Most asset data never reaches the people who decide when to maintain, so failures come as a surprise. Relevant builds a single live view of sensor, maintenance, and cost data, so decisions run on current conditions, not last year’s records.
Asset records live in spreadsheets, vendor files, and people’s heads, so no one trusts the count. We design and build the registry that serves as the single source of truth for every asset, its history, and its place in the hierarchy.
Maintenance runs on fixed calendars or on failure, and both waste money. Our engineers build the scheduling and prediction logic that triggers work based on conditions, rather than guesswork.
Sensors stream data off your equipment, but it never reaches the people who could act on it. We engineer the pipelines that bring sensor and SCADA data into one live view of asset health.
Work orders move on paper and phone calls, and jobs fall through the cracks. Our delivery team assembles the workflow that carries every job from request to sign-off, with the field team working from their phones.
Parts run out when you need them and pile up when you don’t. Our team builds the inventory layer that ties parts to assets and reorders before a stockout halts a repair.
Downtime, cost, and reliability numbers sit in separate systems, so no one sees the whole picture. Our architects design the reporting layer that turns asset data into the performance, cost, and compliance figures that leadership signs off on.
Transformers, batteries, generators, and renewable assets degrade on cycles that matter to both uptime and cost. You get degradation tracking and early warnings per unit, so replacement is planned rather than forced.
Vehicles and mobile equipment rack up costs across fuel, parts, and downtime that no single system tracks. You get one record per asset that ties usage, maintenance, and cost together, so the true cost of each unit is visible.
HVAC, electrical, and elevator systems tend to run until something breaks, and the repair always costs more than the prevention would have. The software schedules maintenance based on condition and use, so building systems get attention before they fail.
Pipelines, grids, rail, and network assets span distances that make manual inspection slow and incomplete. Our team builds monitoring that tracks conditions across the entire asset, so problems surface by location rather than by complaint.
Pumps, turbines, compressors, and motors fail in ways their runtime hours never predict. We bring sensor and maintenance data together per unit, so wear shows up as a trend you can act on instead of a breakdown you react to.
You sign off on a clear scope: every asset, system, and data source in play, and the maintenance, cost, and uptime decisions the solution has to support.
You get a blueprint for the layer everything depends on, how asset, sensor, and maintenance data is captured, modeled, and validated, with the lineage that lets you trust every figure downstream.
One high-value asset class or site goes end-to-end first, real data in, reliable insight out. That early result is yours to check, and it proves the approach holds before the wider build commits budget.
Registry, monitoring, maintenance, and reporting land every sprint, tested and documented, so your team works from the early pieces while the rest is still in build.
Every metric ties back to your source systems and ground truth, and the edge cases get pressure-tested at full data volume. What reaches your team is a number that holds up in operations.
A live solution is handed over to your team, pipelines are healthy and documented. Our engineers keep it tuned, add the questions your team raises in use, and scale it as assets and sites grow.
Book a 30-minute call to scope a first high-value asset class: what it tracks, what it costs, and how fast it’s live.
For more than a decade, our teams have shipped systems that run on heavy streams of asset, sensor, and operational data across organizations with dozens of sites to coordinate.
92% senior-level talent, 96% employee retention. Sensor integration, maintenance logic, and analytics all sit within the same senior group, so the work never drops at handoffs between specialists. The team stays with you from the first sprint through launch and into support.
Audit trails, data lineage, and validation are part of how each pipeline is built, present from the first commit rather than bolted on later. The work holds to ISO 27001:2022 and GDPR.
Engineers are senior, based in the EU, and reachable through US and UK working hours.
99% of engagements land on time and on budget. Works every two weeks, and you see it running at each checkpoint. The backlog stays open to you, scope flexes as your priorities move, and the budget holds.
The senior-led Relevant AI Delivery Framework speeds up discovery, architecture, build, and QA simultaneously, driving up to 50% faster cycles and up to 20% fewer bugs.
Scattered data is a normal place to start, and you don’t need to fix it first. Your records probably sit in a few places at once: a spreadsheet, a vendor portal, sensor feeds nobody checks, and the knowledge of the people who run the equipment. The first thing to do is list what you own, identify where each record comes from, and assess its reliability. From there, you rank assets by the cost and risk they carry.
The cleanup happens during the build, not before it. Gaps and duplicates appear as sources come together, and sorting them out is part of the work.
You can begin with a short discovery or audit with our team. It might confirm that a custom solution is worth building, or it might point to a smaller fix that solves the problem without one. You’ll come away with a clear map of your assets and the decisions that depend on them.
The right choice depends on how closely your assets and processes match what a ready-made tool expects. A standard EAM or CMMS handles routine maintenance and work orders well, sets up quickly, and fits operations whose assets and workflows resemble those for which the tool was designed.
A custom solution makes more sense when those tools keep leaving gaps you pay for. Unusual asset types, heavy sensor and SCADA integration, complex multi-site setups, and maintenance rules that no template covers all push you that way.
You can settle this before spending on a build. A brief analysis compares what an off-the-shelf tool covers with what you need. You can book a short call with our team to walk through it, and if the answer is to buy rather than build, that’s a genuinely useful result. The goal is to spend on the option that fits, not to default to a custom build.
Recording a failure tells you what already broke. Predicting one means reading the conditions that lead up to a failure and acting while there’s still time. The solution learns those conditions from your own history: sensor readings, runtime, past repairs, and the failures behind them. When the same pattern shows up again, it flags the asset before it breaks.
How far ahead the warning comes depends on the data you have. Rich sensor data supports earlier, more confident predictions. Thinner data still supports condition-based rules that beat a fixed calendar. Getting the right data flowing first comes before the prediction, because the prediction is only as good as what feeds it.
If your sensor and maintenance data are scattered across systems or arrive in poor condition, we can audit them, handle the AI-powered data engineering, and build pipelines to bring them together cleanly, then layer data analytics on top once the foundation is solid.
Connecting to the systems you already run is the starting point, not an extra. Sensor feeds, SCADA, and control systems serve as data sources, and your ERP and maintenance systems link up so asset and cost figures move between them rather than being typed in twice.
The systems you run shape the design, so we map the integration early. None of it means replacing your stack. The aim is to pull your existing data into one place where it finally lines up.
Your asset and operational data is sensitive, and the build treats it that way from the first line of code. We hold ISO 27001:2022 certification and work within GDPR. Encryption, access controls, and reconciliation with your source systems are standard, not paid extras.
Lineage and audit trails run through every pipeline, so any figure on a dashboard traces back to the raw reading, the system it came from, and each step in between. That’s the traceability auditors and regulators look for. Before any detailed discussion of your systems, an NDA protects your data and your IP.
You own all of it. We hand the source code, integrations, models, and documentation to your team in full. We charge nothing per asset or per site as you grow, and nothing forces you to keep paying to use what you funded.
The same goes for the roadmap. You can run the solution, build on it, or move it to another team without coming back to us to keep it running. We build for that handover from the first sprint, so what you have at the end is genuinely yours.
Cost follows scope. The number of asset types and sites, the systems that need connecting, the depth of sensor and prediction work, and the reporting you need all move the figure. A solution for one asset class at a few sites costs far less than a multi-site system with predictive maintenance and ERP integration.
To get the clearest picture, you can determine the cost of the first phase. A brief analysis helps set the scope of work, and the cost of that specific first phase follows from it. You can schedule a short discovery call with our team, and we’ll discuss exactly what a realistic first phase might involve. You’ll decide whether to continue once you see what the phase delivers and what value it brings.
You can expect a usable result within the first six to eight weeks. Instead of building everything and launching once, the work takes one high-value asset class or site all the way through first, so your team has a real result in hand while the rest is still in progress.
The full timeline depends on scope, the number of integrations, and the state of your asset data at the start. You’ll get a firm estimate from the discovery phase, once the assets, systems, and rules are mapped.
IoT
AI
AIBehind every result below is our energy software development experience, applied to the systems operators run every day.
