Services

What we do

Six practices that share a spine. Read them in order and they run from the smallest silicon we work on to the platform at the other end.

EdgeCloud
  1. 01

    Microcontroller

    64 KB – 2 MB, no NPU

  2. 02

    Embedded Linux

    512 MB – 2 GB

  3. 03

    Edge accelerator

    up to 67 TOPS

  4. 04

    On-prem server

    GPU

  5. 05

    Cloud

    elastic

Each practice names the range of compute it works in. Most engagements touch two or three of them.

Our Practices

Six practices, one continuum

The interesting engineering is not in the model. It is in making a model that was trained on a workstation survive on a part with kilobytes of RAM, no accelerator, and a power budget it has to share with everything else on the board.

The layer that decides whether any of the rest ships. Bring-up, drivers, capture pipelines and the unglamorous work of making a build reproducible on someone else's hardware.

Three sensing modalities, one discipline: turning a raw signal into a decision close to where it was captured. Radar is the one most teams skip, and often the one the application actually needed.

Most generative work fails on retrieval, not generation. We build the corpus handling and evaluation first, then decide how small the model can be, including small enough to run with no connection at all.

The classical work, done properly. Method chosen to fit the problem class and the data you actually have, rather than one approach applied to everything.

Before a model is worth building, the data has to arrive reliably and mean the same thing every time. Often the hardest part is the system that already exists and cannot be replaced.

Capabilities

  • Post-training quantization and QAT
  • Pruning and architecture reduction
  • Cycle and memory budgeting
  • Power profiling and duty-cycle design
  • On-device inference runtimes
  • Accuracy recovery after compression

Tools

TensorFlow Lite Micro · ONNX Runtime · CMSIS-NN · DeepCraft · Hailo · Jetson · PSoC6 · SAMA7 · STM32MP

Capabilities

  • Board bring-up and BSP work
  • Embedded Linux, Yocto and Buildroot
  • MIPI camera and sensor pipelines
  • Video capture and streaming
  • Python to C++ conversion and optimisation
  • Cross-compilation and release builds

Tools

Yocto · Buildroot · GStreamer · V4L2 · OpenCV · C/C++ · Rust · Zephyr · FreeRTOS

Capabilities

  • Object detection and tracking
  • Surface and PCB defect inspection
  • Face detection, recognition and liveness
  • FMCW radar: presence, counting, gesture
  • Range-Doppler and angle-of-arrival processing
  • Keyword spotting and acoustic anomaly detection

Tools

60 GHz FMCW · raw ADC capture · CFAR · YOLO family · RetinaFace · OpenCV · multi-vendor radar front-ends

Capabilities

  • Retrieval over manuals and technical corpora
  • Chunking, embedding and hybrid retrieval
  • On-device LLMs and offline assistants
  • Vision-language models for anomaly description
  • Agent and tool-calling pipelines
  • Evaluation sets and regression testing

Tools

RAG · hybrid retrieval · rerankers · quantized 1B–8B models · VLMs · streaming TTS · tool-calling agents

Capabilities

  • Predictive maintenance and failure modelling
  • Forecasting and demand modelling
  • Dimensionality reduction on wide feature sets
  • Structured experimental data analysis
  • Anomaly and outlier detection
  • Model evaluation, drift and monitoring

Tools

scikit-learn · PyTorch · XGBoost · time-series methods · dimensionality reduction · experiment tracking

Capabilities

  • Pipeline design and orchestration
  • Warehouse and lakehouse modelling
  • Legacy and informatics system integration
  • Data quality, lineage and validation
  • Streaming and batch ingestion
  • Platform migration without downtime

Tools

Python · SQL · Airflow · dbt · Spark · Kafka · Snowflake · Databricks · Foundry · on-prem warehouses