Operational AI
Industrial diagnostics, infrastructure monitoring, and supply-chain decisions where teams need root cause, urgency, and a replayable record.
Learn moreFour product lines, one evidence-first architecture — engineered so every decision stays local, explainable, and replayable.
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 documentationIndustrial diagnostics, infrastructure monitoring, and supply-chain decisions where teams need root cause, urgency, and a replayable record.
Learn moreVision, LiDAR, spatial, audio, and robotics pipelines where the model must preserve evidence about what it detected and why.
Learn moreMedical, legal, finance, public-sector, and defense workflows where decisions need uncertainty, documentation, and governed model changes.
Learn moreNot post-hoc approximations. The inference path carries the evidence: model state, uncertainty, convergence, and output.
Know when the model is confident and when it's not — without bolting on calibration tools. Prevents overconfident high-risk decisions.
EU AI Act evidence generated from inference: logs, uncertainty, human-review hooks, and decision records for high-risk governance workflows.
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.
Identify when hundreds of accounts post the same message within seconds. Temporal bursts, copy-paste coordination, synchronized amplification — all flagged with confidence scores.
Map influence operations back to their origin — state media, malicious actors, diplomatic networks, amplification chains. See who seeds narratives and who amplifies them.
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.
Visualize who amplified whom. Map repost chains, identify bridge nodes between state networks, and trace how narratives cascade across communities.
Detect when a single campaign runs parallel translations into dozens of languages simultaneously — targeting each community at once with coordinated, localized messaging.
Export campaigns as PDF, JSON, or CSV — ready for regulatory submission, parliamentary inquiry, or journalistic investigation. Every detection is explainable and auditable.
Validated on a 39-hour coordinated influence campaign involving multiple malicious state actors — 785 accounts across 9 countries. All six coordination signatures identified automatically.
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.
Network, transport, application, AI, semantic content, state integrity, and collective intelligence — seven layers working together.
Don't just block attackers — waste their time. Tarpits, honeypots, and behavioral challenges that drain attacker resources while real users pass in seconds.
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.
Four tiers — allow, rate-limit, challenge, block — escalating proportionally. Threat modes auto-adjust from relaxed to lockdown based on attack intensity.
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.
One shield's detection instantly protects every other shield in the network. Shared memory, distributed defense. Attack one node — the entire network remembers.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Run on customer-controlled infrastructure, from edge sensors to plant servers, when sensitive signals cannot be sent to a remote model API.
Learn moreGive operators and auditors the input state, model release, convergence path, uncertainty, and final output so the decision can be replayed later.
Learn moreExpose 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 moreNot every signal deserves the same response. Monitor, flag, recommend, or block depending on severity, confidence, policy, and operational context.
Learn moreUse Energy Language for targeted behavior changes that can be inspected, verified, and rolled back instead of hidden inside another full retraining run.
Learn moreTurn incidents, approved corrections, and validated patterns into reusable knowledge that teams can inspect, transfer, or retire under governance.
Learn moreHigh-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.