NVIDIA is using Palantir Foundry and cuOpt to automate its hardware supply chain allocation decisions across global manufacturing sites.

The company measures operational delivery from wafer-out to first token. This window splits into time-to-rack (the transit from fab output to an assembled data centre system) and time-to-token (which covers power, cooling, networking, and day-one software readiness.)

Managing NVL72 and Vera Rubin component flows

Hardware scaling has magnified supply constraints. An NVIDIA Grace Blackwell NVL72 rack contains 18 compute trays, with each tray requiring two Grace CPUs, four Blackwell GPUs, and 32 HBM3e memory packages sourced across thousands of suppliers, OEMs, and contract design partners.

The upcoming supply chain constructed for NVIDIA’s Vera Rubin architecture is twice as large as the network supporting Grace Blackwell.

Assembly cannot proceed until parts arrive from three designated channels: direct inventory, consignment stock, and external suppliers. Early shipments must wait on delayed components, extending the metric NVIDIA terms ‘Time of Ownership’ (the duration from when a facility receives materials to when finished sub-assemblies depart.)

Factory allocations are reworked weekly over rolling two-quarter horizons to resolve part availability, throughput limits, and customer fulfilment schedules.

Mixed-integer linear programming via cuOpt

To coordinate these dependencies, the NVIDIA operations team built the ‘Digital Supply Chain Intelligence’ command centre using Palantir Foundry. Foundry’s Ontology models facilities, supplier commits, component stocks, and production targets as interconnected objects and links.

NVIDIA cuOpt, an open-source library for GPU-accelerated decision optimisation, reads this operational layer directly. Formulating distribution as a mixed-integer linear program designed to minimise TOO, the solver evaluates parts constraints across every tier of the bill of materials.

Beyond outputting weekly delivery schedules, cuOpt identifies active factory limits, such as regional assembly capacity caps versus raw memory availability.

Training Nemotron on qualitative operational records

Mathematical optimisation alone failed to capture unstructured operational variables observed by human planners, including supplier call transcripts, regional weather forecasts, partner email exchanges, and geopolitical events.

NVIDIA addressed this by post-training Nemotron 3.5 Lightning, an open-weight mixture-of-experts model featuring 30 billion total parameters and approximately three billion active parameters per forward pass.

The engineering pipeline processes historical records through NeMo Anonymizer to redact sensitive operational fields, NeMo Data Designer to balance training examples with synthetic capacity disruption scenarios, and NeMo AutoModel to apply low-rank adaptation (LoRA) parameters while keeping base model weights frozen. Palantir Autopilot manages data lineage, model tracking, and recommendation delivery.

Production benchmarks and future reinforcement learning

Evaluated on historical allocation records, the post-trained Nemotron 3.5 Lightning model achieved 86.7 percent decision accuracy, compared to 55.5 percent for the larger Nemotron 3 Ultra model and 17.5 percent for the un-tuned Lightning base model.

The post-trained model achieved a 58.6 percent balanced accuracy and a 57.5 percent macro-F1 score, outperforming Nemotron 3 Ultra’s 42 percent balanced accuracy and 39.5 percent macro-F1 score.

Fine-tuning completed on two NVIDIA B200 GPUs within minutes. Domain fine-tuning improved allocation decisions, though production risk forecasting further into the future remained difficult.

Operational choices, planner revisions, overrides, and observed factory outputs are continuously written back to the Palantir Ontology.

NVIDIA confirmed this dataset will form preference pairs for reinforcement learning routines – scoring recommendations on allocation precision, policy compliance, and evidence grounding – with production models remaining strictly isolated from live and unmonitored retraining.

See also: Supply chains detect fast, act slow: How AI agents fix it

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Supply chain disruption cost businesses about $184 billion in 2025, according to the J.S. Held Global Risk Report, and most of that bill still buys faster detection, not faster action.

That figure is usually treated as weather (i.e. storms happen, costs follow.) Treated as a product specification instead, it highlights an operating model that can spot a problem hours or days earlier than it used to, and still cannot move until a person has opened a ticket, convened a call, and re-entered the same data into three systems.

Visibility platforms, control towers, risk scores, digital twins, and exception dashboards have defined the last decade of AI in the supply chain. That decade has been very good at collapsing the time between an event and awareness of it, but it has been far less good at collapsing the time between awareness and a commercial act.

Detection is a ‘solved-enough’ problem

Ask a chief supply chain officer where the AI budget went and the answer tends to follow a familiar list: demand sensing, ETA prediction, supplier risk scoring, inventory optimisation, and lane analytics. These tools work. Forecast error comes down. A vessel delay is flagged before the container misses the cut-off. A second-tier fab outage shows up on a heat map instead of in a customer email.

None of that accounts for the $184 billion. The bill is the interval after the flag: expedite or wait; split the order or accept the miss; retender the lane or pay the spot rate; consolidate two half-empty movements or ship both; swap ocean for air on the SKUs that actually justify the premium. These are bounded, repeatable decisions that sit inside policy, contract, and inventory limits the company already set—and they still queue behind a human inbox.

Surveys keep describing the same lag in different language. A 2026 Knosc survey of mid-market manufacturers and distributors found that supply-chain teams spend 28 percent of their working time responding to disruptions, most of it investigating what happened rather than changing what happens next.

Logistics executives still rank AI as a strategic priority (Capgemini’s 2025 research put an AI-driven “new-gen” supply chain among the top three technology trends for 70 percent of large-company executives) and then report that measurable financial impact remains rare. Gartner found in 2025 that only 23 percent of supply-chain organisations even have a formal AI strategy. The shortfall is not a shortage of models, but a shortage of authority granted to software.

The ticket is the product

Most current deployments are built around the ticket. The model produces a recommendation, the recommendation becomes an alert, the alert becomes a work item, and the work item waits for a planner already occupied with other work items. By the time the planner acts, the option set has narrowed—the alternative carrier’s capacity is gone, the consolidation window has closed, and the supplier’s next production slot is allocated.

That workflow is not a temporary step on the way to autonomy but the product companies bought. Vendors sold insight because insight is easy to demonstrate and easy to govern; action touches money, contracts, service levels, and blame. So the industry automated the part of the job that does not require a signature. FourKites and ABI Research reported in 2025 that only 27 percent of organisations allow AI to take autonomous action, while 52 percent confine it to decision support.

Adding another dashboard to a delayed shipment rarely moves EBITDA as a result. The decision cycle has not changed; it has only been decorated.

Bounded action as the next model

The firms set to take share are not the ones with the tidiest control tower but the ones that pre-authorise a narrow class of moves and let agents execute them while the exception is still cheap.

Retender a lane when the contracted carrier’s ETA slips beyond a threshold and a qualified alternate sits inside the approved rate band. Consolidate outbound waves when fill rates and cut-off times make a combined movement cheaper than two. Swap mode on a defined SKU set when the cost of air is lower than the cost of a missed retail window. Reallocate safety stock across two distribution centres when a forecast miss and a transport constraint line up.

None of that requires a strategy offsite. Each can be written as: if these conditions, then this action, within this spend cap, with this audit trail, and a human only if the case falls outside the fence. That is not a “lights-out” supply chain—it is the same discipline manufacturers already apply to machine control, where the agent may act inside the interlock and escalates outside it. The difference here is commercial rather than physical: the interlock is a policy object – category, supplier tier, mode, dollar limit, and service class – not a PLC.

Three conditions for real change

First, decisions have to be written as policies, not tribal knowledge. If the only place “we will pay air on A-items after 48 hours of ocean slip” lives is in a planner’s head, no agent can execute it. The work of the next two years is less model training than decision design: which moves are reversible, which are capped, and which suppliers and modes are pre-cleared.

Second, execution systems have to accept machine-initiated transactions. An agent that can draft an RFQ but cannot post it is still a detection tool. TMS, WMS, sourcing suites, and carrier APIs need to treat a bounded agent the way they treat a junior buyer with a spend limit—authenticated, logged, and reversible.

Third, accountability has to move with the action. If a retender inside policy goes wrong, the post-mortem should inspect the policy, the data, and the fence, not hunt for the person who “should have checked”. Until that cultural change happens, every agent will be designed to wait, because waiting is how careers survive.

The competitive split

For a while, both models will look alike on a slide—both will have AI, and both will have a control tower. The difference will show up in cycle time from detection to commercial act, and then in service and cost.

Companies that keep buying detection will know about the storm earlier. Companies that authorise bounded action will already have retendered the lane, consolidated the wave, and moved the A-items before the incident call is booked.

Disruption is not going away. Lead times in critical components, mode volatility, and multi-tier opacity are structural features of the network. What remains optional is whether the response waits for a human to open a queue. The product that created the lag was insight without authority. The product that ends it is an agent allowed to spend a little money, inside a fence, before anyone is free to look.

See also: JD.com expands physical AI in logistics with 3 million robots

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

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CloudNC has secured $20 million in new capital to scale its AI precision machining technology across global supply chain networks.

The investment round was led by US venture investor Nimble Ventures, with participation from Calculus Venture Capital, Entrepreneur First, and LM Ventures, the venture capital fund of Lockheed Martin.

Founded in 2015, CloudNC operates from headquarters in London and an active production facility in Chelmsford. The company previously drew backing from Atomico and Episode 1 Ventures, alongside strategic partnerships with Autodesk and Lockheed Martin.

Precision component suppliers face pressures to balance tight engineering tolerances with compressed delivery schedules. CloudNC designs its lead software product, CAM Assist, to automate computer numerical control (CNC) programming—generating machining strategies and toolpaths from computer-aided manufacturing models to accelerate production runs.

Automating CNC programming for supplier networks

The software shortens the transition phase between technical part design and factory production, allowing machinists to increase physical component output.

CloudNC reports that CAM Assist is now active across more than 1,000 machine shops globally, including several hundred facilities in the US. Confirmed commercial users include Lockheed Martin and Major Tool and Machine.

Theo Saville, CEO and co-founder of CloudNC, said: “Machine shops everywhere are under pressure to quote and program faster, and deliver more with the people and machines they already have.

John Burbank, founder of Nimble Ventures, added that automated CNC workflows will support “massive increases in onshoring of manufacturing and global production” for precision industrial supply bases.

CloudNC says it will direct the capital injection into go-to-market operations, technical support infrastructure, and partner activity across international regions.

AI quoting targets procurement turnaround times

CloudNC is expanding its software line with Quote Agent, an AI-assisted estimating tool scheduled for release later in 2026.

Preparing job estimates represents a major operational drag for manufacturing suppliers. Evaluating incoming technical drawings, calculating cycle times, and establishing part pricing remains heavily manual, exposing supply shops to administrative delays or miscalculated margins once components enter physical production.

Quote Agent applies AI to early-stage costing, enabling suppliers to return customer bids rapidly while standardising cost estimations.

“Quote Agent is a natural next step for CloudNC as we seek to accelerate global machining with AI,” says Saville. “CAM Assist already helps machinists get parts onto machines faster; Quote Agent will help shops assess new work, prepare quotes more efficiently and respond to customers with greater confidence.”

“We believe our AI can make quoting faster, more consistent and more scalable, helping manufacturers win more of the right work while keeping expert judgement firmly in control,” Saville added.

Knox Systems partnership advances FedRAMP authorisation

CloudNC is collaborating with Knox Systems to achieve FedRAMP certification for CAM Assist.

The compliance roadmap aims to clear CAM Assist for deployment by US government departments, defence contractors, and aerospace manufacturers operating under federal data governance rules. 

Authorisation, if granted, will permit public-sector and defence suppliers to deploy automated toolpath generation across regulated production workloads.

See also: Samsung taps Mistral AI models for semiconductor manufacturing

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

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Samsung has partnered with Mistral AI to deploy on-premises models across its semiconductor manufacturing and engineering operations.

The agreement was announced during the bilateral state summit held in Paris between South Korea and France. Samsung will integrate Mistral’s software suite – including its flagship Mistral Large model – into internal semiconductor facilities to build customised models for intelligence-driven factory infrastructure.

On-premises AI models for semiconductor fab infrastructure

The deployment relies on private enterprise installations to process sensitive engineering and operational records within Samsung’s computing perimeter. This architecture keeps proprietary technical data contained within company infrastructure, avoiding external cloud exposure while maintaining control over operational assets.

“Increasing complexities involved in AI chip design and manufacturing requires continuous innovation in semiconductor technologies,” says Young Hyun Jun, Vice Chairman and CEO of the Device Solutions (DS) Division at Samsung Electronics.

Mistral will provide Samsung with a specialised stack of software tools to assist in how processors are designed and produced.

“AI is reshaping how we build complex technologies, from silicon to software,” says Arthur Mensch, co-founder and CEO of Mistral.

“We are proud to support Samsung Electronics with our expertise in electronics and semiconductors, helping to improve how chips are designed and manufactured, and to accelerate technical progress across the global semiconductor and AI value chain.”

Defect detection and yield stabilisation

Samsung plans to deploy the targeted models directly to automated defect detection and fab machinery tuning. As semiconductor production processes advance, rapid data analysis inside the fab becomes necessary to maintain factory throughput.

The company expects targeted AI models to accelerate development cycles, improve manufacturing precision, and stabilise production yields across advanced memory and logic chips. The operational scope covers Samsung’s memory division, logic design units, and contract foundry business.

Samsung also led Mistral AI’s Series D funding round, securing a strategic equity stake to support long-term technical cooperation.

The lead investment expands cross-industry collaboration between silicon manufacturers and AI developers across advanced memory, logic, and foundry operations.

See also: Arm launches Total Design for Physical AI and robotics framework

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

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MG Ship has introduced an AI route optimisation and carrier selection module as logistics deployments demonstrate rapid cost and time returns.

The technical module targets global retailers and commercial shippers, pairing automated routing algorithms with carrier recommendation systems across international trade corridors. The deployment arrives as enterprise supply chain operators report measurable operational returns from machine learning tools, moving capital allocations away from speculative trials toward production deployments.

Measurable returns from deploying AI for logistics

Suki Cheung, CEO of MG Ship, will present deployment metrics during a panel discussion at the upcoming WMX Asia conference. Cheung will join executives from Pos Malaysia, Omniva, and OnyX Space for the session, titled AI Beyond the Hype: Measurable Results in Logistics Today.

“Too many AI conversations in logistics remain focused on future possibilities,” said Cheung. “The reality is that AI is already delivering measurable business outcomes today. Leading organisations are reducing transportation costs, improving forecast accuracy, increasing warehouse productivity, and achieving payback within months rather than years.”

Industry operational data indicates that initial investment returns are concentrating across three primary workflows:

• Dynamic route planning has reduced enterprise fuel consumption by 15–20 percent, improved transit speeds by 15–25 percent, and lowered overall transportation costs by 12–22 percent, with capital payback reached within three to six months.
• Predictive demand forecasting has reduced projection errors by 20–40 percent, improved planning accuracy by up to 35 percent, and decreased excess inventory by 20–30 percent within six to 12 months.
• Automated freight documentation processing has cut manual task duration by up to 85 percent, recovering initial expenditure inside three to six months.

Over five-year deployment cycles, enterprise adopters have recorded average operational expense reductions between 10–25 percent, accompanied by warehouse productivity gains of 25–35 percent.

Routing algorithms and carrier scoring

MG Ship built the new routing capability directly into its visibility and supply chain intelligence platform, which serves retailers, manufacturers, and freight operators across multiple international markets. The base system synthesises live cargo telemetry with trade intelligence, risk monitoring, and predictive analytics to support operational planning and trade financing.

The route optimisation engine processes live and historical lane transit logs, weather patterns, air and ocean port congestion indicators, customs risk alerts, and transit reliability data. Shippers receive automated recommendations identifying low-cost, low-risk transit paths.

Carrier evaluation features rank transport providers per lane and service tier. Rather than selecting capacity purely on spot freight pricing, the system scores carriers against historical on-time metrics, transit consistency, exception occurrences, claims rates, available volume, and total cost-to-serve.

Logistics teams can also execute scenario simulations prior to peak shipping quarters. The software models lead times, service levels, freight spend, and risk exposures under alternative carrier allocation rules.

Early enterprise implementations demonstrate lower lead-time variance, reduced expedited freight expenditure, and improved on-time-in-full delivery rates.

Cheung stated that the platform “does not simply tell businesses where their cargo is”, adding that “it recommends the best route, the right carrier, and the lowest-risk option based on real-time conditions, helping organisations make faster and more profitable decisions.”

See also: OneRail uses Nvidia AI for real-time last-mile delivery optimisation

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

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The Cloud Native Computing Foundation (CNCF) and Kusari have announced a new collaboration aimed at strengthening software supply chain security across cloud-native projects, providing free access to Kusari's AI-powered security tooling for CNCF-hosted projects.