As a trusted data and AI partner to leading B2B industrial companies, our models are already driving transformative performance improvements across the sector.

For manufacturers with a global footprint, scaling data and AI across the entire organization is essential to unlocking maximum value. We deploy artificial intelligence to optimize processes across business units, service lines, and geographies—enabling companies with complex manufacturing networks to overcome productivity barriers and accelerate growth.

AI as a Catalyst for Competitive Advantage

With our extensive suite of ready-to-deploy AI models, we help manufacturing companies make smarter, faster decisions across every stage of the production lifecycle. From optimizing supply chains and managing B2B pricing and revenue, to predicting events that could disrupt operations—we turn data into tangible business value.

Our AI solutions are already driving measurable performance gains across the manufacturing sector. Building on this experience, we collaborate with your teams to translate advancements in data and technology into scalable AI use cases, continuously pushing the boundaries of what’s possible for your business.

Delivering scalability, reusability, and security, with measurable reductions in cost.

A leading high-tech manufacturing company and innovation pioneer in the semiconductor industry, providing chipmakers with advanced hardware, software, and services.

Challenge

After a period of strong organic growth—and with further expansion on the horizon—the company faced increasing challenges from scattered and siloed data across the business. This fragmentation made it difficult to access and share high-quality data, slowed the development of new applications, reduced service levels, and undermined customer trust in data security and IP protection. The key challenge was to consolidate and streamline the disjointed infrastructure while enabling seamless data exchange between producers and consumers.

Solution

We partnered with a leading cloud-native service integrator to consolidate more than five platforms into a single, secure data and technology solution—reducing costs while enabling scalability, reusability, and enhanced security. Standardized data management principles and a new operating model were introduced to support seamless exchange between data producers and consumers. In addition, we transformed a tightly coupled architecture—where data and applications were bound to their underlying platforms, limiting portability—into a modern, decoupled architecture. This new approach leverages platform capabilities while ensuring interoperability across platform instances.

Outcome

This transformation unlocked a multi-billion-euro business case by increasing the value of data through easy access to high-quality information, trusted and secure data exchange, greater reusability, and faster time-to-market.

• Accelerate: Launch new solutions in days instead of months.
• Enable: Unlock new use cases, such as data-streaming projects.
• Re-use: Repurpose data products and features to extend value and drive innovation.
• Reduce: Apply a build-once, use-many principle to eliminate duplication and significantly cut platform DevOps costs.

Advancing clinical trial excellence through Data and AI innovation

Accelerating clinical trial execution with AI-powered methods to reduce costs and deliver innovative medicines to patients faster.

Challenge

Clinical trials are complex and highly regulated studies designed to evaluate the efficacy and safety of medical interventions. Late-stage trials—required for regulatory approval of new drugs—typically involve hundreds or even thousands of patients across regional or global sites, representing major investments with budgets ranging from tens to hundreds of millions of US dollars. Their scale and complexity make them highly vulnerable to delays, often driven by inefficient site operations, slow patient recruitment, or poor patient retention. In fact, one in three late-stage trials must extend timelines to meet enrolment targets, and for the average drug, every day of delay adds an estimated $500,000 in costs.

One such global late-phase trial, sponsored by a leading pharmaceutical company to evaluate a novel therapy for patients with advanced chronic disease, faced significant enrolment challenges and severe delays.

Outcome

Techstar Industrial conducted a quantitative risk evaluation of the trial, resulting in a clear, actionable set of recommendations to mitigate challenges. The process included:

• Identification of highly similar benchmark studies
• AI-assisted assessment of protocol risk factors
• Comparative analysis of patient eligibility criteria
• Site selection analysis and enrolment forecasting

The analysis revealed that the trial’s limited and difficult-to-reach patient pool was a critical bottleneck. Specific recommendations to simplify eligibility criteria expanded the addressable patient population by at least 40%, improved site search for eligible patients, and strengthened outreach efforts.

Site potential modelling further showed that two-thirds of study sites had low enrolment potential, while 80% were already engaged in competing trials within the same therapeutic area. To address this, Rewire identified 15 high-potential sites projected to contribute up to 25% of the enrolment target, mitigating the risks of extended recruitment.

Impact at a glance:

• +40% expanded patient pool — achieved by simplifying complex eligibility criteria that excluded patients likely to benefit.
• 15 high-potential sites identified — expected to deliver 25% of the late-stage trial’s enrolment target.