CloudFactory works with fleet operators and with the companies building technology for them. We supply the training data, the model evaluation, and the AI oversight layer that moves a promising model into daily operations.
Trusted partner to over 700 of the world's most ambitious AI companies, since 2010.
Operators and the technology vendors serving them approach this market from opposite directions and converge on the same constraint. Production AI demands data worth training on, evaluation that survives scrutiny, and an oversight layer that contains failure before it reaches an asset, a customer, or a regulator.
Carriers, 3PLs, distribution networks, rental and leasing operators, heavy plant and contractor fleets, service fleets. You own assets, drivers, and service levels, and AI has to pay for itself in those terms.
Autonomy and ADAS programs, telematics and fleet platforms, inspection hardware, warehouse robotics, logistics software. Your buyers are the operators above, and your model has to hold up on their worst day.
Drawn from conversations with fleet, maintenance, and operations leaders. If two or three of these are yours, the intake takes 15 minutes and we will build the conversation around them.
Human visual checks miss subtle damage and hold up turnarounds.
Repair cost, downtime, and residual value loss accumulate without a clear owner.
Underbody, chassis, and corrosion issues stay hidden on trucks and heavy plant.
Mixed fleets of vehicles and equipment across many sites defeat a single standard.
Incomplete condition evidence drives disputes, settlements, and premiums.
Inspection results sit outside maintenance scheduling and parts ordering.
Without trend analysis, nobody traces events back to a driver, a site, or a process.
Driver churn at large truckload carriers routinely runs near 90 percent a year, by American Trucking Associations figures.
Unclear ROI, uneven data capture, and compliance exposure keep good pilots parked.
Each one runs on the same foundation: data your model can learn from, evaluation you can defend, and an oversight layer on the cases that carry consequence.
Exterior, underbody, and structural damage classified from images and video, with severity tagging and a consistent condition record before and after every deployment.
LiDAR, radar, and camera annotation, lane and traffic sign labeling, pedestrian and cyclist tracking, and trajectory work for perception and behavior models.
Label and barcode detection, package condition classification, autonomous forklift perception, and drone navigation data for the first and last 100 meters.
Clear high confidence paperwork automatically and route the rest to expert review. Damage disputes get a consistent, evidenced answer instead of a support queue.
Early wear and failure patterns surfaced before downtime, diagnostics logs summarized into action, and disruption responses recommended with human validation on high impact calls.
The operational factors behind driver turnover, surfaced early enough to act on, and correlated with the behavior and site data you already collect.
Safety calls, claims approvals, maintenance sign offs, and customer communications cannot run on a probably right answer. Most stalled pilots we see did not fail on accuracy. They failed because nobody could show how a wrong answer gets caught. Our oversight layer combines automated agents with expert evaluators to catch, contain, and remediate AI failure in production.
A platform that manages the risk, and expert evaluators on the edge cases no platform settles alone.
Agents score every output against your acceptance criteria and surface drift, uncertainty, and known failure modes as they occur, not at month end.
Flagged cases are held before they reach an asset, a customer, or a regulator, and routed to the evaluator qualified to settle them.
Every correction returns as training signal, so the same failure does not recur and accuracy improves where it costs you most.
Show an auditor, a regulator, or your board which decisions were reviewed, by what, by whom, and on what evidence.
Most of this market is assembled from point solutions, and the seams between them are where programs stall. CloudFactory is built to carry a transportation AI program end to end: the training data underneath it, the evaluation that qualifies it, and the oversight layer that governs it in production.
Annotation vendors stop at the dataset. Platform vendors start after the model works. We own the whole arc, and we are accountable for the outcome at the end of it.
Software manages risk at volume. Domain evaluators settle the edge cases software cannot. Buying those separately is how oversight ends up with seams in it.
Trusted partner to over 700 of the world's most ambitious AI companies since 2010, across autonomy, robotics, and industrial inspection programs already in production.
Identify where your organization sits on the AI adoption curve. Whichever of these resonates, we work with clients at all four stages, and we can help you build trusted AI from any starting point.
We size the opportunity against your data and tell you honestly which ideas are worth a pilot.
We build a dataset and a baseline on your own material, so the answer comes from evidence.
We close the accuracy gap on edge cases and put numbers against cost, risk, and throughput.
We grade live runs, catch drift and failure modes, and keep agents and evaluators on the consequential cases.
We would like to learn more about your business, and share how CloudFactory can help your organization enable trusted AI at scale.
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