useful life from sensors, logs, images, and operator notes.
Open use case- Build AI ModelsDeploy local model families for language, vision, audio, 3D, and edge systems with replayable evidence.
- Turn Signals into IntelligenceFuse sensors, logs, images, and context into cause chains your team can act on.
- Defend with ShieldProtect infrastructure with active response and a clear reason chain for every action.
- Diagnose Root CauseIdentify severity, source, and next action from industrial signals without a black-box score.
Energy and grids
Monitor substations, turbines, pipelines, and load anomalies with local explanations for operators.
Open use caseInfrastructure defense
Detect coordinated abuse, network anomalies, and active attacks with evidence for every block.
Open use caseMedical review
Support imaging and clinical triage where the explanation must be readable by humans.
Open use caseAutonomous perception
Explain scene decisions from camera, LiDAR, radar, and 3D point cloud inputs.
Open use caseLegal and finance
Make credit, fraud, contract, and risk decisions traceable instead of opaque.
Open use caseSupply chain
Connect delays, capacity shocks, and quality issues into a cause chain your team can act on.
Open use caseEdge sensors
Run tiny local models on machines, vehicles, and embedded devices where cloud is too slow or risky.
Open use caseBusiness impact
Applications
Problems solved
What Qriton builds.
Model families, signal intelligence, active defense, and diagnostics share one evidence-first architecture.
One architecture for model families that need evidence.
HLM keeps each modality's structure intact, then uses a shared Hopfield core to expose convergence, uncertainty, and replay metadata for language, vision, audio, 3D, and sensor workflows.
Energy Language "Energy minima as a programming language in a completely new fashion." John J. Hopfield, March 2026, on programming energy landscapes directly
Energy Language is the documentation layer for HLM: observe the model's basins, edit targeted behavior without full fine-tuning, and verify the change with a replayable change record.
Read the HLM documentationOperational AI
Industrial diagnostics, infrastructure monitoring, and supply-chain decisions where teams need root cause, urgency, and a replayable record.
Learn morePerception AI
Vision, LiDAR, spatial, audio, and robotics pipelines where the model must preserve evidence about what it detected and why.
Learn moreRegulated AI
Medical, legal, finance, public-sector, and defense workflows where decisions need uncertainty, documentation, and governed model changes.
Learn moreEvery decision explained
Not post-hoc approximations. The inference path carries the evidence: model state, uncertainty, convergence, and output.
Built-in uncertainty
Know when the model is confident and when it's not — without bolting on calibration tools. Prevents overconfident high-risk decisions.
Regulation-ready
EU AI Act evidence generated from inference: logs, uncertainty, human-review hooks, and decision records for high-risk governance workflows.
See the campaign. Trace the network. Show the evidence.
Thousands of accounts. Dozens of platforms. One coordinated goal. Qriton's engines map the actors, the timing, and the message — then hand you a report your analysts and legal team can actually use.
Coordinated Campaign Detection
Identify when hundreds of accounts post the same message within seconds. Temporal bursts, copy-paste coordination, synchronized amplification — all flagged with confidence scores.
State Actor Attribution
Map influence operations back to their origin — state media, malicious actors, diplomatic networks, amplification chains. See who seeds narratives and who amplifies them.
Temporal Pattern Analysis
Detect posting bursts within 60-second windows. When 26 posts land in 58 seconds across 4 countries, that's not organic conversation — that's coordination.
Network Graph Intelligence
Visualize who amplified whom. Map repost chains, identify bridge nodes between state networks, and trace how narratives cascade across communities.
Multi-Language Operations
Detect when a single campaign runs parallel translations into dozens of languages simultaneously — targeting each community at once with coordinated, localized messaging.
Evidence-Grade Reporting
Export campaigns as PDF, JSON, or CSV — ready for regulatory submission, parliamentary inquiry, or journalistic investigation. Every detection is explainable and auditable.
Tested against real operations.
Validated on a 39-hour coordinated influence campaign involving multiple malicious state actors — 785 accounts across 9 countries. All six coordination signatures identified automatically.
Protection you can trust because you can read every decision it makes.
Seven layers of detection, graduated response that matches the threat, and a clear explanation for every action taken. Built for infrastructure teams that need defense they can inspect.
7-Layer Detection
Network, transport, application, AI, semantic content, state integrity, and collective intelligence — seven layers working together.
Active Defense
Don't just block attackers — waste their time. Tarpits, honeypots, and behavioral challenges that drain attacker resources while real users pass in seconds.
Auto Subnet Blocking
Five bad actors from the same /24? The entire subnet gets blocked. Botnet infrastructure neutralized with a single rule — up to 4.2M IPs per range.
Graduated Response
Four tiers — allow, rate-limit, challenge, block — escalating proportionally. Threat modes auto-adjust from relaxed to lockdown based on attack intensity.
Explainable Decisions
Gradient-based attribution shows why each threat was blocked. Not a confidence score — a reasoning trace designed for audit, review, and high-risk AI governance.
Federated Intelligence
One shield's detection instantly protects every other shield in the network. Shared memory, distributed defense. Attack one node — the entire network remembers.
Proven in production.
A 7-day coordinated attack campaign across three continents. SYN floods, botnet waves, after-hours probes. Shield handled all six attack phases with automated response and reviewable evidence.
Available on
Windows
Linux
Node.js
Docker
ARM64
Know what's failing, why, and how long you have — before it costs you.
Sensors, maintenance logs, images, safety context — one system that reads it all and converges to a diagnosis. You get severity, root cause, remaining useful life, and the evidence behind the recommendation.
Ingest
Sensor time series, maintenance logs, operator notes, inspection images, 3D point clouds, and live safety scenes — streamed or uploaded. The system reads the modalities your plant generates every day.
Diagnose
HLM evaluates equipment state, defect likelihood, safety condition, root cause, and urgency — in one pass. Not five tools stitched together. One architecture that converges to a diagnosis.
Report
Plain-language diagnostic with severity score, intervention window, and a replayable audit record tied to the actual inference path. Hand it to your auditor, not a screenshot.
Remaining Useful Life
Not a vague "at risk" flag — a time window. "Compressor seal wear progressing. Estimated intervention: 18 hours." Your maintenance team can actually schedule around that.
Pattern Composition
Others say "anomaly detected." Qriton says "60% bearing wear, 40% shaft misalignment." Your maintenance team knows exactly what to fix and in what order. Actionable, not ambiguous.
Intrinsic Uncertainty
The model knows when it doesn't know — from energy and iteration metrics, not a confidence score stapled on afterward. In a plant, overconfident AI is more dangerous than no AI.
Not another score.
Every vendor monitors sensors and gives you a number. The Diagnostics system gives you a decision with evidence — the inference path is the explanation, not a post-hoc guess. When a regulator asks "why did you replace that part?" you hand them the decision record, not a log file.
Maintenance Directors
Stop reacting to breakdowns. Get a diagnosis with severity, root cause, and a maintenance window — before the line stops.
Quality Leads
Every inspection decision auditable. Defect evidence mapped to parts. Compliance documentation generated from inference, not paperwork.
Plant Operations
One system for sensors, images, logs, and safety — not five vendors and middleware. Less downtime, less scrap, faster decisions, traceable workflows.
When AI affects equipment, infrastructure, patients, or money, a score is not enough.
Keep data under control
Run on customer-controlled infrastructure, from edge sensors to plant servers, when sensitive signals cannot be sent to a remote model API.
Learn moreShow the decision path
Give operators and auditors the input state, model release, convergence path, uncertainty, and final output so the decision can be replayed later.
Learn moreKnow when to review
Expose energy and convergence behavior so teams can see when the model is stable and when the case should move to a human review gate.
Learn moreAct at the right level
Not every signal deserves the same response. Monitor, flag, recommend, or block depending on severity, confidence, policy, and operational context.
Learn moreChange behavior safely
Use Energy Language for targeted behavior changes that can be inspected, verified, and rolled back instead of hidden inside another full retraining run.
Learn moreRemember what worked
Turn incidents, approved corrections, and validated patterns into reusable knowledge that teams can inspect, transfer, or retire under governance.
Learn moreWhat it solves
Qriton record
Where it matters
Current stack
Qriton model path
Typical pilots
Why this is
different.
High-stakes AI needs more than a confident answer. Qriton keeps the decision path local, inspectable, and replayable so operators can understand what happened and defend the action later.
Model state, convergence path, uncertainty, and output are part of the decision record.
Compare
Reviewers can reconstruct what happened instead of relying on screenshots or summaries.
Compare
Deployments can run on customer-controlled infrastructure when sensitive data must stay inside the operating environment.
Compare
Behavior updates can be validated, tracked, and rolled back instead of hidden inside another training run.
Compare
Current situation
Qriton approach
Newsroom
News and press,
News and press,
in one feed.
Press releases, product updates, research notes, and company announcements for teams following Qriton's Critical AI work.
Follow Qriton on LinkedIn
May 2026
Mar 2026
Mar 2026
Live
HLM early access opens the technical path for auditable AI.
Validated model families, replayable inference, uncertainty signals, and implementation notes for regulated and edge deployments.
Qriton Shield moves active infrastructure defense into production.
Seven-layer detection, graduated response, and explainable blocking decisions for infrastructure teams that need a replayable security record.
Energy Language turns model behavior into an inspectable operation.
Observe, edit, verify, and replay targeted changes in Hopfield energy basins instead of retraining for every narrow update.
Follow product progress, technical milestones, and field notes.
Short-form updates from Qriton as Shield, HLM, industrial diagnostics, and partner pilots move forward.
Ready to build AI
you can defend?
Bring Qriton the workflow where opacity is becoming a risk. Qriton will help map the model, evidence path, deployment constraints, and first pilot.
Start a ConversationShield pilots and HLM early access are available for qualified teams
About Qriton.
Qriton is a European AI company building Critical AI for infrastructure, industrial diagnostics, regulated workflows, and edge systems where failure, opacity, or data leakage are not acceptable.
Qriton Shield is in production for infrastructure defense. HLM and Energy Language extend the same architecture into auditable model families, targeted behavior updates, and replayable decision records.
Qriton builds systems operators can inspect, auditors can review, and teams can run under their own control.
Let's talk.
Tell Qriton what decision needs to be local, explainable, or replayable. Qriton will map the use case, deployment constraints, evidence path, and first pilot route.