Cloud, data and AI delivery for manufacturing
The line produces data all day and the business sees it a shift later. We connect the floor to the systems that plan, buy and ship, and we keep the connection running.
- AWS Premier Tier Services Partner, the top tier of the AWS network
- Generative AI competency The only GenAI and MSP partner in Canada
- Agentic AI Specialization Selected Canadian partner
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SOC 2 Type 2 + ISO 27001
Independently audited
What is changing in manufacturing
- 80% of manufacturers plan to put a fifth or more of improvement budgets into smart manufacturing Deloitte, November 2025
- 22% plan to use physical AI within two years, against 9% today Manufacturing Leadership Council, November 2025
- 30% of AI-using EU manufacturers apply it to marketing and sales, more than to production Eurostat, December 2025
Who we work with in manufacturing
The manufacturers we build for, and what each of them needs from a data platform.
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Discrete manufacturing
Mixed lines, frequent changeovers and traceability down to the part.
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Process manufacturing
Continuous runs where a sensor drift shows up as scrap hours later.
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Industrial equipment
Machines in the field that have to report back without a technician.
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Automotive suppliers
Tier demands on quality evidence and delivery windows measured in hours.
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Electronics and high-tech
Short product lives, tight tolerances and yield that has to be explained.
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Contract manufacturers
Many customers, many systems, one plant floor to satisfy them all.
From the floor to the customer in five stages
One chain from a machine signal to a shipped order. Each stage names the services that serve it, so the page routes into the work rather than describing it.
What happens here
Machine and sensor data leaves the line with an identity and a timestamp, buffered locally so a network gap costs nothing.
What you get
An edge pipeline in your plant, with the buffer and the certificates.
The failure it prevents
A line that goes dark whenever the link drops.
Services behind this stage IoT platforms Security
What happens here
Defects are caught at the station by inspection models your quality team can retrain, not at the customer.
What you get
An inspection model and the loop that keeps it current.
The failure it prevents
Finding the batch problem in a complaint rather than on the line.
Services behind this stage Computer vision Generative AI
What happens here
Wear signals become scheduled work, planned around production rather than around failure.
What you get
A prediction service, and the maintenance calendar it feeds.
The failure it prevents
Unplanned downtime priced in shifts rather than minutes.
Services behind this stage IoT platforms Data architecture
What happens here
Orders, inventory and supplier commitments reconcile into one plan that survives a late delivery.
What you get
One planning model, with the integrations that keep it honest.
The failure it prevents
A schedule that assumes every part arrives on time.
Services behind this stage Data architecture Cloud data migration
What happens here
Utilisation, scrap and cost per unit land in one governed model, queryable by plant and by line.
What you get
A governed model and the dashboards your plant managers asked for.
The failure it prevents
Six plants reporting six different truths.
Services behind this stage Data architecture Engineering
Our Core Focus Area
Smart Transportation
Smart Transportation
Vehicles, infrastructure and control centres coordinated in real time, so a disruption is rerouted rather than absorbed. Telemetry from fleets and roadside systems lands in one stream, and the routing logic runs against it continuously. Control room staff see the same picture the vehicles report, which makes a reroute a decision rather than a guess.
Benefits
- Traffic and conditions in real time
- Vehicles and centres in one view
- Routing that reacts to disruption
AI-Powered System Reliability
AI-Powered System Reliability
Monitoring, anomaly detection and failure prediction, so systems move from reactive repair to planned work. Models train on your own event history rather than a vendor reference set, and every alert carries the signal that raised it. Maintenance is scheduled around production instead of around a breakdown.
Benefits
- Failures predicted, not discovered
- Root cause found automatically
- Less unplanned downtime
Intelligent Product Engineering
Intelligent Product Engineering
Modular, AI-ready architecture for R&D teams, from prototype to a product that scales. Components are reused across programmes and the infrastructure ships as code, so a second product does not start from an empty repository. Compliance and observability are part of the build rather than a later project.
Benefits
- Prototypes from reusable components
- GenAI features from day one
- Compliance and observability built in
Smart Factory Automation
Smart Factory Automation
Software connected to your machines, controllers and back office, from inspection to robotic process control. We read from the line first and write to it only where an owner signs for the change, which keeps the plant safe while the data starts moving. Cycle times and defect rates become numbers the plant can act on the same day.
Benefits
- Automated workflows across assembly and QA
- Visual inspection that catches defects
- Shorter cycle times
Predictive Maintenance
Predictive Maintenance
Machine data and pattern recognition catch wear early, so maintenance is scheduled rather than urgent. Sensor streams and maintenance records are joined per asset, and the model is measured against what your engineers already know. The result is fewer emergency repairs and parts ordered before the line stops.
Benefits
- Anomalies detected from sensors and logs
- Models trained on your asset history
- Fewer emergency repairs
How we start
Three scoped entry points, each ending in something your team keeps.
Industrial data assessment
Two weeks, fixed scope
- Machines, protocols and historians mapped
- What is reachable today and what needs a gateway
- A costed plan your team keeps whether or not we build it
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Most popular
Predictive maintenance pilot
Six weeks
- One asset class, your own telemetry
- Predictions measured against your maintenance log
- A production path if it lands
- Nothing to maintain if it does not
Managed industrial platform
Monthly
- Edge and cloud pipeline run and monitored
- Models retrained on your data as the line changes
- Named engineers and agreed response times
Questions we get
Will this work with machines that predate the internet?
Usually yes, through a gateway that speaks the protocol the machine already speaks and handles identity on its behalf. The assessment says which machines are reachable as they are, which need a gateway and which are not worth connecting.
Do we have to move the historian to the cloud?
No. Plenty of plants keep the historian where it is and replicate what the business needs. Moving it is a decision about cost and access, not a prerequisite for anything here.
How much telemetry do you need before a prediction is useful?
Enough history to have seen the failure you want to predict, which usually means months rather than weeks. Where that history does not exist, we start by collecting it properly and say so rather than shipping a model with nothing behind it.
What happens on the line if the cloud link drops?
Nothing that matters. Latency-critical logic runs at the edge and data is buffered locally, so a link outage is a gap in reporting rather than a stop in production.
Who owns the models and the code?
You do. Everything is built in your accounts, in your repositories, under your licences, and there is no runtime of ours in the middle.
Cloud, data and AI for manufacturing
Plant data is usually trapped in the plant. Machine controllers, historians and quality systems each hold part of the picture, and none of them was built to share it with a central team. The work is moving that data out safely, without touching what keeps the line running.
Dedicatted builds edge to cloud pipelines, plant data models and the AI that runs on them. We have delivered this work since 2016 as an AWS Premier Tier Services Partner with the Generative AI and Machine Learning competencies, under SOC 2 Type 2 and ISO 27001.
What we build for manufacturers
- Edge to cloud pipelines from controllers, historians and sensors, with buffering for sites that lose connectivity.
- One plant model across sites, so output, downtime and scrap are counted the same way in every report.
- Predictive maintenance and visual quality inspection trained on your own asset history rather than a vendor demonstration.
How the work starts
A two week assessment covers one line or one site: what data exists, what it costs to move, and which failure is worth predicting first. You get a costed plan and a ranked list, not a platform proposal. The first build is normally one use case on one line, measured against the current baseline.
- Operational technology boundaries respected: read paths first, write paths only where an owner signs for them.
- Dashboards for plant managers and an API for the systems that need the same numbers.
- Platform and DevOps work included, so a model in production is monitored like the rest of the estate.
Where a site runs equipment that cannot be modified, we collect around it. That constraint is normal and it does not stop the rest of the programme.
Get started with a manufacturing technology consultant
Outline your plant data, OT integration or automation challenge. Our team responds within one business day with relevant experience and initial technical insights.
Thanks, we have it.
Our team replies within one business day, with relevant experience and a first read on your problem.