We build the OEE dashboards, maintenance apps and plant-floor integrations your operation actually runs on, on top of an industrial AI platform that is already in production. You own the application. We keep everything underneath it running.
A 30–60 minute call. You leave with an indicative range, not a sales pitch.
Most of a manufacturing application has nothing to do with manufacturing. It is ingestion, storage, logins and alerting — and whoever builds it owns it forever.
Before anyone writes the OEE screen you asked for, a team builds — and then maintains — the whole stack underneath it.
The work begins at your data model and your interface, because everything below that line already exists and is under support.
Every project starts from a list — a handful of screens, a calculation, an integration. These are the ones manufacturers bring us most.
Availability, performance and quality per line and per shift, computed from machine signals rather than clipboards — with downtime reasons your operators actually use.
Condition-based monitoring on vibration, temperature and current, with Agents that flag a trend early and draft the work order before the failure.
kWh per machine, per line and per unit produced, with peak-demand alerts and the cost reports finance asks for every month.
Supervisory screens for sites without a control room: live tags, alarms and history from any browser, without another on-premise server to patch.
Branded iOS and Android apps for supervisors and technicians — built as a customisation of an existing app, so login, navigation and push alerts are reused, not rebuilt.
Agents that read your plant data, answer questions from supervisors in plain language and act through Channels like WhatsApp — configured around your process, not a generic chatbot.
Custom software rarely replaces your MES or CMMS. It connects to them — and each integration is scoped as its own work package, so you see what it costs.
Pull orders and part counts in, push machine states and OEE back out, so production and maintenance read the same numbers.
Turn a condition alert into a work order in the system your technicians already use, with the trend attached.
Energy and production costs per unit, delivered where finance closes the month instead of in another spreadsheet.
Operator stations and tablets that log stops, scrap and changeovers next to the machine that caused them.
A platform you build yourself is worth the most on the day it ships. After that, someone owns its uptime, its security patches and every dependency in it — permanently.
Here, everything above the line is yours: the data model, the screens, the brand. Everything below it is ours to run, under support, and it keeps improving underneath you without landing on your roadmap.
Where a custom application is the deliverable, the code is written as work for hire. You own the repository and have access to it throughout the build, not at the end.
Nobody can price a conversation accurately. So we tell you how wide the estimate is at each stage, and narrow it by doing work rather than by guessing harder.
A short call to capture scope and sizing, and to put an indicative range on the work.
±100%Your requirements document — or a fixed-fee, timeboxed discovery that produces one.
±50%Every requirement mapped to a work package, each one sized. A range we can defend line by line.
±50%Something you can use on the real platform before the build is committed. Then re-estimated.
±25%Agreed scope, stated exclusions, delivered in reviewable increments with monthly effort reporting.
Fixed scopeDiscovery, when you need one, is a fixed fee credited in full against the project that follows. Build phases run under a not-to-exceed ceiling you set: work stops for your approval at the ceiling rather than running past it.
It would be easy to overclaim here, so plainly: AI-assisted development compresses the build step, which is the part that used to dominate a quote.
It does not compress the senior specification in front of it, or the code review and QA after it. Those are why the result is maintainable, they are done by engineers, and they sit inside the same rate.
Typical effort split of a custom build. The cyan block is where the time goes away; the rest is the human quality floor.
Two current engagements, described by what was asked and how it is being built.
They arrived with a finished requirements document and one real question: not "can you build this", but "how much of this do we have to build at all?"
145 requirements ship through configuration and 52 through the extension layer. A working prototype was built on the real platform, not in a design tool, and became a delivery asset in its own right.
Enterprise white-label licence closed August 2026 · delivery under way
Their detectors competed well on sensing and badly on connectivity. After winning a tender, they needed a connected layer in time to deliver it.
An Android app turns the phone into the gateway: it pairs over Bluetooth, decodes the manufacturer's binary frame, tracks location and publishes to a central console with a live map and alarm panel. The client owns the source.
Proposals signed August 2026 · dedicated team, sprint demos as acceptance. The app relays detector data; it is not a safety device and does not replace the detector or its alarms.
Manufacturing software development is building applications around how a specific plant runs: OEE tracking, maintenance workflows, energy reporting, operator screens and integrations with MES, CMMS or ERP systems. Off-the-shelf tools cover the common case; custom software covers your lines, your vocabulary and your reporting.
Because the work starts at your data model and your interface, not at an empty repository. Ingestion, time-series storage, authentication, roles, alerting and reporting already exist and are in production. AI-assisted development then compresses the build step itself, while specification, code review and QA stay with senior engineers.
The infrastructure underneath your application — ingestion, storage, orchestration, security, availability and upgrades — is run and maintained by Ubidots under your platform subscription. You own the application layer. Changes to it after delivery are scoped as new work, the same way the original build was.
On engagements where a custom application is the deliverable, the code is written as work for hire: you own the repository and the source, with access throughout the build rather than at the end. The platform underneath stays Ubidots'.
Yes, where the system exposes an API, a database or a file export. An integration is scoped as its own work package, so you see what it costs separately from the screens that use it.
It depends on scope, and an honest first answer is a range rather than a number. We give that range on the first call, then narrow it at each stage: specification, work-package estimate, and a prototype you can use before the full build is committed. Discovery, when you need one, is a fixed fee credited against the project.
Days or weeks for a focused application, rather than the months or years a from-scratch build needs. A tightly specified mobile app has been quoted at a 2.5-week delivery window; a full white-label platform runs as a phased release ladder.
Yes. The application runs on the Ubidots platform, and the subscription is scoped and quoted separately from the development work. Development is not part of a subscription, and a subscription is not part of a development engagement.
The first call is where we work out whether there is a project here at all, and put a range on it that is honest about how wide it still is.
Book a scoping callBring whatever exists: a requirements doc, the list of requests your team keeps making, or a screenshot of the thing that doesn't work the way you need.