From a reading
to a closed job.
A chart is where the question starts. BeconixAI carries a signal all the way through — from the sensor that produced it to the work order that closes it, and the baseline that proves it worked.
AI diagnostics, energy performance and maintenance management — in one platform.
Three things normally bought separately. One here, running on the systems already installed. The AI validates the data and works out what is failing and why. The energy layer measures what it is costing and verifies what a fix actually returned. Maintenance turns that into a work order and closes it with the evidence attached.
Connect
Meet every site where it is
Already have a BMS, RTU or monitoring system? It gets integrated whatever the brand, read-only where required — over BACnet, Modbus or a vendor driver, which covers most of what is installed. Where a system is genuinely closed, we meter around it instead. Gaps get instrumented with sensors and meters. Either way you end up with one normalised data layer instead of a rack of dialects.
- Vendor-agnostic integration on an open Niagara framework
- Wireless sensors, meters and controllers where nothing exists yet
- Edge gateways that buffer locally and forward when the link returns
- Fire panels joined listen-only, without touching the certified system
Monitor
The estate on one screen
Portfolio, site, building, asset, point. Every level is a live page rather than a static rollup, and each one drills into the level beneath it.
- Live estate view ranked worst-first, not an alphabetical list
- Energy, comfort, asset health and open work in one picture
- Interactive graphics, floor plans and digital twins
- The full history behind any number on the screen
Detect
Alerts that arrive already diagnosed
A threshold being crossed is the start, not the answer. The reading is validated first, the alarm is confirmed as genuine rather than a spike, and AI fault detection and diagnosis names the likely fault — so what lands in front of an engineer is a diagnosis with its evidence, not a line in a list.
- Configurable rules per asset, with delay and severity that suit the site
- Sensor anomaly detection for drift, stale readings, out-of-range and flapping
- AI fault detection and diagnosis that names the likely fault and traces it through the equipment chain
- Confirm, partial or reject on every diagnosis, so the verdicts are corrected by the people who know
- Predictive alerts from recurring patterns and weather ahead of the event
- Real-time delivery, with the suspect points and their history attached
Analyze
A verdict you can argue with
Scoring only helps if you can see how it was reached. Every analyzer shows its formula, its gates and the data quality behind the call — so an engineer can agree or push back.
- Asset performance scored on real behaviour, analyzer by analyzer
- Comfort measured as unmet hours per space, not a satisfaction survey
- Peer, baseline and trend signals shown next to every verdict
- AI investigation of a performance result, and fleet-wide search for the same root cause
Act
One queue, SLAs attached
Here an alert becomes a work order natively — same system, same asset record, nothing re-keyed and nothing lost between two products.
- Work orders carrying the asset, the alarm and the evidence that raised them
- Preventive maintenance driven by real runtime rather than the calendar
- Technicians, vendors and requesters working in their own portals
- Compliance and calibration tracked to closure, not just logged
Prove
Evidence, not estimates
Anyone can claim an improvement. The question is whether it survives a hot summer, a change of occupancy and a finance review. What counts as proof depends on what changed, so the platform carries both kinds and shows the workings for each.
- Energy verified IPMVP-style against a weather-normalised baseline, with the model and its fit visible
- Operational fixes verified by recurrence, time between failures and SLA performance
- Compliance and calibration evidenced per schedule, rather than asserted at year end
- Dashboards and reports built on the live data layer, not exports
- Board-ready energy and ESG reporting
- Full history retained, exportable, and traceable to the point
It doesn’t stop at the alarm.
Raising an alarm is the easy part. Here it is the halfway point. Each agent does one job and hands on what it found — validate, verify, diagnose, act — so by the time work is raised you can see exactly why, and who decided what.
Intelligence on bad data is not intelligence — it is a confident guess. So the raw signal is cleaned first: drift, stale values, out-of-range readings and flapping are identified before anything is allowed to become an alarm.
Ask anything
The assistant reads the same live data the dashboards do. Ask in plain language and it answers with the chart, the table or the report — not a paragraph of guesswork. Every answer keeps the trail back to the points behind it.
- Which assets are costing me the most this quarter?
- Show unmet comfort hours by floor for Tower One
- What changed on CH-04 before the alarm?
- Which vendors are breaching SLA this month?
Illustrative exchange. Responses are generated from your connected data.
The whole platform, in a pocket.
Not a cut-down companion that only shows alarms. The same estate, the same assets and the same live points as the desktop — because the person who needs an answer is usually standing in front of the plant, not sitting at a desk.
- Portfolio, site, asset and point — every level, live
- Asset health with the readings behind it, not just a score
- Alarms as they happen, with the evidence attached
- Scan an asset to open its record on the spot
MRLive points
Autonomous should mean fewer surprises, not more.
Occupancy schedules, group setpoints and rule-driven actions written back to the plant — on points explicitly marked writable, by users who explicitly hold the permission, with overrides that expire and every change attributed.
Knowing the bill went up is not energy management.
Which system, on which floor, at which hour, and what is it costing against what it should. That needs metering that reconciles, attribution down to the asset, and the tariff that actually applies — not a single blended rate across a portfolio.
A full CMMS, not a ticket list
An alert is only half the job. This is a complete maintenance system in its own right — the one your team would otherwise buy separately — already holding the asset the alarm came from.
01Trigger
A job starts from one of four places — an alarm the agents confirmed, a maintenance schedule falling due, a request raised by an occupier, or a defect found on an inspection. Not every alarm earns a job, so the queue does not fill with noise.
Measurement and verification, done to the standard
Claiming a saving is easy. Proving one against a building that changed occupancy, weather and operating hours over the same period is the hard part — and it is the only version a finance team will accept. This is a full IPMVP module, not a chart with a line on it.
Illustrative. The fitted model, its variables and its goodness of fit are all inspectable in the platform.
You shouldn't be investing in two platforms
Buy monitoring and maintenance separately and that is two licences, two asset registries, two sets of users to administer, an integration project to make them agree with each other — and someone maintaining that integration for as long as you own both. BeconixAI is one product that does both.
Two platforms
- ·An IoT platform for monitoring
- ·A separate CMMS for maintenance
- ·Two asset registries that drift apart
- ·Middleware and an integration project to join them
- ·Two licences, two vendors, two support contracts
- ·Findings re-keyed from one system into the other
One platform
- ✓Monitoring, analytics and maintenance in one product
- ✓An alert becomes a work order natively
- ✓One asset registry behind every module
- ✓Nothing to integrate between them
- ✓One licence, one vendor, one support line
- ✓One permission model and one audit trail
One licence, not two
You are not paying twice for the same asset list, the same users and the same estate. The commercial model follows the product: one platform, one subscription, one renewal to argue about.
No integration project
The costly part of the usual approach is rarely the licences — it is the middleware, the mapping work and the person who maintains it forever. There is nothing to join here, so none of that exists.
Configured, not custom-built
Work order types, statuses and workflows, auto-raise and auto-planning rules, checklists, failure-code trees, SLA policies, custom fields, notification rules and dashboards are all configuration. Your operation shapes the platform without a code change.
Dashboards built by you
Thirteen widget types over your own points and work-order data, arranged on a drag-and-drop grid, kept private or shared with the organisation. Nobody has to raise a ticket to get a chart.
Branded per tenant
Client, portfolio and site scoping with per-tenant identity, so a managing agent can run several brands from one deployment without them ever seeing each other.
Adds sites, not systems
Taking on a new building means connecting it — not procuring another monitoring product and wiring it into what you already run. Growth stays a configuration exercise.
Scoped, permissioned and deployed the way you need it.
See it on your own estate
A pilot baselines your sites before anyone quotes a number. Connect what is already there, instrument the gaps, and judge the platform on your data rather than a demo.