The platform

What Qriton delivers.

One AI engine for the jobs where you need to show your work.

HLM handles text, images, audio, 3D, and sensor data in one system, and for every answer it also shows how sure it is and a record you can replay.

HIGH-RISK INPUT HLM Computation = Explanation DECISION + uncertainty REPLAY RECORD review metadata
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 lets you see how HLM behaves, tweak one specific behavior without retraining the whole model, and keep a record that proves what changed.

Read the HLM documentation

Operational AI

Industrial diagnostics, infrastructure monitoring, and supply-chain decisions where teams need root cause, urgency, and a replayable record.

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Perception AI

Vision, LiDAR, spatial, audio, and robotics pipelines where the model must preserve evidence about what it detected and why.

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Regulated AI

Medical, legal, finance, public-sector, and defense workflows where decisions need uncertainty, documentation, and governed model changes.

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Every decision explained

No guessing after the fact. The reasons come from the actual decision, not a separate tool bolted on later.

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

The proof the EU AI Act asks for, produced as the AI works: logs, confidence levels, human-review steps, and decision records for high-risk systems.

What happens in your company stays in your company.

Qriton Flow runs AI workflows on your own infrastructure. It connects your sources, applies the right model under your policies, and delivers finished output to the channels your organisation already uses — with a complete record of every run.

APIs & data feeds Documents & PDFs Internal systems QRITON FLOW policies · budgets · audit HLM-Critical · open models cloud models, by policy Web Reporting self-publishing, on schedule Email Distribution briefs where people read them Run Record exactly once, fully logged

Integrate

Connect APIs, data feeds, documents, and internal systems — including PDF and document intake — without custom plumbing. Flow reads what your organisation already produces.

Automate

Summarisation, classification, extraction, translation, drafting — governed by your policies and budgets. Describe the workflow in plain language and Flow drafts it for you, ready to review and run.

Deliver

Publish to the web, distribute by email, hand off to downstream systems, or produce finished documents — automatically, on schedule. The same platform serves a daily brief and a multi-stage pipeline.

Executes Exactly Once

Across retries, restarts, and redundant servers, a task runs a single time. No duplicate communications, no double postings, no repeated transactions — enforced, not hoped for.

Recovers Automatically

Interrupted work is detected and resolved within seconds. A failure is reported and contained — never left hanging for someone to find the next morning.

Your Model Strategy

HLM-Critical and open-source models on your hardware where confidentiality or cost governs; leading cloud models by policy where quality matters most. No provider becomes a dependency.

In production. Every day.

Flow operates a public media-intelligence service today — scheduled runs, unattended, with a complete operational record for every execution. It deploys inside your environment, and outbound access is governed by policy you control: what leaves the organisation is a decision, never a default.

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.

Organic activity 6 DETECTION ENGINES temporal · semantic · network linguistic · behavioral · attribution Coordinated campaign detected

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.

THREATS NETWORK TRANSPORT APPLICATION AI ENGINE SEMANTIC STATE COLLECTIVE PROTECTED 0 breaches

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.

Always on. Always accountable.

Live for months on real public-service and media infrastructure, Shield inspects every request in real time, responds in proportion, and keeps services available under heavy, abnormal load — retuning its own defenses as conditions change and logging the reason behind every decision. 125 days in production, every action explained.

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 reads it all and reaches a diagnosis. You get severity, root cause, remaining useful life, and the evidence behind the recommendation.

Sensor streams Maintenance logs Inspection images Safety scene DIAGNOSE fault state · severity root cause · uncertainty remaining useful life Decision + Evidence 60% bearing · 40% misalignment Plain-Language Report for operators and management Replay Record reviewable evidence

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

In one pass, HLM checks equipment condition, defect risk, safety, root cause, and urgency. Not five tools duct-taped together, one system that reaches 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's unsure, and that comes from how it actually works, not a confidence number 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. Diagnose gives you a decision plus the evidence behind it. The actual reasoning is the explanation, not a guess made after the fact. When a regulator asks "why did you replace that part?" you hand them the decision record, not a log file.

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.

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Show the decision path

Show operators and auditors what went in, which model version ran, how it reached the answer, how sure it was, and what it decided, so anyone can replay it later.

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Know when to review

Show when the model is steady and when a case should go to a person for review.

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Act at the right level

Not every signal deserves the same response. Monitor, flag, recommend, or block depending on severity, confidence, policy, and operational context.

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Change behavior safely

Use Energy Language to change one behavior on purpose, and check it, prove it, or undo it, instead of burying it in a full retrain.

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Remember what worked

Turn past incidents and fixes that worked into knowledge your teams can reuse, share, or retire, all under your rules.

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The difference

Why this is
different.

When the stakes are high, a confident answer isn't enough. Qriton keeps the whole decision on your systems, easy to read, and easy to replay, so your team can understand what happened and defend it later.

Inference Evidence
Model state, convergence path, uncertainty, and output are part of the decision record.
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Replayable Audit
Reviewers can reconstruct what happened instead of relying on screenshots or summaries.
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Local Control
Deployments can run on customer-controlled infrastructure when sensitive data must stay inside the operating environment.
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Governed Change
Behavior updates can be validated, tracked, and rolled back instead of hidden inside another training run.
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