Knowledge Exists — But Is Disconnected  

Critical data remains trapped in silos. Expertise is confined within teams. Every day, valuable insights are lost because organizations lack the ability to connect their knowledge with their data. In a competitive landscape, this disconnection costs time, money, and missed opportunities. 

Knowledge Unification

Unified knowledge. Modular flexibility. Enterprise results.  

Contextualize unifies scattered data sources into a flexible knowledge system that works within your current environment.  Our highly modular framework adapts to your existing tools, processes, and workflows—giving you complete control over how information is organized, connected, and applied.  

Contextualize bridges isolated data systems and sources into a modular scalable framework, preserving the integrity of your data while amplifying its value. By embedding context, you transform your data into actionable insight.

The whole is greater than the sum of its parts.

- Aristotle

Contextualize the numerous moving parts of your organization into a unified whole using Carta. Carta is Contextualize’s enterprise-ready platform comprised of a modular suite of tools purpose-built to bridge fragmented data and manufacturing expertise to maximize the value of our clients’ knowledge. Experience the many benefits of Carta by Contextualize today.

  • Capture expert context at the source.

    Seamlessly extract critical insights from subject matter experts to preserve institutional knowledge and intent.

  • Go beyond integration—unlock insight.

    Integration is just the beginning; Contextualize’s Carta platform transforms data into actionable understanding.

  • Your data stays where it belongs.

    Carta operates within your environment—ensuring data sovereignty, security, and compliance. If data is the new currency, why give it away?

  • Extend, don’t replace, your existing platforms.

    We enhance the tools you already rely on—without disrupting established systems or workflows—and
    unlock new value.

  • Complementary, not competitive.

    Contextualize works alongside platforms like Google Cloud,
    AWS, Azure, and more—amplifying their value with deep context.

  • Maximize your current investments.

    No rip-and-replace required—we augment your existing systems without forcing new infrastructure.

  • Data alone isn’t enough.

    Carta captures the human expertise needed to turn raw data into meaningful, scalable insight.

  • Preserve the ‘why’ behind the data.

    Preserve context, intent, and rationale—so your organization can understand not just what happened, but why it matters.

  • Bridge data to decision

    By embedding expert knowledge into data workflows, Carta enables faster, more confident decisions grounded in context.

Use Case: Classifying Pores in 3D Printed Metal Parts

Challenge

In metal additive manufacturing, defects like pores and cracks are inevitable. Despite advances in 3D printing, these flaws form randomly throughout a part, making it difficult to compare one print to another or identify consistent causes. Traditional X-ray computed tomography (XCT) reveals these defects — but with hundreds of anomalies and hundreds of thousands of data points tracking the telemetry of each scan, the volume and randomness of the data make analysis nearly impossible. Until now.

Key Insight

The critical question is no longer “Are defects near a feature?” —but rather, “Are defects consistently near the same type of feature?”

To answer this, spatial patterns need to be uncovered across previously disconnected data collected by different machines at different times by different people.

Solution

Contextualize has developed a novel correlation algorithm that looks for patterns in spatial data. Rather than focusing on individual defects, Contextualize's spatial correlation algorithm searches through hundreds of billions of possible interactions looking for part properties that correlate with higher defect densities. Instead of just counting how many defects appear, this approach maps what types of features attract defects, creating a new framework for quality control, print optimization, and design feedback.

Impact

This method transforms raw XCT data and telemetry data into actionable knowledge. But more generally, this approach sensibly combines data collected on two different machines at two different times in two completely different ways. It creates a single source of truth that provides deeper insight into failure modes, enables better designs, more reliable prints, and reduced waste– without increasing the inspection burden.

Unify Knowledge Today