Machine Learning at the Edge. Instant Inference. Zero Cloud Dependency.
Sphere's TinyML Edge Intelligence solutions deploy trained ML models directly onto microcontrollers and embedded devices – enabling real-time AI inference for anomaly detection, gesture recognition, predictive maintenance, and image classification without any cloud connectivity. Sub-10ms latency. Weeks of battery life.
Organizations around the world trust us
Why This Matters Now
Most industrial IoT AI applications require sending data to the cloud for inference – adding 100–500ms latency, cellular/Wi-Fi connectivity costs, and privacy exposure for sensitive operational data. For use cases requiring instant response (equipment safety shutoffs, real-time quality inspection, gesture control), cloud-dependent AI simply isn't fast enough or reliable enough.
1. Cloud Latency Kills Real-Time Use Cases
Sending sensor data to the cloud, running inference, and receiving a response adds 100ms–2 seconds of latency – unacceptable for safety systems, quality inspection, and real-time control.
2. Always-On Connectivity Isn't Always Available
Remote industrial sites, underground facilities, and mobile assets frequently have intermittent or no connectivity. AI that requires the cloud fails the moment connectivity drops.
3. Sending Raw Sensor Data Creates Privacy Exposure
Industrial processes, proprietary manufacturing data, and sensitive operational information should not be transmitted to external clouds – TinyML keeps data local.
What Sphere Delivers
Sphere's TinyML practice combines model architecture expertise, hardware-specific optimization, and deployment tooling to train, compress, and deploy ML models on microcontrollers with as little as 256KB of flash memory. We work across the full TinyML stack – from data collection and model training through CMSIS-NN optimization and production firmware integration.
Model Training & Compression
Train custom ML models on your sensor data and apply quantization, pruning, and knowledge distillation techniques to reduce model size by 10–100x without significant accuracy loss.
Hardware-Optimized Inference
Deploy models optimized for your specific MCU architecture – ARM Cortex-M (CMSIS-NN), RISC-V, or Xtensa – using TensorFlow Lite Micro or Edge Impulse framework.
Continuous Learning Pipeline
Cloud-connected model retraining pipeline feeds new edge data back to improve model accuracy over time – without disrupting production inference operations.
Multi-Sensor Fusion
Fuse data from accelerometers, microphones, temperature sensors, and cameras for higher-accuracy inference than single-sensor approaches.
Edge Impulse Platform Integration
Certified Edge Impulse partner – Sphere uses the platform's end-to-end workflow for data collection, model training, testing, and deployment.
Production Firmware & Edge Integration
Embed TinyML inference directly into production firmware so models work inside the real device, not only in a lab demo. Sphere integrates model execution with sensor pipelines, event logic, power constraints.
Built On Industry-Leading Technology
Our TinyML offering is built on a practical stack for training, optimizing, and deploying machine learning models on constrained edge devices. The architecture combines embedded inference frameworks, hardware-level optimization, real-time firmware environments, and cloud-based training services so teams can move from raw sensor data to production-ready edge intelligence on microcontrollers with very limited memory and compute.
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Who This Is For
Vibration and acoustic anomaly detection on motors and pumps – running directly on the machine's embedded controller.
Visual defect detection on production lines using ultra-compact vision models running on camera modules without cloud connectivity.
Equipment fault detection from audio signatures – identifying abnormal sounds indicating bearing wear, belt slippage, or lubrication failure.
Hand gesture and motion recognition for touchless HMI interfaces on industrial equipment.
Worker safety monitoring for falls, hazardous postures, and heat stress – running on body-worn sensors with days-long battery life.
See TinyML Running on Your Hardware in 2 Weeks
Sphere's TinyML engineers will run a proof of concept on your target hardware – collecting sensor data, training a model, and demonstrating inference on your actual MCU – within 2 weeks. You'll see exactly what's possible before committing to a full project.
How It Works
Data Collection
Deploy data collection firmware on target hardware. Collect labeled sensor data across normal and anomalous operating conditions.
Model Training
Train candidate models on collected data. Evaluate accuracy, latency, and memory footprint tradeoffs across model architectures.
Optimization
Apply quantization and pruning to achieve target memory/compute budget. Profile on target hardware for latency validation.
Firmware Integration
Integrate optimized model into production firmware. Deploy cloud retraining pipeline to improve model accuracy as new edge data is collected from the production fleet.
Data Collection
Deploy data collection firmware on target hardware. Collect labeled sensor data across normal and anomalous operating conditions.
Model Training
Train candidate models on collected data. Evaluate accuracy, latency, and memory footprint tradeoffs across model architectures.
Optimization
Apply quantization and pruning to achieve target memory/compute budget. Profile on target hardware for latency validation.
Firmware Integration
Integrate optimized model into production firmware. Deploy cloud retraining pipeline to improve model accuracy as new edge data is collected from the production fleet.
ROI & Business Impact
Eliminate Cloud Inference Costs
TinyML implementations eliminate cloud inference costs entirely for high-frequency use cases – saving $50K–$300K/year in cloud compute for applications with 100+ inferences per second per device.
Predictive Maintenance Savings
Equipment predictive maintenance via TinyML delivers average savings of $800K–$2M/year for large manufacturing operations through reduced unplanned downtime.
Hear from
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Lee Ebreo
VP of Engineering at Credit Ninja
These things would not have been achievable if we did not build our own in-house system and if we did not partner with Sphere to help us achieve our goals.
Selah Ben-Haim
VP of Engineering at Prominence Advisors
Our experience with Sphere and their team has been and continues to be fantastic. We keep throwing new projects at them, and they keep knocking them out of the park (including the rescue of a project that was previously bungled by another vendor).
Ben Crawford
Senior Product Manager at Enova Financial
I would expect to be delighted. It's been a really positive experience, working with Sphere, and I would expect you to have the same.
Mark Friedgan
CEO at CreditNinja
Sphere consistently prioritizes the needs of their clients, demonstrating both agility and teamwork. As an offshore team, they have been an integral part of our organization and we plan to continue growing with them.
René Pfitzner
Co-Founder at Experify
Sphere provided excellent full-stack development manpower to augment our team and help push our product forward. They are easy to work with, tech-savvy and proactive.
Bruce Burdick
Chief Information Officer at Integra Credit
We've been working with Sphere and its excellent consultants since our founding. I've found that they are true partners in the success of our business.
Jemal Swoboda
CEO at Dabble
The resources and developers that Sphere Software provides are skilled and have the required technical expertise, but more importantly, they have helped us build a culture of excellence within our team.
Arthur Tretyak
Founder and CEO at IntegraCredit
With Sphere, we were able to migrate in half the time it would take to train an additional FTE… and for a fraction of the cost. Our experience with Sphere has been exceptional.
Lee Ebreo
VP of Engineering at Credit Ninja
These things would not have been achievable if we did not build our own in-house system and if we did not partner with Sphere to help us achieve our goals.
Selah Ben-Haim
VP of Engineering at Prominence Advisors
Our experience with Sphere and their team has been and continues to be fantastic. We keep throwing new projects at them, and they keep knocking them out of the park (including the rescue of a project that was previously bungled by another vendor).
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