AI consulting · Canada & United States
Linx Insights works with energy, transportation, manufacturing and infrastructure organizations. We find the AI use cases worth funding, prove them on your own operating data, and get them into production.
What we do
Most clients start small and expand once the numbers hold up.
Industrial organizations rarely fail at AI because the modelling is hard. They fail because someone picked the wrong use case, or the data was not ready, or nobody agreed what success would look like. We spend the first few weeks on those questions.
We review your data, sit with the people who generate and use it, and map your end-to-end workflows to see where AI would add value, and where it would not.
We take a single bounded scope and build a working model against your historical data. The baseline and the success measure are agreed before any of it starts.
Production models wired into the systems your teams already use, with the monitoring and controls needed to keep them reliable over time.
Senior support for teams building their own AI capability, so the expertise stays in house after we leave.
Industries
Sectors where downtime and safety performance carry real cost.
We came out of operations rather than research: regulated generation, transit networks, asset-intensive utilities and industrial risk. Select an industry to see the work that applies there.
Regulated environments where an unplanned outage is expensive and every change has to survive scrutiny. We have worked inside plant operations, work management and asset lifecycle planning.
Networks where safety and service performance are measured in public. We designed and validated an operator risk model end to end for this sector.
Plants sitting on years of process data and maintenance history that nobody has time to read through. The techniques we use in regulated generation transfer directly.
Long-lived assets, capital plans that run over several cycles, and regulators who expect the forecast to be defensible. Our asset lifecycle work applies here with little translation.
Carriers and risk functions underwrite the same physical assets we work on from the operator's side, so the modelling translates.
Selected work
Engagements delivered by our partners. Clients are named on request.
Nuclear power generation
Assessed equipment sensor data and maintenance logs to identify AI applications for predicting equipment failures, then developed and implemented solutions for equipment health optimization.
Nuclear asset lifecycle
Assessed long-range asset investment plans against equipment reliability data and obsolescence risk to optimize multi-cycle capital allocation. This improved the forecast accuracy of end-of-life projections.
Nuclear work management
Used custom AI algorithms to optimize maintenance scheduling by analyzing unstructured data, including emails and meeting transcripts, alongside the formal plan.
Nuclear plant operations
Assessed 10+ years of outage schedules, work orders and parts data for AI-driven planning optimization. The outputs fed predictive resource allocation.
P&C insurance
Evaluated claims workflows and historical loss data to identify ML-based risk scoring opportunities, and developed the roadmap to automate the routine end of triage.
P&C insurance
Assessed the regulatory and competitor intelligence workflow for major US carriers and identified generative AI opportunities to reduce cycle time and adverse selection risk.
In depth
One example of what a full build looks like, start to finish.
We designed and validated an operator risk engine for transit. It combines dozens of quantitative and qualitative inputs, including supervisors' written comments, into a single profile that flags elevated risk early enough to act on.
Inputs
Outputs
Approach
Three phases, each with a decision gate. You can stop at any of them.
PHASE 01
One to two months reviewing and sanitizing data, meeting the people who own the workflows, and pressure-testing candidate use cases against business need.
You leave with A shortlist of value-added use cases, a roadmap and projected ROI. That is enough to make a decision without spending more.
PHASE 02
A bounded pilot on a defined scope, such as a single route, line or asset class, using historical data. The baseline and success measure are agreed in advance.
You leave with A working model, results measured against the baseline, and a straight answer on whether it's worth scaling.
PHASE 03
Deployment across the network or fleet, integrated with your existing systems, with the monitoring, documentation and training it needs to survive handover.
You leave with A production capability your own people can run, and the evidence trail to defend it internally.
Get in touch
You do not need a pitch deck. A short call is usually enough for us to say whether there is something worth pursuing, and we will tell you plainly if there is not.
Regions served
Helpful to include
Your sector, the systems your data lives in, and the decision you wish you could make with more confidence.