How to Start Working with Us
Four bounded entry offers, each with defined scope, duration, and deliverables. Start with the Measurement Baseline — it is the lowest-risk way to establish what you are working with before committing to a larger engagement.
Measurement Baseline
Full characterization of an existing vision system on its target hardware. This is the default first engagement — the lowest-risk way to know what you are actually working with before committing to optimization or redesign.
- Latency distribution: p50, p95, p99, max
- Power draw from sustained-window sampling
- Thermal throttling behavior under load
- Claim-to-file manifest (numbers linked to raw logs)
- Verifier script for independent reproduction
- Honest limitations section
- Access to your target hardware (physical or remote)
- The model or pipeline to be measured
- Your latency and power budget targets
Feasibility Assessment
Go/no-go on "can this run on this hardware, at this accuracy?" with measured trade-off curves — not estimates. Use this before you commit capital to a hardware choice, a training run, or an architecture.
- Accuracy-vs-latency trade-off curves, measured on device
- Architecture options ranked against your constraint
- Power and thermal envelope assessment
- Risk and gap identification
- Recommended path with evidence rationale
- Go/no-go recommendation with supporting data
- Your target hardware (or agreed proxy)
- Accuracy and latency requirements
- Candidate model(s) or architecture brief
Deployment Engineering
Make the system meet its production budget — TensorRT, ONNX, JetPack, pipeline engineering, and validation under sustained load and real thermal conditions. Delivered when a working prototype exists but has not yet met its production requirements.
- TensorRT or ONNX engine with verified export
- Inference pipeline — profiled and optimized
- Thermal and sustained-load validation report
- Before/after measurement comparison
- Integration documentation and deployment guide
- Verification and acceptance test protocol
- A working prototype or trained model
- Access to target production hardware
- Clear performance targets (latency, power, accuracy floor)
Model Development Under Constraint
Build or adapt a vision model to fit a hardware budget — measured first, designed second. Architecture, quantization, and pruning decisions are made against real numbers from your target device, not from paper estimates.
- Trained or adapted model meeting measured constraint
- Quantization and pruning strategy documentation
- Full benchmarking report on target hardware
- Accuracy-latency-power trade-off analysis
- Model artifacts, training code, and export scripts
- Deployment-ready engine for your target runtime
- Target hardware platform confirmed
- Dataset or access to data collection pipeline
- Accuracy, latency, and power requirements