Artificial Intelligence of Things
A sensor tells you the temperature. A system that has been watching for two years tells you the compressor is about to fail.
Acaiberry builds connected systems that notice something, work out what it means and do something about it, in that order and without waiting for a person. Devices, gateways, edge models, cloud platforms and the automation on top: one team, one architecture, one number to call when a machine goes quiet.
Connected is not the same as intelligent
Plenty of organisations already have devices reporting in. The readings arrive, land in a dashboard, and are looked at when something has already gone wrong. The value is not in the connection. It is in what happens between the reading and the response, which is where AI earns its place and where most deployments stop short.
The reading is not the answer
One temperature means nothing. The same reading against two years of history, ambient conditions and the machine's duty cycle is a warning. Context is what we build.
Decisions belong close to the machine
If a response has to travel to a cloud region and back, it is too slow for a production line and useless when the link is down. We put the model on the device.
Acting is the point
A prediction that lands in an inbox is a report. A prediction that raises a work order, throttles a pump or reroutes a truck is an operating system for your assets.
Four environments, one architecture
The stack is the same. What changes is the physics, the regulation and the tolerance for being wrong.
Industrial
Plants, lines and fleets where an hour of downtime has a price everyone already knows.
- Predictive maintenance
- Equipment monitoring
- Energy optimisation
- Smart manufacturing
- Asset tracking
- Remote diagnostics
Smart infrastructure
Buildings, networks and public systems where the operator is accountable to residents, not customers.
- Smart buildings
- Smart city systems
- Utility networks
- Environmental monitoring
- Water supply & leak detection
- Traffic monitoring
Agriculture & environment
Sites spread across valleys and hillsides, on unreliable power and patchy mobile coverage. The conditions we build for by default.
- Precision agriculture
- Irrigation optimisation
- Weather intelligence
- Crop monitoring
- Livestock monitoring
- Environmental sensing
Healthcare
Clinical settings where a missed alert is a patient safety event and the data is regulated from the moment it is captured.
- Remote patient monitoring
- Wearables integration
- Medical device connectivity
- Hospital asset tracking
- Cold chain monitoring
- Facility infrastructure
We design the whole thing, not a piece of it
Most AIoT projects fail at a seam: the hardware vendor and the software team hand each other a problem. We take responsibility for the full ecosystem, which means the seams are ours to solve.
The full ecosystem, owned end to end
Connected devices
Device selection, firmware, provisioning, over-the-air updates and fleet management for units you will never physically visit again.
Smart sensors
The right instrument for the measurement, specified against accuracy, sampling rate, power draw and what site conditions will do to it.
Embedded intelligence
Logic on the microcontroller itself, so a device can filter, buffer and decide without a gateway or a network.
Edge AI
Models compressed to run on constrained hardware, delivering inference in milliseconds with no round trip to a data centre.
Gateway architecture
The layer that translates industrial protocols, buffers through outages and enforces security between the plant floor and the internet.
Cloud integration
Ingestion, device registry, time-series storage and the connection into the ERP, CMMS or SCADA systems your operation already runs on.
The network will go down. The system should not.
Deployments across South Asia taught us to treat connectivity as a variable rather than an assumption. Every system we build has a defined behaviour for the hours when the link is gone, and for most of our clients that behaviour is: carry on working.
On-device AI
Models quantised and pruned to fit microcontrollers and single-board computers, running inference within the device's power and memory budget.
Offline intelligence
Full local operation with buffered telemetry, then reconciliation when the connection returns. No lost readings, no duplicated records.
Real-time inference
Detection and response inside the window where it still matters, measured in milliseconds for control loops and vision inspection.
Low-latency systems
Architecture that keeps the decision path short, with no dependency on a cloud region for anything time-critical or safety-related.
Distributed intelligence
Work divided deliberately between device, gateway and cloud, so each layer does what it is best placed to do and bandwidth is spent on what matters.
Data kept where it belongs
Processing locally means sensitive footage and patient readings need never leave the site, which settles most privacy questions before they are raised.
Four steps, running continuously
Sense, analyse, predict, act, then sense the result of acting. Most projects build the first step well and the last one not at all. Choose a step to see what we do there.
Step 01
Sense
Choosing what to measure, with what, and how often. We specify and integrate sensors for vibration, temperature, current, flow, air quality, moisture, location and vision, then handle the unglamorous part: power budgets, enclosures, calibration drift and the fact that half the equipment on site predates the internet. Where machines cannot be replaced, we read them through retrofit sensors and protocol converters.
- Sensor selection
- Retrofit instrumentation
- Industrial protocols
- Power & battery design
- Device provisioning
- Calibration
What the device data becomes
A fleet of sensors produces an enormous volume of readings that nobody will ever look at. The platform is what turns that into a small number of things worth someone's attention. It sits on the same foundation as our data engineering work.
Streaming analytics
Continuous processing of device telemetry, with rules and models evaluated on the stream rather than on yesterday's extract.
Device management
Registry, health, firmware versions, certificates and remote configuration across a fleet that grows faster than your team does.
AI prediction services
Trained models served at the edge and in the cloud, versioned and monitored for drift as equipment and conditions change.
Alert systems
Tuned thresholds, severity levels and escalation paths, designed so operators keep trusting the alerts instead of muting them.
Dashboards
Live asset views for the control room and rolled-up performance for management, both reading from the same definitions.
Automation
Rules and model outputs wired into the systems that act: maintenance scheduling, control setpoints, dispatch and procurement triggers.
Your sector. Not listed?
Bring the problem. We start with a readiness assessment before promising anything.
Tell us moreHow we deliver
Eight stages, and a pilot before any of it scales
Hardware makes mistakes expensive. We prove the loop on a handful of assets first, then roll out with the enclosure, the mounting and the connectivity already sorted.
Device strategy
What to measure and why, which assets justify instrumentation, and what a useful answer would be worth against the cost of getting it.
MEASUREMENT PLAN • ASSET PRIORITY
WHAT CHANGES
You find out before it breaks
Common questions
Before you get in touch
Start with one site and one question.
Show us the asset that causes the most trouble. We will tell you what would need to be measured to see it coming, what the instrumentation would cost, and whether a pilot on a handful of units could prove it within a quarter.
Book a site assessment