Real device benchmarking
Every claim about speed, memory and power is measured on your target hardware, not estimated from a datasheet.
Edge intelligence
Embedded AI
Intelligence that runs on the device itself. We build vision, audio and sensor models that fit on wearables, cameras, machines and gateways, so decisions happen in milliseconds without a round trip to the cloud.
Under 30mson device inference on a low power chip
4x to 20xsmaller models after compression, with accuracy held
0 bytesof raw sensor data leaving the device when privacy demands it
Fleet widemodel updates delivered like firmware, with rollback
Why run AI on the edge
Some decisions cannot wait for the network, and some data should never leave the device
A safety cut off, a gesture, a defect on a fast line, a fall detected on a wristband. These need an answer in milliseconds, and a connection that might drop is not good enough.
Running the model on the device also keeps raw camera, microphone and sensor data local. Only a result crosses the network, which is often the difference between a product that is acceptable to deploy and one that is not.
The challenge is fitting a capable model into a tight budget for memory, compute and battery. That is the engineering we do: shrink the model, use the hardware fully and keep the whole fleet updatable.
- 01Model efficiency
Fit a real model into a small budget
We take a model that works and make it small and fast enough for the target chip through quantisation, pruning, distillation and architecture changes, measuring accuracy at every step.
The result runs within the memory, latency and power envelope of the device, with headroom for the rest of the firmware to do its job.
- Post training and quantisation aware approaches to 8 bit and below
- Structured pruning and knowledge distillation
- Hardware aware architecture search
- Accuracy, latency and power measured on the real board
- 02Sensing and integration
From raw sensor to reliable signal
Cameras, microphones, accelerometers, radar, temperature, current. We handle capture, filtering and fusion so the model sees clean input, and we integrate the inference into your firmware and real time constraints.
Where several sensors tell part of the story, fusion combines them into one confident decision rather than three noisy ones.
- Signal conditioning and calibration on device
- Multi sensor fusion for robust detection
- Integration with RTOS, bare metal or embedded Linux
- Deterministic timing for control critical paths
- 03Fleet lifecycle
Update and monitor models across every device
A deployed model is not finished. We build the pipeline to push new models to the fleet over the air, staged by cohort, with health checks and automatic rollback if a release misbehaves.
Aggregated, privacy respecting telemetry comes back so you can see accuracy in the field and gather hard cases for the next training round.
- Signed model packages delivered like firmware
- Staged rollout by cohort with rollback
- On device metrics without exposing raw data
- Hard case capture to improve the next model
What we bring to an embedded AI build
The practices that make edge intelligence dependable in the field, not just on the bench.
Privacy by design
Raw media and sensor streams stay on the device by default, with only results or aggregates transmitted.
Updatable from day one
The over the air path for models is built and tested before launch, so improvements are routine.
Firmware fit
Inference is integrated into your build system, memory map and timing budget, with your engineers involved throughout.
Field observability
Lightweight telemetry shows how the model performs across the fleet and flags devices that need attention.
Reproducible builds
Model, converter and toolchain versions are pinned, so a device built next year behaves like one built today.
Where embedded AI earns its place
Products and environments where a cloud round trip is not an option.
Wearables and health
Activity recognition, arrhythmia flags and fall detection on a device that must last days on one charge.
Industrial and manufacturing
Defect detection on a moving line, anomaly detection from vibration and current, and safety interlocks.
Smart cameras
People counting, PPE checks and licence plate reading with video that never leaves the unit.
Automotive and mobility
Driver monitoring, cabin sensing and predictive maintenance running inside the vehicle.
Voice and acoustic
Wake words, keyword spotting and machine sound monitoring with tiny always on models.
Agriculture and environment
Pest and crop analysis on solar powered field gateways with intermittent connectivity.
How an embedded AI project runs
Feasibility proven on hardware before the product design is locked.
Feasibility on hardware
We port a candidate model to your target board and measure accuracy, latency, memory and power, then report what is realistic.
Optimise to budget
Compression and architecture work brings the model inside the envelope, validated against a representative dataset.
Integrate and harden
Inference goes into your firmware with the sensing pipeline, timing guarantees and the over the air update path.
Deploy and iterate
Staged rollout across the fleet, field telemetry reviewed, and improved models shipped on a regular cadence.
Silicon and toolchains
Targets
Arm Cortex MSTM32Nordic nRFESP32NXP i.MXNVIDIA Jetson
Accelerators
Arm Ethos UHailoCoral Edge TPUQualcomm NPURockchip NPU
Frameworks
PyTorchTensorFlow Lite MicroONNX RuntimeApache TVMOpenVINOCMSIS NN
Questions about edge AI
Can our model really run on a microcontroller?
Often yes, after optimisation. Vision and audio models in the low hundreds of kilobytes are routine on modern microcontrollers with an accelerator. We start with a feasibility study on your exact hardware so you know before committing.
How much accuracy do we lose by compressing the model?
With quantisation aware training and careful pruning the drop is usually small, often within one or two percentage points. We measure it against your acceptance criteria and stop when the trade off stops being worth it.
How do we update models once devices are in the field?
We build a signed over the air pipeline that ships models like firmware, staged by cohort, with a self test on the device and automatic rollback. Model updates become a normal part of your release process.
What about devices with no connectivity?
They still work. The model runs fully offline. Updates can be applied during scheduled maintenance or via a local gateway, and telemetry can be collected on connection or on site.
Do you work with our hardware team?
Yes, closely. Embedded AI touches the board design, the memory map, the power budget and the build system, so your engineers are part of the project from the feasibility stage onward.
Tell us about the device and the decision
Share the chip, the constraints and what the model needs to detect. We will tell you what is achievable on that hardware.
Start a project Spykra Technologies UK Ltd, London and Mumbai.