Solutions

What we deliver, anchored to KPIs.

Every engagement names the metric, agrees on the baseline, and verifies the lift.  We start with the fundamentals of your process, then use the right technology and methodology to move the needle in the right direction.

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Outcomes We Target

Five places where AI delivers the most value.

Most plant-floor value clusters into five themes. We start where your highest-value, most-ready opportunity lives.

01

Throughput & OEE

The Symptom

The line runs, but rarely at nameplate. Micro-stops nobody logs, slow cycles, and changeovers that run long. Ask five people where the bottleneck is and you get five answers.

What's Missing

A shared, honest view of where the hours actually go. Losses tied to a real cause instead of a hunch, and a constraint you can prove instead of argue about.

What We Do

Break OEE down into its real losses across availability, performance, and quality, find the true constraint, and put the next best action in front of the crew running the line. Fix it, then move to the next one.

Targets
OEE Throughput Changeover time
02

Equipment Reliability

The Symptom

Six-figure-per-hour downtime. Calendar-based maintenance. Chronic bad actors everyone knows by name, and a firefighting culture that never gets ahead of them.

What's Missing

An integrated view of process and maintenance data, repeatable failure signatures, and an owner after the pilot ends.

What We Do

Combine predictive maintenance with failure-mode effects analysis, optimize preventative maintenance plans, and use unsupervised machine learning to catch outliers and anomalies before your equipment fails again.

Targets
MTBF Maintenance Cost Unplanned downtime
03

Quality

The Symptom

Every crew runs the unit a little differently, so results drift shift to shift. Lab feedback comes back late and can't always be trusted, and by the time you see the problem, the product is already made.

What's Missing

A clear read on which process signals actually move your quality numbers, and guidance operators believe enough to act on.

What We Do

Use multivariate quality analytics (MVA) to connect the process signals that matter to the quality outcomes you care about, so the people running the plant can steer before it goes out of spec.

Targets
Right-first-time Out-of-spec product Rework
04

Yield & Raw Material Utilization

The Symptom

More raw material goes in than the recipe calls for. It shows up as giveaway, off-spec blends, and rework nobody fully accounts for. Theoretical yield and actual yield have a gap, and nobody owns it.

What's Missing

A tight link between operating conditions and where material is lost, and a way to see conversion loss while there's still time to correct it.

What We Do

Model what actually drives yield, expose where material is lost, and put live targets in front of operators to close the gap between theoretical and actual, batch after batch.

Targets
Yield Giveaway Raw material loss
05

Energy & Sustainability

The Symptom

Energy is your largest variable cost. Heat-exchanger fouling, steam leaks, and controls left in manual quietly eat it, undetected for months.

What's Missing

Meter-level attribution, asset-level optimization targets, and the sustained focus that keeps them honest.

What We Do

Energy baselining, optimization ranges delivered in the control room, and continuous improvement as a managed service.

Targets
Energy intensity Emissions per unit Flaring & venting
Capabilities

The full toolkit. Methodology first, technology second.

We don't lead with the algorithm. We start with your problem, then bring the right capability to it. Technology is an enabler, not the destination.

Digital Transformation Roadmap

A sequenced, fundable plan that moves you out of pilot purgatory and into scaled value, supported by the data architecture and governance needed to sustain it.

Generative AI

Practical GenAI that runs on your own contextualized data. Ask natural-language questions to solve your most complicated process and chemistry questions. Context is king here, and without the context, the large language model is just guessing.

Machine Learning

Transparent models you can explain and defend: PCA, PLS, gradient boosting, and others. We use them for prediction, soft sensors, outlier and anomaly detection, and changes in operating conditions. We build them to run on shift.

Predictive Analytics

We turn your historical and real-time signals into early warnings on yield, quality, energy, and reliability. The point is to catch the problem before the loss shows up in the numbers.

Outlier & Anomaly Detection

We surface the abnormal conditions that matter most, such as plugged valves, drifting control loops, and the bad actors that erode your uptime, and route them to the people who can act on them.

LEAN Six Sigma & DMAIC

We're not here to replace a proven methodology. We're here to run it faster. AI moves you through Define, Measure, Analyze, Improve, and Control with the discipline intact. The methodology came first. AI accelerates it.

Process Optimization

Stabilize the process first, then lift throughput, yield, quality, and energy. Under the hood it's bottleneck analysis, setpoint strategy, and bad-actor elimination. At the end of the day we're only ever doing two things: reducing variation or moving the average.

Statistical Process Control (SPC)

The most underrated tool in the toolkit, and we deploy it at scale. SPC gives every operator a consistent, teachable view of process health that reads the same on every line and every site. Simple enough to run on shift, strong enough to deliver value at scale.

Change Management

The hardest part, and the part most people skip. Standing up the tool takes months. Getting people to actually use it, shift after shift, takes years. We do the unglamorous work: sponsor mapping, leader standard work, and the adoption habits that make a new way of working stick.

Actionboards

We banned the word “dashboard.” A dashboard shows you data. An Actionboard tells you what to do with it. Every item on the Actionboard answers three questions: what is the action, who owns it, and are they trained and equipped to take it? If an item cannot answer those questions, it does not belong on the Actionboard.

Frameworks

Signature Methods

Field-tested frameworks, proven at enterprise scale.

01

The AI Highway

Contextualized, reusable data as infrastructure, with standardized, templated analytics. One foundation every use case runs on, instead of rebuilding it each time.
02

PPDTG Maturity Model

People, Process, Data, Technology, Governance. A straight read on where you actually are before anyone buys software.
03

AI Accelerated LEAN Six Sigma

Six Sigma still runs the show. AI just gets you through Define-Measure-Analyze-Improve-Control faster.
04

Actionboards, Not Dashboards

Every visualization has to drive a decision: what action, by whom, and are they equipped to take it.
05

Operationalizing AI

The operating model that turns a finite project into an ongoing capability, because the build is the easy part.
06

Daily Rapid Problem Solving

The Actionboard flags it, the team finds the root cause and corrective action, and runs that loop as many times a day as they can.
The AI Highway

The Foundation for enterprise AI at scale

Like an interstate: expensive to build, but it pays back for decades. Every solution above runs on the same four layers, which is why the second use case costs a fraction of the first.

LAYER 1

Grounded data

Reliable ingestion from historians, MES, LIMS, and maintenance
LAYER 2

Contextualization

Governed namespace and asset models, standardized for scale.
LAYER 3

Analytics

SPC and capability first; multivariate and ML where they earn it
LAYER 4

Consumption

Actionboards for operators, tools for engineers, a layer for data science

"A Dashboard shows data, but an Actionboard drives behavior."

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