We make vision models run on real hardware.
Edge AI and computer vision for devices with power budgets, thermal limits, and deadlines. Every claim measured on your target hardware, not estimated from spec sheets.
Pesgit Engineering makes vision models run on real embedded hardware. We provide feasibility studies, inference benchmarking, model development under constraint, and deployment engineering — working with clients in the US, UK, and Europe. Every performance claim is measured on your target device, not estimated from spec sheets.
The Gap Between Paper and Hardware
Most vision projects are sized on estimates. Operation counts, FLOPs, and spec-sheet TOPS are computed on paper. Real hardware routinely disagrees with them, and the disagreement is systematic rather than random. That gap is why models that fly in a notebook miss their deadline in the field. We close it by measuring on the actual device, first.
Four Capabilities. One Standard of Evidence.
From feasibility to production, every engagement is measured on your actual hardware, not estimated from paper.
Feasibility & Applied R&D
Can this run on this hardware, at this accuracy? We answer with evidence before you commit capital, not with a slide deck or a spec-sheet estimate.
Measurement & Benchmarking
Worst-case latency, power, and thermal behavior on your target device. p99, not averages. Measured on the actual hardware, not estimated from operation counts or TOPS figures.
Model Development Under Constraint
Build or adapt a model to fit a budget we measured first, instead of training big and hoping it fits. Architecture, quantization, and pruning decisions made against real numbers.
Deployment Engineering
Export, engines, pipelines, thermal reality. Getting a vision system from notebook to production means surviving sustained load, thermal throttling, and driver constraints on real hardware.
Measured Results, Not Promises
Case studies follow the same structure: the question asked, the number found, the decision it changed.
Before Scaling from 10 to 100 Units
A team preparing to scale an edge vision deployment from 10 units to over 100 needed to know whether it would hold. Existing hardware, existing pipeline, production conditions. We assessed deployment readiness across inference performance, failure modes under sustained operation, and the operational assumptions the scale-up depended on.
Where the Time Actually Went
A computer vision model on embedded hardware was slower than the team expected. Embedded target, existing PyTorch/C++ inference pipeline. We audited the inference pipeline, profiling it stage by stage on the target hardware.
Architecture Before Commitment
What architecture should a new AI product commit to, before engineering effort was spent? New product, no existing system, decisions with long-term cost consequences. Full architecture review against deployment realities and production requirements.
Where Deployment Projects Get Stuck
The problems we hear most from teams trying to close the gap between notebook and production.
- Models that meet their accuracy target in the notebook but miss their latency budget on the actual device.
- Spec-sheet TOPS numbers that don't translate: real hardware routinely disagrees with paper estimates, and the disagreement is systematic rather than random.
- Thermal throttling that degrades inference performance under sustained load in production conditions.
- No measurement baseline: no way to tell whether an optimization is improving performance or hitting a ceiling.
- Cloud-dependent architectures that cannot operate when connectivity is lost, limited, or too expensive.
“We measure success by outcomes: whether a system works under real conditions, not just in the demo.”
Our methods come from active research in efficient vision model deployment on embedded hardware.
Measured, Not Estimated
Most claims about edge AI performance are computed on paper. Ours are taken on the device.
Typical Approach
- ✕Performance claims estimated from FLOPs or spec-sheet TOPS
- ✕Benchmarks run in controlled lab conditions, not on the target device
- ✕Latency averages reported; p99 and worst-case omitted
- ✕Thermal throttling behavior not characterized or disclosed
The Pesgit Standard
- ✓Inference measured on your target device with locked clocks and thermal guard
- ✓Full distribution reported: p50, p95, p99, max. Not just the mean.
- ✓Thermal derating characterized under sustained load, not just peak
- ✓Honest about limits: what remains uncertain is documented, not omitted
- ✓We take no margin on hardware. The recommendation is the product, and if the cheaper option wins that is what the report says.
Talk to us when you need to know whether it will actually run.
Before you commit to the hardware, the timeline, or the architecture, we can tell you what the numbers are. A 30-minute technical call costs nothing. We work with clients in the United States, the United Kingdom, and across Europe; where a UK contracting entity is required, engagements run through Femtus Solutions Limited (England and Wales, Co. 15629036). A measurement baseline gives you the evidence to decide.
We also deliver data and reporting foundations, management reporting, and project controls for selected clients, in partnership with our UK affiliate Femtus Solutions.