Solutions
Intelligence that sees, decides and acts in the physical world, including where connectivity is poor.
01Overview
Physical AI connects sensors, machines and models into systems that can perceive and respond. A camera over a conveyor belt, a vibration sensor on a pump, a drone over a field, a scanner at a port gate: each one produces data that only has value if it reaches a decision in time. We design the whole path from sensor to action.
Across much of Africa the path has to work with constraints. Power can be unstable. Mobile data is expensive or patchy. Sites are remote and spares take days to arrive. So we run perception models on devices at the edge, keep a local buffer of events and sync to the cloud when a link is available. A system that needs a constant connection is not a system for a mine at the end of a gravel road.
We also take the physical world seriously. Anything that moves a machine or opens a valve is wrapped in safety rules, limits and human override. We start with perception and recommendation, prove accuracy on real site data, and only then move toward automated action. Our engineers work on site, with your maintenance and operations staff, until the system is theirs.
02
Physical AI projects fail when the team never leaves the office. Sensors are installed badly, labels are wrong and nobody checks the model against what the operator sees. Our forward deployed engineers spend time at the asset. They watch the work, mount the hardware and label data with the people who know what normal looks like.
That is also how we find the practical limits: dust on a lens, a power cut during a shift, a pump that is serviced on Fridays. Those details decide whether a system works in month six.
03What you get
We build vision and signal models that detect defects, count, classify, read gauges and flag unusual vibration or temperature. Models are trained on images and signals from your site, not on generic datasets. Each model reports its confidence so low-quality cases go to a person.
Models trained on your site work on your site.
We package models to run on rugged edge devices with limited power and memory. Updates are signed, staged and reversible. Devices keep working during network loss and report health when they reconnect.
A model that cannot run on site is a demo.
We design store-and-forward pipelines with priorities. Alerts travel first over whatever link exists, including SMS or satellite where justified, and bulk data waits for a good connection. Conflicts are resolved by rules agreed with your team.
Remote sites are where value is often highest.
We integrate with PLCs, SCADA systems, drones, conveyors and handling equipment. We start by recommending actions to an operator, then automate specific, low-risk steps once accuracy is proven. Every automated action has a limit and a manual stop.
Safety is earned in steps.
We build the pipelines that carry events, images and summaries from the edge to storage and analytics. Data is compressed, labelled with site and asset identifiers, and linked to the operational model so engineers can ask questions across sites.
One site learned, many sites improved.
We build mobile tools for technicians that work offline, show the asset history and guide inspections. Findings return to the model and improve the next prediction. Maintenance schedules shift from fixed intervals to condition.
The technician is the best sensor you have.
Free 30 minutes with an engineer.
04African sectors
Situation
Large farms and outgrower schemes cannot walk every field. Pests, water stress and harvest timing are noticed late.
What it does
Drone and satellite imagery feed models at the edge and in the cloud. Alerts reach farm managers by SMS even where data is poor.
Human decides
The agronomist decides what to spray, irrigate or harvest and when.
05Boltcliq platform
Boltcliq product
Hosts the pipelines and agent workloads that sit behind the edge devices, with portable deployment close to your site.
Heavy processing can run in-country.
Boltcliq product
Models assets, sensors, events and work orders so readings are tied to the things operators care about.
A signal becomes a statement about a specific asset.
Boltcliq product
Observes devices, models and decisions, and shows drift, outages and overrides.
You see problems before the shift supervisor does.
06
Does
Does not
07FAQ
Experience you can lean on
Immersed in technology and across many different verticals, so the people on your project have done this before.
Physical AI
Tell us the problem. An engineer, not a salesperson, answers.
Intelligence that sees, decides and acts in the physical world, including where connectivity is poor.
