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Use Cases

Seven concrete onboarding scenarios across the Qriton HLM stack, from laptop-class language assistants down to battery-powered sensor nodes. Each scenario lists the target persona, the problem, which models + tools apply, a step-by-step walkthrough, and the honest caveats.

Where to start

Pick the scenario that most resembles your situation. Each page is self-contained — you don't need to read the others first.

For regulated-industry teams

Use casePersonaStack
EU AI Act-compliant credit-scoringFintech compliance officer, ML engineerHLM3 + audit certificate
Medical imaging with provenanceClinical-AI product lead, radiology partnerHLM-Spatial Medical3D + audit

For edge / industrial teams

Use casePersonaStack
Industrial anomaly detection on factory floorManufacturing data engineer, maintenance opsHLM-Micro + ESP32 + MPU6050
Sovereign edge sensor networkDefense / critical-infrastructure PM, systems integratorHLM-Micro × N nodes + hash-chain audit
Wearable health monitoringConsumer wearable PM, health-tech engineerHLM-Micro-har / ecg + phone or Arduino

For AI product / research teams

Use casePersonaStack
Programmable AI assistant personaChatbot PM, LLM-app developerHLM3 + HLM-Audio + Energy Language surgery
Research notebook for interpretabilityML researcher, interpretability scientistHLM3 + Jupyter + Energy Language

How each scenario is organised

Every use-case page has the same six sections so you can scan quickly:

  1. Who this is for — the target persona(s)
  2. The problem you're solving — what breaks today without Qriton
  3. What you'll build — the concrete artefact at the end of the walkthrough
  4. The stack — which Qriton pieces apply, and what external tools you bring
  5. Walkthrough — step-by-step, with commands / code
  6. Caveats & what to read next — honest limits, and where to go from here

Not sure where to start?