High physical risk
Temperature drift, cryogenic storage depletion, gas events, door openings, power issues, and equipment failures can directly affect regulated work.
Platform for AI for Science (AI4S)
From lab operations to an AIoT physical context layer.
iLabService presents the international platform for customers and partners building toward the AI for Science era. Laboratories are the first wedge because physical conditions, equipment state, samples, inventory, people activity, and compliance evidence all shape scientific outcomes and AI-ready context.
Why labs first
Labs expose the full physical context problem in one operating environment.
Manual checks, device alarms, sample records, equipment logs, access activity, and audit evidence are often disconnected. That makes labs a practical first market and a strong proof point for a broader AIoT physical context infrastructure category.
Many manual workflows
Freezer checks, inventory counts, equipment booking, training records, waste handling, and audit prep still rely heavily on people.
Clear compliance need
Events need traceable records, responsible people, response history, and evidence that can survive audit review.
Platform stack
A physical-to-intelligence stack for regulated laboratories and AI for Science (AI4S).
Read the stack from bottom to top: configurable hardware captures the physical world, Sci-Edge provides offline-first local intelligence where sites need autonomy, lab applications govern the work, and the physical context layer makes that operating reality usable for data intelligence and AI4S.
Data Intelligence and AI4S
Contextual operating data supports anomaly detection, prediction, reproducibility review, automation, and scientific decision support.
Physical Context Layer
Time-stamped, auditable context connects assets, locations, samples, materials, people, actions, thresholds, and response history.
Lab Applications
Guardian, Equipment, Inventory, Samples, Space, Procurement, Qualification, and Waste bind signals to daily regulated workflows. View the software catalog.
Sci-Edge
Offline-first edge nodes run protocol adaptation, local rules, agents, buffering, distributed coordination, and edge AI compute before data leaves the site.
Hardware Layer
Sensors, meters, access devices, smart cabinets, adapters, labels, and workflow terminals capture physical signals close to the work.
How context moves upward
Capture
Temperature, humidity, CO2, cryo storage, gas, camera gauge readings, power, freezer compressor status, door, occupancy, equipment state, and material movement.
Contextualize
Map signals to assets, rooms, samples, reagents, people, policies, experiment windows, and response ownership.
Govern
Create alerts, tasks, escalation paths, audit records, exception reviews, and structured evidence for regulated teams.
Value for AI4S
AI for Science needs the physical context behind the scientific record.
ELN, LIMS, and instrument files explain part of what happened. In regulated labs, scientific outcomes are also shaped by room conditions, storage history, equipment state, access activity, material movement, training status, and incident response. iLabService makes that physical context computable.
Building an AI4S application that needs physical ground truth? Use trusted physical context to strengthen anomaly detection, reproducibility scoring, predictive maintenance, and workflow automation.
Partner as an AI builder Scientist workflowEnvironmental and utility state
Temperature, humidity, gas, cryogenic storage, oxygen safety, pressure, power, and door disturbance become continuous context.
Assets, samples, and materials
Equipment usage, sample storage, inventory movement, reagent expiry, and maintenance history are tied to time and place.
People, actions, and qualification
Access, task ownership, training status, escalation, and response records connect physical events to responsible workflows.
Anomaly detection
Models can compare live conditions, equipment behavior, material movement, and historical baselines to surface unusual operating patterns earlier.
Reproducibility scoring
Experiment windows can be reviewed against disturbance, storage, equipment, environmental, and operator histories that were previously hidden.
Predictive maintenance
Utilization, power, condition, calibration, and service context can support earlier maintenance signals for critical instruments and utilities.
Regulated deployment readiness
Answer the questions QA, Compliance, IT, and scientists ask before rollout.
iLabService does not replace a customer's validation responsibility. It provides structured product, deployment, and evidence artifacts that teams can assess within their own quality system.
IQ/OQ/PQ support
Deployment records, configuration evidence, sensor mappings, test scripts, and operating checks can support customer validation packages.
ALCOA+ aligned records
Time-stamped source context, user actions, review trails, and event histories help QA evaluate attributable and reviewable evidence.
LIMS, ELN, and equipment data
Physical context can be connected with existing lab records, instrument files, and notebook workflows rather than replacing them.
Deployment-specific hosting review
iLabService supports deployment planning around customer data-residency requirements, with region, access, export, retention, and integration boundaries defined before rollout.
Expansion logic
The same pattern extends beyond laboratories.
The lab wedge proves the core architecture: capture physical state, contextualize it against assets and people, govern the response, and make the resulting data computable. That pattern applies to other regulated operations where physical state and evidence matter.
Regulated laboratory operations
Freezers, cryo storage, gas, inventory, samples, equipment, people qualifications, waste, and audit workflows.
AIoT physical context layer
Hardware signals become structured context tied to assets, locations, people, events, workflows, and records.
Regulated physical operations
Extend the same layer into other environments where safety, compliance, uptime, and operational evidence are critical.
Integrated command center
One platform for integrated lab physical contexts.
Command-center views bring equipment, inventory, rooms, environmental conditions, gas supply, booking, service, alerts, and response records into one operating layer instead of leaving each physical context in a separate dashboard.
Physical signals
Temperature, humidity, CO2, gas, camera gauge readings, door, power, asset state, and inventory movement become live operating context.
Operational ownership
Bookings, work orders, refill tasks, service notifications, and response actions are tied back to the physical event.
Evidence continuity
Teams can move from a room or device view into alerts, exceptions, reviews, and audit-ready records without rebuilding the timeline.
Integrated intelligence
Equipment, inventory, environment, safety, and utilization data become a shared layer for analytics and AI-ready operations.