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HardwareSoftwareAIEnd-to-end

We engineer products from silicon to cloud.

PCB to firmware to cloud to model — one accountable team.

Start a projectSee the work
  • Zynq-7000
  • Vivado
  • Verilog
  • NVIDIA Jetson
  • RWKV
  • RISC-V
  • STM32
  • ARM
  • PyTorch
  • React
  • AWS
  • Docker

Built to ship, not to theorize.

Three disciplines, one team. Most projects cross at least two — that is the point of an end-to-end partner.

01

Hardware & IoT

Custom electronics taken from schematic to manufactured, certified product.

  • PCB design & layout
  • Embedded firmware
  • FPGA & SoC bring-up
  • Sensor & RF integration
02

Software & Cloud

Web, mobile, and infrastructure engineered to scale with your product.

  • Web & mobile applications
  • APIs & microservices
  • DevOps & CI/CD
  • Cloud architecture
03

AI & Architecture

System design and machine learning that survive contact with production.

  • System architecture
  • Model training & evaluation
  • Quantization & edge deployment
  • MLOps

Numbers that speak for themselves

Quantization benchmark results from the edge AI case study
ConfigMemoryAccuracy ΔEdge-ready
FP16 baseline1.00×baselineNo
INT80.50×—Yes
INT4 (ours)0.25×< 1 ptYes

Measured on NVIDIA Jetson. 38-train / 10-test split, zero leakage. — = not published.

Selected work.

Documented like engineering: what was built, how it was measured, and what shipped.

View all 17 engagements
BlickfeldGermany

FPGA signal-processing pipeline for solid-state LiDAR

Real-time point-cloud extraction in Verilog on Zynq-7000 — waveform to points at line rate.

4.6 µs
waveform-to-point latency, point cloud at line rate
1.2 M/s
points sustained on-device
±1.8 cm
range precision after fabric calibration
38%
lower processing power vs. the prior SoC path
Zynq-7000VivadoVerilogVivado HLS
MindpeakGermany

Tumor-cell detection for digital pathology slides

Gigapixel whole-slide segmentation and cell scoring — reproducible and audit-ready.

38 s
gigapixel whole-slide inference, from 6+ minutes of manual regions
0.93
cell-detection F1 on the held-out cohort
46%
reduction in inter-reader scoring variance
3
scanner vendors validated against
PyTorchCUDATensorRTOpenSlide

Trusted by teams shipping serious technology.

Seventeen completed engagements across four regions — IoT and silicon, edge AI, industrial ML, medical vision, cloud data, and developer platforms.

CowlarPKLivestock health IoT
TrukkrPKFleet telematics
Bazaar TechnologiesPKB2B-commerce data platform
SadeemKSAEnvironmental / flood IoT
IyrisKSAAgritech greenhouse control
nyblKSAIndustrial predictive-maintenance ML
BlickfeldDESolid-state LiDAR / silicon
KonuxDERailway IoT + predictive maintenance
MindpeakDEComputational pathology / vision
Blues WirelessUSCellular IoT device + fleet
Latent AIUSEdge model quantization
MemfaultUSEmbedded observability + OTA
PriceOyePKE-commerce web + operations
AbhiPKEarned-wage-access fintech
ZidKSAE-commerce SaaS
ChocoDEB2B food-supply ordering
ResendUSDeveloper email API + portal

One team, silicon to ship.

  1. 01

    Architecture

    System design, constraints, and the plan to hit them.

  2. 02

    Hardware

    Schematic, PCB layout, firmware, and board bring-up.

  3. 03

    Software

    Applications, APIs, cloud, and the pipelines between.

  4. 04

    Deploy & iterate

    Production rollout, measurement, and the next revision.

4×

smaller model footprint, FP16 → INT4

<1 pt

accuracy delta vs. FP16 baseline

2-day

maximum first-response time

Have a product in mind?

Tell us what you are building. The brief lands with the engineers who would build it — you will hear back from an engineer, not a salesperson.