Manufacturing

AI that helps manufacturers make better decisions.

Manufacturers generate data across production, equipment, quality, inventory, suppliers, and the supply chain. We bring that data together and apply AI to help teams determine when equipment needs attention, what and how much to produce, where supply risks are developing, and how to identify and address quality issues.

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The Challenge With Implementing AI in Manufacturing

Putting manufacturing data to use across an operation can be difficult when it's spread across different systems, plants, and business units.

27%

Manufacturers that are actively using AI-powered predictive maintenance today, although two-thirds report plans to adopt it in the future. Many manufacturers are still working to move predictive maintenance from planned initiative to production use.

That makes it harder to use consistently for planning and operational decisions. That data provides the foundation for AI, which then has to fit into existing manufacturing processes. From there, AI can support maintenance planning, demand forecasting, quality control, and supply chain management.

For organizations operating across multiple plants or business units, governance also has to extend beyond the first deployment. Establishing that framework early makes it easier to apply what was developed for one use case elsewhere in the organization.

Predictive models only create value if their outputs reach teams in a format they can act on. That requires integrating AI into plant workflows, not simply deploying another system.

COMMON MANUFACTURING APPLICATIONS

What we've shipped in manufacturing

Most AI initiatives fail after the strategy deck is delivered. PivotX stays through execution, adoption, and measurable business outcomes because that's where transformation actually happens.

Yours would look different.

Data Governance as a Reusable Product Capability

Governance designed across air-gapped and cloud deployments in 10 weeks, with a framework that can be used across business units and future deployments.

Predictive Maintenance Tied to Scheduling

Connect predictive maintenance data and alerts to maintenance planning and scheduling.

Supply Chain Visibility Across Tiers Two and Three

Bring supplier data and risk signals from deeper in the supply chain into planning and operational decisions.

Quality Inspection with Human-in-the-Loop

Automated defect detection can flag exceptions for human review, with people retaining responsibility for decisions that require human judgment.

Demand Forecasting with External Signals

Combine operational data with external signals to improve demand forecasts used for production, staffing, and inventory planning.

Vendor Onboarding and Procurement Automation

Automate supplier intake with ESG screening, duplicate detection, and an audit trail.

HOW WE WORK

AI helps plant managers, maintenance teams, and planners make better-informed decisions.

We start by understanding how your teams work, both on the floor and during planning, as well as what data they rely on. From there, we identify where data and AI can improve production, maintenance, quality, supply chain, or other parts of the operation. The governance framework is designed as a reusable capability, so the work done for the first use case can support additional deployments across the organization.

See our full method

Week 1–4

Explore & Evaluate

We map the business and identify where AI could create measurable value. Working with your team, we evaluate potential use cases and narrow the list to the ones worth pursuing. By the end of week four, we know what we're building, why it matters and how we'll measure success.

Week 5-8

Validate

Solution design and prototyping happen alongside user involvement. The people who will use the solution help shape it as it develops. Working MVPs give the team something real to test and evaluate rather than asking them to react to a finished product.

Week 9-10

Value & Scale

We deliver the working AI solution, a validated 12-month roadmap, and a quarterly execution plan. We also begin the process of putting the solution into use. At this point, your team has experience with the solution and a clear understanding of what comes next.

Proof