Engagements that ship.

Fixed scope, clear deliverables, your hardware.

01 / 04

Edge AI Feasibility Sprint

We take your sensor data and target hardware and answer one question: can a model do this job at the edge, at what accuracy, and at what power budget? You get a working prototype and a go/no-go report.

Typical timeline

4–6 weeks

You get

  • A working prototype model running on your target hardware
  • Measured accuracy, latency, and power draw, on your data
  • A go/no-go report: what it takes to deploy, and what it costs
Book a scoping call for Edge AI Feasibility Sprint (opens in a new tab)

02 / 04

Model Build & Deploy

Custom detection, classification, or fusion models trained on your data, optimized for your SWaP envelope, and deployed to your platform with integration support (TAK, ROS, custom APIs).

Typical timeline

8–16 weeks, depending on data readiness and integration scope

You get

  • A trained model, optimized to your SWaP envelope
  • Deployment on your platform, integrated through TAK, ROS, or your API
  • Test results against acceptance criteria agreed at kickoff
Book a scoping call for Model Build & Deploy (opens in a new tab)

03 / 04

Compression & SWaP Optimization

Already have a model? We shrink it with quantization, pruning, and semantic compression, so it runs on smaller hardware or sends less over a degraded link without losing what matters.

Typical timeline

3–8 weeks

You get

  • A compressed model that runs on your target hardware
  • Before-and-after numbers: size, latency, power, accuracy
  • For link-limited systems, a semantic compression pipeline sized to your bandwidth
  • Integration support to drop it into your existing stack
Book a scoping call for Compression & SWaP Optimization (opens in a new tab)

04 / 04

Data & Benchmarking

Independent evaluation of models, datasets, and hardware. Honest numbers on what performs, where the benchmark lies, and what to buy.

Typical timeline

2–6 weeks

You get

  • A test plan built around your mission, not a leaderboard
  • Results on your data and hardware, with the method to reproduce them
  • A dataset audit: leakage, split errors, and labeling problems that inflate scores
  • A clear recommendation: what to deploy, what to buy, what to drop
Book a scoping call for Data & Benchmarking (opens in a new tab)

Have a platform, a dataset, and a problem?

Tell us the hardware, the sensors, and the constraint. We'll come back within a week with a scoped proposal.

Book a scoping call (opens in a new tab)