Edge inference
Capture, decode, pre-process and execute qualified models on the right site hardware.
Shared delivery capabilityEdge & Production Engineering
The production system
around the model.
Edge inference, real-time services, operator products, integrations and fleet operations engineered as one observable system.
The problem
What has to work.
Production operation brings source health, workload performance, operational state, integrations, rollout, observability and recovery together across every qualified site.
Who it is for
Edge AI product teamsMulti-site operationsComputer vision teamsIndustrial and physical-system operatorsPlatform teams responsible for deployed fleets
What ApexFlo provides
What the engagement delivers.
Real-time services
Correlate events, maintain operational state and expose reliable APIs to products and external systems.
Operator products
Turn system events into review, configuration, investigation and action workflows.
Fleet operations
Deploy, observe, update and support workloads across sites with explicit lifecycle and recovery controls.
What is delivered
✓Qualified end-to-end workload
✓Edge and real-time platform software
✓Operator and enterprise integrations
✓Fleet deployment, monitoring, update and recovery controls
The production stack
From physical signal
to controlled operation.
Capture
Acquire camera, audio, sensor or machine data with source health, timing and quality visible.
Inference
Pre-process and run qualified models against the exact hardware, precision and workload profile.
Event and state
Correlate detections, maintain operational context and expose dependable real-time services.
Operator and integration
Connect evidence to review, action and the enterprise systems that own the wider workflow.
Fleet operations
Configure, observe, update, recover and support the deployed system across sites.
Fleet lifecycle
Deployment is
the start of operation.
A production platform needs a repeatable path for versioning, rollout, visibility and recovery across every qualified site and hardware profile.
01Qualify
02Package
03Deploy
04Observe
05Update
06Recover
Versions, configuration, health, logs and recovery status must remain visible after the installer leaves the site.
Workload validation
Qualify the complete workload,
not only model inference.
Workload
Representative streams, event rates, model graph, precision, latency and throughput targets.
Environment
Target hardware, network behavior, power, thermal limits, site access and failure domains.
Operation
Users, review workflow, integrations, retention, support ownership and acceptable degraded modes.
Acceptance
Quality, performance, resilience, upgrade and recovery tests recorded for the qualified configuration.
Delivery evidence
Proof from delivery.
Validation on real systems.
Live 25-store retail PoC
The first-stage retail PoC combines transaction ingestion, camera correlation and operator review across 25 stores. Scale-up is in progress.
Three accelerator reference configurations
Intel Core Ultra, NVIDIA Jetson Orin and Axelera Metis work demonstrates bounded model and workload qualification on exact lab configurations.
Deployment considerations
- Performance and compatibility are recorded for the qualified workload, hardware, software versions and environmental profile.
- Fleet scale, support coverage and recovery objectives are agreed for each deployment.
Next engagement step