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.

<10ms

Inference Latency

Coin Cell

Battery Compatibility

No Cloud

Required for Inference

87%+

Anomaly Detection Accuracy

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.

icon TensorFlow Lite Micro (TFLite Micro)
icon Edge Impulse (end-to-end TinyML platform)
icon ARM CMSIS-NN (Cortex-M optimization)
icon Arduino / ESP-IDF / Zephyr RTOS
icon AWS SageMaker (cloud training)
icon AWS IoT Greengrass (edge-cloud hybrid)

Who This Is For

INDUSTRY

VERTICAL APPLICATION

Predictive Maintenance

Vibration and acoustic anomaly detection on motors and pumps – running directly on the machine’s embedded controller.

Quality Inspection

Visual defect detection on production lines using ultra-compact vision models running on camera modules without cloud connectivity.

Audio Classification

Equipment fault detection from audio signatures – identifying abnormal sounds indicating bearing wear, belt slippage, or lubrication failure.

Gesture Recognition

Hand gesture and motion recognition for touchless HMI interfaces on industrial equipment.

Wearable Safety

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.

icon No sales pressure icon Senior engineer call icon Custom ROI estimate

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.

ROI & Bussines Impact

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.

Equipment predictive maintenance via TinyML delivers average savings of $800K–$2M/year for large manufacturing operations through reduced unplanned downtime.

Let’s Connect

Trusted by

Flexible, fast, and focused — Sphere solves your tech and business challenges as you scale.

Luke Suneja

Client Partner

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Hear From Our Clients

Sphere Partners
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).

Sphere Partners
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.

Sphere Partners
Mark Friedgan CEO at CreditNinja

Sphere consistently prioritizes the needs of their clients, demonstrating both agility and teamwork. They bring innovative and well-considered solutions, consistently surpassing my expectations.

Sphere Partners
René Pfitzner Co-Founder at Experify

Sphere provided excellent full-stack development manpower to augment our team and work with us.

Sphere Partners
Bruce Burdick Chief Information Officer at Integra Credit

We've been working with Sphere and its excellent consultants since our founding. Their combination of offshore talent, pricing, and shift offsetting is hard to beat. They provide crucial augmentation to our in-house team. We simply couldn't achieve our production ambitions without their service.

Sphere Partners
Jemal Swoboda CEO at Dabble

The resources and developers that Sphere Software provides are skilled and have the required technical expertise to complete their tasks successfully, with the team easily scaled in either direction. The deliverables are always high-quality.

Sphere Partners
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…

Sphere Partners
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. We augmented our development team capabilities using Sphere’s developer, who works very well with our Dev Lead in Chicago. Sphere’s developer was an expert in the new system, and continues to be an expert as we evolve it.

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Sphere in Numbers

We understand that actions speak louder than words and numbers
but here are some key facts about us.

20

Years of Experience

230

Delivered Projects

200+

Senior Specialists

94%

Satisfaction Rate

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Frequently asked question

TinyML is machine learning designed to run on low-power embedded devices such as microcontrollers with very limited memory, storage, and compute. TinyML works by training compact models in the cloud, compressing them through techniques such as quantization and pruning, and deploying the final model into device firmware for local inference on sensor data.

TinyML can run on microcontrollers with as little as 256KB of flash memory when the model architecture, compression strategy, and inference runtime are chosen carefully. Sphere’s TinyML solution is built around that kind of constrained deployment, using quantization, pruning, knowledge distillation, and hardware-aware optimization to fit useful models into very small MCU footprints.

TinyML is a subset of edge AI focused on running machine learning models directly on very small embedded devices, while edge AI is a broader category that also includes gateways, industrial PCs, cameras, and larger edge hardware. TinyML is usually the better fit when the goal is low-power local inference inside the device itself rather than inference on a more capable edge computer.

TensorFlow Lite Micro is used to run machine learning inference on microcontrollers and other deeply embedded devices with very limited resources. TinyML teams use TensorFlow Lite Micro to move trained models into production firmware where local inference can happen on live sensor streams without depending on constant cloud connectivity.

dge Impulse is an end-to-end TinyML platform used for data collection, model training, testing, and deployment on embedded hardware. Companies use Edge Impulse because Edge Impulse shortens the path from raw sensor data to working edge inference, and Sphere uses Edge Impulse as part of its TinyML delivery model when clients need a faster, more structured production workflow.

CMSIS-NN optimization is a set of neural network kernels and performance optimizations designed for ARM Cortex-M microcontrollers. CMSIS-NN optimization matters because embedded machine learning performance often depends on how well the inference pipeline is tuned to the target MCU, and Sphere works with CMSIS-NN, TFLite Micro, and hardware-specific optimization paths to improve TinyML speed, memory use, and power efficiency.

TinyML development services help with data preparation, model training, compression, hardware targeting, inference benchmarking, and production firmware integration. The value is not only getting a model to run once, but making the model accurate enough, small enough, and stable enough for real-world operation on embedded devices.

TinyML can combine data from accelerometers, microphones, temperature sensors, and cameras in a multi-sensor fusion approach. Multi-sensor TinyML usually improves inference quality because the model has more context than a single-sensor pipeline, and Sphere includes multi-sensor fusion in its solution when the use case needs stronger detection accuracy or fewer false positives.

inyML continuous learning works by sending selected edge data back into a cloud training pipeline, retraining or refining the model, and deploying improved versions back to the device fleet without disrupting live inference. Sphere supports that type of continuous learning pipeline so TinyML models can improve over time instead of freezing at the quality level of the first production release.

Buyers should look for experience across the full TinyML stack: sensor data handling, model architecture, compression, TFLite Micro or Edge Impulse deployment, CMSIS-NN or architecture-specific optimization, and production firmware integration. Sphere is strongest when the project needs more than an experiment, because Sphere works across training, compression, embedded deployment, and firmware integration to turn TinyML into a real production capability rather than a lab demo.

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