Our Approach, step 3 of 3

Act before Problems Reach the Floor

With clean, connected data in place, we build models and AI agents that spot issues early, forecast demand and capacity, and draft the next step for your team to approve.

From Reacting to Planning Ahead

The goal is not a dashboard. It is a warning early enough that the fix can be planned into a normal day.

Before

Equipment runs until it fails, with no warning
Repairs mean rush parts, overtime and lost output
Plans are built from last month instead of current conditions

After

Condition data flags a problem days ahead
Maintenance is planned into the schedule with the right parts ready
An AI agent drafts the work order for a person to approve

How It Works

Four stages, each small enough to review before the next one starts.

01
Check the Data

We confirm the data is complete and connected enough to support the use case.
02
Prove the Signal

We test a model on your history and review the results with the people who run the assets.
03
Put It in the Workflow

Alerts and recommendations land in the apps and systems your teams already use.
04
Monitor and Retrain

We track accuracy in production and retrain as equipment and operations change.

What We Deliver

Condition Monitoring

Sensor and inspection data turned into health indicators and alerts that crews trust.

Predictive Maintenance

Models that flag equipment likely to fail and create the work order before it does.

Forecasting

Demand, capacity and inventory forecasts built on your own operational history.

AI Agents with Guardrails

Agents that read your records, draft reports and actions, and wait for human approval where it matters.

What Predict Looks Like in Practice

Prediction projects that start from connected data and end in a normal work order or schedule change.

Oil and gas

Early Warning on Rod Pumps

The situation: Pump problems were found on the next visit or when production dropped, often after damage was done.
What we built: A model that compares each pump card with its normal shape and flags patterns like fluid pound, with a suggested adjustment.
What changed: Operators adjust the pump before it fails and plan well visits around the wells that need attention.

Manufacturing

Quality Drift before Out of Spec

The situation: Measurements were checked against limits, so the line was adjusted only after parts were already out of spec.
What we built: Trend detection on inline measurements that alerts the line lead while the values are still inside the limits.
What changed: The line is adjusted early, scrap and rework drop, and quality reviews focus on causes instead of sorting parts.

Logistics

Dock Capacity for the Next Shift

The situation: Dock staffing was set from a fixed plan, so busy hours meant waiting trucks and quiet hours meant idle crews.
What we built: An hourly arrival forecast from carrier updates and history, compared with dock capacity for the next shift.
What changed: Supervisors add or move a crew before the peak, and drivers spend less time waiting at the gate.

What You Need to Start

One failure or forecast that matters to operations
History for that asset or process, even if it is incomplete
Connected data, or a plan to connect it as part of the project
The people who run the asset, to review what the model finds

Related Services

1 Digitize
2 Optimize
3 Predict

Back to the overview See the Full Approach See how Digitize, Optimize and Predict build on each other, and where your operation fits today.

Test One Prediction on Your Data

Tell us which failure or forecast matters most. We will check your data and propose a proof on your own history.

01Discovery

We review your processes, systems and objectives.
02Proposal

Defined scope, timeline and investment for phase one.
03Delivery

A production release, with a roadmap to scale.