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QURE Science

The execution layer for lab work.

Accelerate discovery with an execution platform engineered for scientific rigor. QURE Science unifies augmented-reality execution, science-native AI, telepresence collaboration, and governed data pipelines—so every experiment is reproducible, traceable, and fast.

Reproducible Execution Across Teams & Sites

Ensure every scientist performs protocols the same way—every time. AI-anchored AR guidance removes operator-dependent variability, enforces correct technique in real time, and validates each step as it happens. Standardization scales across shifts, geographies, CRO partners, and program phases.

Protocol Integrity & Error Prevention

QURE Science detects deviations, timing drift, incorrect tool use, ergonomic risks, and environmental mismatches—then corrects them instantly. This reduces failed runs, rework cycles, reagent waste, and batch inconsistencies.

Regulatory-Ready Data, Automatically Captured

Every step is captured with time-stamped logs, versioned protocols, and full chain-of-custody metadata. Generates QA- and GxP-aligned evidence packages suitable for submissions and audits.

AI-Assisted Experiment Planning & Optimization

Agent copilots analyze historical runs, literature, telemetry, and simulation outputs to identify failure points, propose next-step experiments, and accelerate hypothesis → execution → insight cycles.

Telepresence for Real-Time Expert Oversight

Scientists, automation engineers, and computational teams can “beam in” remotely to observe live execution through the operator’s AR view. They can annotate, guide, verify critical steps, and collaborate across sites—reducing tech-transfer friction, accelerating troubleshooting, and enabling distributed experimentation.

Unify Your Lab Stack

QURE Science harmonizes wet-lab instruments, robotics, ELNs, LIMS systems, and computational models—forming a governed, lineage-tracked institutional knowledge graph.

ELN & LIMS synchronization
Robotics & automation orchestration
Simulation engine connectors
Secure cloud, on-prem, and air-gapped deployment

AI Scientist Ready

Integration with AI Scientist Systems

QURE Science provides the structured, validated execution data that autonomous discovery systems depend on.

Its real-world semantics enable:

Closed-loop wet-lab optimization
Reliable protocol generation
Model-to-lab feedback
Autonomous discovery cycles

QURE becomes the execution substrate that makes AI Scientists viable.

Outcomes We Deliver

  • Significant reproducibility gains across scientists, labs, and partners
  • Shorter experiment cycles with AI-guided design and error-preventing execution
  • Higher regulatory confidence via fully validated, auditable records
  • Reduced operational cost from fewer failed experiments & repeats
  • AI Scientist readiness through structured, high-integrity execution data
  • Creation of a World Model Dataset from validated human behavior, deviations, environment conditions, and outcomes—fueling next-generation autonomous discovery
  • Seamless collaboration & remote oversight through telepresence-enabled execution

Questions about XR + AI for labs

Inclusive guidance built for real-world labs

How does XR guidance improve reproducibility?
Instrument-aware AR cues lock in protocol timing, positioning, and technique so different operators across sites execute the exact same steps and validations.
What compliance controls are built in?
Each action is time-stamped with role-based attestations, SOP versions, and environmental metadata, producing records aligned to GxP expectations.
Do headsets integrate with existing lab systems?
Yes—XR devices stream telemetry and capture observations while syncing with ELNs, LIMS, robotics controllers, and MES for closed-loop orchestration.
How is telemetry and video kept private?
Data routes through encrypted channels with strict access policies; sensitive frames can be redacted on-device before storage or sharing.
Can the platform operate in constrained or offline labs?
Adaptive caching and edge inference let operators continue guided work during connectivity drops, syncing securely when links are restored.
How is world-model training handled responsibly?
Validated execution traces, deviations, and outcomes feed world-model datasets with governance controls, opt-in scopes, and auditability for every contribution.
What safeguards prevent AI guidance from drifting?
AI-generated prompts are versioned, tested against golden runs, and gated by human review before deployment to production protocols.