Faststream Technologies provides AI engineering as a complete path: feasibility on real data, sensing chain design, annotation strategy, model development, edge deployment, integration with existing controls, and the monitoring that keeps a deployed system honest. Engagements are scoped so that the expensive discovery happens in week two, not month seven.
Two to six weeks. Real parts or real signals, captured under real conditions, to establish whether the information the system needs is present at all — and what sensing change would be required if it is not. Ends with a written answer, including the answer that it will not work as posed.
Six to twelve weeks. A working system on a single cell, line or vehicle, integrated far enough to produce a defensible number rather than a demonstration.
Full development through to deployment: sensing hardware, models, edge compute, controls integration, platform and support.
Faststream engineers working inside your programme, on your tooling, where the capability gap is specific rather than whole.
Accuracy is a poor objective on its own. What matters is which error is expensive, and by how much.
| Application | Cost of false positive | Cost of false negative | Design bias |
|---|---|---|---|
| Safety-critical inspection | Good part scrapped or re-checked | Defective part reaches a customer | Toward sensitivity; accept re-check load |
| High-volume cosmetic inspection | Scrap and throughput loss at scale | Cosmetic complaint | Toward precision; protect throughput |
| Rail corridor intrusion | Unnecessary service disruption | Missed obstruction | Toward sensitivity, with classification to control nuisance alarms |
| Predictive maintenance | Unnecessary intervention on healthy plant | Unplanned failure | Toward lead time and confidence, not raw detection |
| Driver monitoring | Nuisance alerts, and the driver disables the system | Missed fatigue event | Toward precision; a disabled system detects nothing |
If the defect is not visible in the image, no model will find it, and the honest recommendation is a sensing change or a process change rather than an AI programme.
If there are eleven examples of the failure mode and no prospect of more, supervised classification is the wrong frame — anomaly detection, physics-based modelling or better instrumentation will serve better.
If the cost of a false alarm is high enough that operators will switch the system off, and the achievable precision does not clear that bar, the deployment will fail regardless of the accuracy number. That is worth establishing before the purchase order, not after.
Typically two to six weeks, depending on how much real data already exists and how difficult the capture conditions are. The output is a written assessment of whether the signal is present, what sensing changes would be needed, what accuracy is realistically achievable and what the deployment would involve.
That is the normal starting position. The engagement then covers a capture protocol, an annotation standard, augmentation and synthetic data where appropriate, and — where the failure mode is genuinely rare — reframing the problem as anomaly detection rather than classification.
Yes. Team extension is a common model, particularly where the customer has modelling capability but not the embedded, imaging or controls-integration engineering that industrial deployment requires.
Customer data and models developed under the engagement belong to the customer, with terms agreed in the contract. Faststream retains its own pre-existing tooling and frameworks.
Tell us the specification, the constraint and the deadline. Programmes that cross silicon, radio, embedded and AI are where Faststream is strongest.