Smart Solutions, Real Impact
Your Vision, Our Craft
Connecting Your World
Mobiloitte delivers Edge & IoT AI with quantized models, federated learning, streaming analytics, and offline-first decision loops, enabling devices to sense, decide, and act instantly.
Compressed, quantized, and pruned models on hardware provide low latency for low-power on-device machine learning.
Train on the device, send only gradients/metadata, and keep raw data local to protect privacy.
Pruning, distillation, int8/4-bit quantization, operator fusion, and DSP/NPU acceleration.
Do first processing at the edge with buffering and offline tolerance, then smart-sync to cloud.
Spot drift, faults, and wear early edge AI for predictive maintenance that reduces downtime across the fleet.
Signed, encrypted rollouts with canaries, health checks, and safe rollback.
Work through poor networks using local consensus, fallback logic, and store-and-forward.
Keep PII local, encrypt data, and enforce retention to stay compliant without centralizing risk.
Use small LLMs and embedders on gateways/edge servers; run local RAG over nearby stores.
Model device behavior and replay edge events to test before wide rollout..
Registries, A/B on device fleets, telemetry, drift detection, and auto redeploys edge MLOps for device fleets.
Manufacturing, energy, automotive, healthcare, smart cities, agriculture, logistics, and more.
Moving a model to a device is not enough. Mobiloitte designs for latency, privacy, power, and flaky networks and adds monitoring, safe over-the-air updates, and clear rules. Device-aware models, federated learning, and real-time analytics help operations run faster, safer, and at lower cost.
Well codedTestable firmware and instrumented edge services.
ResponsiveDevOps and MLOps built for field conditions, not just the lab.
Fast growing Sized for dozens to millions of devices.
Multipurpose One design for vision, audio, sensors, and LLM-lite.
Mobiloitte Maps Use Cases to Hardware Limits, Picks Model Families and Optimisations, and Bakes in OTA, Observability, and Security So Deployments Succeed in the Field.
Split plan for on-device vs. gateway capabilities
Model compression/quantization plan and edge MLOps
Mesh design, sync strategies, and connectivity tolerance
Policies for security, OTA governance, and rollback
Compliance map (PII, healthcare, industry, defense)
Digital Twins and Field Pilots Prove Optimized Models, On-Device Runtimes, Edge Analytics, Command/Control, Telemetry, and OTA Pipelines.
Quantized/distilled models on DSP/NPU/CPU/GPU
Streaming with buffering for edge-first analytics
Privacy-preserving federated learning and analytics
Safe OTA deployments with canary and rollback
Fleet dashboards for health, drift, performance, anomalies
Mobiloitte Runs Edge MLOps: Model Registries, Drift Checks, A/B, Rollback, Device Hardening, Cert/Key Rotation, Telemetry Analytics, and Continuous Tuning for Power, Cost, and Accuracy.
Edge model registry and rollout policies
Telemetry-driven anomaly detection and retraining
Power, latency, and accuracy monitoring
24/7 SLAs, field-ops support, and roadmap updates
Check feasibility, size models, design OTA/security, and test with digital twins.
Ship firmware/models, control planes, federation, and analytics. Pilot on real fleets; tune power, latency, and accuracy.
Monitor, retrain, redeploy, secure, and scale looping back whenever needed.
TensorRT • ONNX Runtime • TVM • TFLite Micro • vLLM (edge servers) • PyTorch Mobile • NVIDIA Jetson • Qualcomm DSP • ARM NN • Kafka/Flink/Spark • MQTT/AMQP • K3s/K8s at the edge • MLflow/W&B • Mender/OTA • Zero-trust, TPM, PKI
Mobiloitte designs for minimal data movement, encryption, attestation, RBAC, signed OTA, firmware integrity checks, and simple audits. The edge stack stays secure by design for regulated healthcare devices, industrial safety, or defence.
By using quantisation, pruning, distillation, operator fusion, and hardware accelerators (DSP/NPU). Mobiloitte balances accuracy, latency, and power for each form factor. Heavy models can sit on gateways or edge servers to avoid cloud round trips.
Use it when privacy, bandwidth, or policy prevents raw data from leaving devices. Only gradients or small stats are shared. Differential privacy, secure aggregation, and drift checks strengthen the setup.
Pipelines buffer locally, apply fallback rules, and use store-and-forward with local consensus. Devices keep working offline and sync state and events later. Important actions are logged for replay and audit.
Updates are signed and encrypted, rolled out with canaries and health checks, and rolled back on failure. Audit trails record every change. Model registries and version pinning keep fleets consistent.
Lightweight XAI, feature-level logs, and model cards show behaviour and limits. For high-stakes devices, inferences can be mirrored centrally for audit. The aim is reliable fieldwork with central accountability.
Pilots monitor power draw, throttling, and inference budgets. Policies adjust precision, batch sizes, and duty cycles to stay within limits. Models switch profiles when devices heat up.
Yes—small LLMs and embedders can run on gateways/edge servers with local RAG. If full on-device is not possible, a hybrid model splits retrieval and classification to the edge and heavy generation to the cloud under strict cost and privacy rules.
Models are versioned, rolled out in rings, checked for drift, and redeployed by policy. Telemetry drives retraining. Treat models like firmware: observable, reversible, and governed.
Depending on the industry: IEC 62443, ISO 27001, SOC2, HIPAA, GDPR, and others. Controls map to the assurance case needed for audits.
A pilot with models, firmware, OTA, and analytics for a small fleet often takes 4–8 weeks. Federation, mesh resilience, and scaled telemetry usually add 8–16 weeks, depending on fleet size and constraints.
Yes. Devices detect issues and schedule checks locally, then sync features and results for central analysis and retraining. This cuts latency and bandwidth while keeping key detections near the sensor.
Secure boot, attestation, TPM-backed keys, encrypted storage, signed models, and runtime checksums are used. Sensitive logic can stay on the server or gateway with minimal exposure on the device. All access is monitored and logged.
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Stop sending everything to the cloud and waiting. With Mobiloitte, devices learn, decide, and act on the edge safely, reliably, and at scale.