Digital transformations fail for a predictable reason: organizations prioritize technology over the human expertise that makes it work. We call this the lost knowledge problem—and we've seen it across every sector, from manufacturers who lose irreplaceable process knowledge to healthcare systems that disrupt the relationships that define good care.
The antidote isn't less technology. It's intelligent products designed to preserve and scale what your organization knows best.
We build predictive applications and decision support tools enhancing human judgment rather than replacing it.
Our approach to automation respects the continuing role of human oversight, creating systems that make recommendations operators and managers can evaluate through the lens of their experience—from maintenance alerts to quality forecasts to resource optimization.
Examples:
Systems that enhance strategic decision-making
AI-driven insights refined by human expertise
Context-aware recommendations for complex scenarios
Automation that adapts to evolving business needs
Decision frameworks integrating intuition and data
We create knowledge management systems and tools, deliberately integrating expertise, experiential insights, and tacit knowledge with hard data.
Knowledge products go beyond "what happened" to help answer "why it happened" and predict "what might happen next." These products use data analysis, statistical modeling, and basic machine learning to uncover correlations, find insights, or predict future trends.
Examples:
Predictive models to anticipate future customer churn and sales
Machine failure forecasting using telematics for preventative maintenance
Big data tools to analyze product feedback and sales trends
We don't just provide data. We provide information and recommendations to optimize human decision-making.
Data products that deliver information create meaningful connections between otherwise isolated information. Your teams will be able to track vital operational indicators like quality control and production efficiency in real-time. From monitoring supply chain performance to predicting maintenance needs, these tools provide the visibility and insights needed to make proactive, data-driven decisions.
Examples:
Simplified and easy-to-understand insights, with data gathered from multiple sources
Data-driven recommendations based on gathered, structured data
Clear models and rules for better decision-making.
We create products and systems that capture "dark data" – information such as institutional knowledge, overlooked internal sources, and IoT and sensor data. By capturing what matters, we can uncover useful insights that standard methods often miss.
Our strategists and engineers ensure your data is captured, structured, and primed for insightful analysis. These intuitive applications centralize user-generated data, making it not just accessible, but truly actionable.
Examples:
Personalized dashboards
Customized notifications and alerts
Automated reports that regularly generate and distribute key metrics
Most organizations aren't short on data. They're short on products that know what to do with it. TXI builds intelligent products that close that gap, combining strategy, engineering, and human-centered design into solutions that learn, predict, and adapt alongside your operations.
We align your data capabilities with your business goals and product strategy—identifying where intelligent products can create the most impact and building a roadmap to get there
We establish the rules, policies, and quality controls that make your data trustworthy—so the products built on top of it can make decisions you can stand behind.
We design interfaces that make complex data legible and patterns clear for the people who need to act on it
We connect data assets across your organization into a cohesive, scalable foundation—so your intelligent products have the full picture
We build the pipelines and infrastructure that keep your data flowing reliably—the unglamorous work that makes everything else possible
We apply machine learning and predictive modeling to find what your data is trying to tell you—and build products that act on it
Solving hard business problems requires teams that understand the real business stakes. Unlike consultancies that assign junior teams supervised from a distance, we deploy the A-squad: senior practitioners averaging 17 years of experience who engage with your experts as peers.
Our integrated team of strategists, designers, and engineers doesn't just build what's technically sound—they build what actually works for how your organization operates. And because TXI is employee-owned, every person on your project has a personal stake in your success.
Data value is crucial for businesses as it drives growth, efficiency, and competitive advantage. Leveraging data effectively enables:
New revenue streams – Identifying opportunities for monetization, optimizing pricing, and uncovering untapped markets.
Timeliness – Making faster, data-driven decisions that improve responsiveness to market changes and operational challenges.
New products & services – Innovating based on customer insights and market trends to develop offerings that meet emerging needs.
Enhanced customer experiences – Personalizing interactions, improving satisfaction, and fostering loyalty through data-driven insights.
By harnessing data strategically, businesses can drive innovation, improve efficiency, and stay ahead in their industry.
Data value manifests in various ways across industries, driving efficiency, innovation, and revenue growth. Some key examples include:
Customer insights & personalization – Retailers use purchase history to recommend products, increasing sales and engagement.
Predictive maintenance – Manufacturers analyze machine sensor data to prevent breakdowns, reducing downtime and costs.
Dynamic pricing – Airlines and e-commerce platforms adjust prices in real-time based on demand and competitor trends.
Fraud detection – Banks use machine learning to identify suspicious transactions and prevent financial losses.
Operational efficiency – Logistics companies optimize routes and fleet management using real-time tracking and analytics.
New product development – Consumer brands analyze social media and feedback data to identify trends and create products customers want.
Finding data value involves identifying, analyzing, and leveraging various types of data within a business's ecosystem. Here are key approaches:
Types of data – Businesses should assess both structured (e.g., transaction records) and unstructured data (e.g., social media interactions, customer feedback) to uncover insights.
Existing data – Often, valuable data already exists within an organization (e.g., CRM systems, operational logs) but may be underutilized. Cleaning, organizing, and analyzing this data can unlock significant value.
Ecosystem – Collaborating within the broader business ecosystem—suppliers, partners, and industry networks—can provide access to valuable external data sources that enhance decision-making.
Intangible assets – Data itself is often an intangible asset. Using analytics to monetize data insights, such as creating reports or predictive models, can turn this asset into tangible business value.
Data marketplaces – Exploring third-party data marketplaces offers access to external data sets that may complement internal data, helping businesses make more informed decisions or discover new revenue streams.
Measuring data value involves assessing its potential to drive business outcomes and generate economic benefits. Here are key methods to evaluate data's value:
Data valuation – Establishing the monetary worth of data by analyzing its contribution to business performance, either through direct revenue generation or cost savings.
Methodologies – Common approaches include the Income Method (assessing future revenue potential), the Cost Method (calculating investment costs to acquire or create the data), and the Market Method (comparing data with similar assets in the market).
Data monetization – Determining how data can be directly monetized, whether through selling data to third parties, offering data-driven services, or licensing insights to other organizations.
Economic benefits – Quantifying data’s impact on reducing operational costs, increasing efficiency, improving decision-making, or driving revenue growth (e.g., through targeted marketing or process optimization).
Market value – Comparing data against industry benchmarks or evaluating it based on its ability to create competitive advantages, such as improving customer experiences or enabling faster innovation.
Find out how intelligent solutions can accelerate growth for your organization.
