Ebenbuild

Creates patient-specific digital twin lung models from CT scans and clinical data to optimize mechanical ventilation.

PUBLIC

Attribute	Value
Name	Ebenbuild
Tagline	Creates patient-specific digital twin lung models from CT scans and clinical data to optimize mechanical ventilation.
Headquarters	Munich, Germany
Founded	2019
Stage	Seed
Business Model	B2B
Industry	Healthtech
Technology	AI / Machine Learning, Computational Modeling
Geography	Western Europe
Growth Profile	Venture Scale
Founding Team	Co-Founders (4)
Funding Label	Seed (total disclosed ~$2,780,000)

Executive Summary

PUBLIC Ebenbuild creates patient-specific digital twin lung models to personalize mechanical ventilation in intensive care and accelerate pulmonary drug development, a deep-tech approach that has attracted significant European grant capital and peer-reviewed validation. The company, founded in 2019 by a team of computational mechanics and AI researchers from the Technical University of Munich (TUM), converts CT scans and clinical data into physics-informed simulations of lung function [LinkedIn]. Its core wedge is a dual-market strategy: providing ICU clinicians with decision support for ventilator settings in acute respiratory distress syndrome (ARDS), while also offering pharmaceutical companies a platform for in-silico trials of inhaled therapeutics [mtec-sc.org]. The founding team, led by CEO Kei Wieland Müller and including TUM Professor Wolfgang Wall, brings deep scientific credibility directly from the field, a critical asset for navigating the regulatory complexities of medical AI [Crunchbase]. To date, Ebenbuild has secured approximately €5.6 million in total funding, anchored by a €2.5 million seed round from Bayern Kapital and High-Tech Gründerfonds in 2022 and followed by substantial non-dilutive grants from the European Commission and the German Federal Ministry of Education and Research [VC Magazin, 2026]; [BioM, 2026]. Over the next 12-18 months, the primary milestones to watch are the commercial translation of its validated technology into initial clinical partnerships and the expansion of its life sciences customer base beyond early consortium work.

Data Accuracy: YELLOW -- Core funding and team facts are corroborated by multiple sources; some product claims and market sizing rely on company statements.

Taxonomy Snapshot

Axis	Classification
Stage	Seed
Business Model	B2B
Industry / Vertical	Healthtech
Technology Type	AI / Machine Learning
Geography	Western Europe
Growth Profile	Venture Scale
Founding Team	Co-Founders (3+)
Funding	Seed (total disclosed ~$2,780,000)

Company Overview

PUBLIC Ebenbuild GmbH was founded in Munich in 2019, emerging directly from academic research at the Technical University of Munich (TUM) [tum.de, 2026]. The company was established by four co-founders, including Professor Wolfgang Wall, who is the founding director of TUM's Institute for Computational Mechanics, to translate years of research in computational biomechanics into clinical applications [tum.de, 2026].

Its initial focus was on developing patient-specific digital twin models of the lungs, a technology that was validated in a peer-reviewed study published in Nature Communications Medicine in 2024 [BioWorld, 2026]. The company secured its first institutional funding in March 2022, a €2.5 million seed round led by Bayern Kapital with participation from High-Tech Gründerfonds (HTGF) [VC Magazin, 2026]; [Munich Startup, 2026]; [BioM, 2026]. This was followed by significant non-dilutive grants, including up to €900,000 from the German Federal Ministry of Education and Research (BMBF) in 2024 and a €2.3 million grant from the European Innovation Council (EIC) in July 2025 [BioM, 2026]; [Munich Startup].

Data Accuracy: GREEN -- Company founding and major funding rounds confirmed by multiple independent sources including Munich Startup, BioM, and VC Magazin. The EIC grant is reported by Munich Startup. The BMBF grant figure is confirmed by BioM.

Product and Technology

MIXED Ebenbuild’s product is a software platform that generates patient-specific digital twin models of the lungs, a process that begins with standard medical imaging. The company’s public materials state the software ingests a patient’s CT scans and clinical data to create a high-fidelity, physics-informed model of the individual’s respiratory system [LinkedIn]. The underlying technology integrates computational fluid dynamics and tissue mechanics with AI to simulate lung function, airflow, and the mechanical forces exerted during ventilation [mtec-sc.org].

The platform serves two distinct but related application surfaces. For clinical use, it is positioned as an ICU decision-support tool for managing mechanical ventilation in patients with Acute Respiratory Distress Syndrome (ARDS). The software provides personalized ventilation settings and treatment recommendations aimed at minimizing ventilator-induced lung injury [htgf.de]. For pharmaceutical research, the same digital twin technology is used to conduct in-silico trials, simulating cohorts of virtual patients to study pulmonary drug delivery and efficacy, such as in pulmonary fibrosis [mtec-sc.org]. This dual-market approach is the company’s stated commercial wedge.

A key differentiator is the platform’s validation in peer-reviewed research. A study published in Nature Communications Medicine, co-authored by Ebenbuild’s scientific founders, demonstrated the technology could predict inhaled drug deposition across the entire human lung with high spatial resolution, showing excellent agreement against in vivo clinical imaging data [BioWorld, 2026] [Mirage News, 2026]. The company’s long-term roadmap, as mentioned in press, spans from these preclinical simulations to future clinical-grade decision support systems [PharmiWeb.com, 2026]. The technical stack is inferred from the required computational workloads to include high-performance computing, advanced numerical solvers, and machine learning frameworks.

PUBLIC The market for personalized, simulation-driven healthcare tools is expanding as regulators and payers increasingly recognize the value of digital evidence and the need to reduce clinical trial costs.

Ebenbuild operates at the intersection of two distinct but related markets: clinical decision support systems (CDSS) for intensive care and in-silico trial platforms for pharmaceutical development. The company's self-reported market potential for these combined areas is "over 300 billion euros by 2030" [Munich Startup]. This figure, however, is not independently verified and lacks a clear breakdown between the two verticals, making direct benchmarking difficult. For context, the global market for clinical decision support systems was valued at $4.5 billion in 2023 and is projected to reach $10.5 billion by 2032, according to a third-party report from Precedence Research [Precedence Research, 2024]. The digital twin market in healthcare, an analogous category, was estimated at $1.6 billion in 2024 and is forecast to grow to $21.1 billion by 2034, per a separate analysis from Future Market Insights [Future Market Insights, 2024].

Demand in the ICU segment is driven by persistent challenges in managing acute respiratory distress syndrome (ARDS), which carries a mortality rate of 35-46% [PubMed, 2023]. The standard of care involves protective mechanical ventilation, but optimal settings vary significantly between patients. This creates a clear need for tools that can personalize therapy to minimize ventilator-induced lung injury, a major cause of morbidity. In the pharmaceutical segment, tailwinds are even stronger. Regulatory bodies like the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) have published guidance endorsing the use of modeling and simulation in drug development [FDA, 2023]. The high cost and frequent failure rates of traditional pulmonary drug trials, particularly for conditions like idiopathic pulmonary fibrosis, create a powerful incentive for sponsors to adopt in-silico methods that can de-risk programs and accelerate timelines.

Key adjacent markets include the broader field of medical imaging analytics and computational physiology platforms. Substitutes are less sophisticated but more established: for ICU ventilation, this includes standardized protocols and basic decision trees embedded in ventilator software; for pharma R&D, it encompasses animal models and earlier-stage computational fluid dynamics (CFD) studies conducted by specialized CROs. The primary regulatory force is the evolving pathway for software as a medical device (SaMD), particularly in the EU under the Medical Device Regulation (MDR). Gaining regulatory approval for a Class IIb or III device intended for clinical decision support represents a significant, but necessary, hurdle for market entry.

Metric	Value
Clinical Decision Support Systems (CDSS)	4.5 $B (2023)
CDSS Projected	10.5 $B (2032)
Digital Twins in Healthcare	1.6 $B (2024)
Digital Twins Projected	21.1 $B (2034)

The cited growth trajectories for adjacent markets suggest a favorable environment for Ebenbuild's core technology, though the company's specific addressable share within these large categories remains unquantified with public data.

Data Accuracy: YELLOW -- Market sizing for Ebenbuild's specific offering is based on a single, unverified company statement. Adjacent market figures are drawn from third-party analyst reports.

Competitive Landscape

MIXED Ebenbuild operates in a specialized niche where direct, head-to-head competitors are not yet publicly named, but the company faces a landscape of established incumbents, adjacent substitutes, and emerging challengers across its two target markets.

A competitive map reveals distinct pressures by segment. In the clinical decision support system (CDSS) market for ICU ventilation, the primary alternatives are not other digital twin startups but established protocols and guidelines embedded in ventilator hardware and clinical workflows. Companies like Dräger, Getinge, and Medtronic dominate the ventilator market itself, and their software offerings for ventilation management represent the incumbent, hardware-integrated path to market [Munich Startup]. For pharmaceutical in-silico trials, the competitive set includes large simulation software providers like Ansys and Siemens Healthineers, as well as specialized computational biology firms. These players offer broader simulation platforms but lack Ebenbuild's specific focus on patient-specific, CT-derived lung models validated for drug deposition [BioWorld, 2026].

Ebenbuild's defensible edge today rests on its validated, physics-informed digital twin technology and its deep academic roots. The company's core differentiator is its ability to generate high-fidelity, patient-specific lung models from standard CT scans, a process rigorously validated against clinical imaging data in a study published in Nature Communications Medicine [BioWorld, 2026]; [Mirage News, 2026]. This scientific validation, coupled with the founding team's backgrounds at the Technical University of Munich's Institute for Computational Mechanics, creates a talent and IP moat that is difficult to replicate quickly [tum.de, 2026]. However, this edge is perishable if the company cannot translate its technical lead into commercial traction and regulatory clearance before larger, well-funded incumbents develop or acquire similar capabilities.

The company's most significant exposure lies in its dual-market strategy, which risks dividing focus and resources. In the clinical CDSS segment, Ebenbuild does not own the critical distribution channel,the ventilator itself,and must navigate complex hospital procurement cycles and stringent medical device regulations. A competitor like Dräger could integrate basic predictive analytics into its next-generation ventilator software suite, effectively commoditizing Ebenbuild's value proposition at the point of care. In the pharma R&D segment, the company faces competition from software giants with established sales forces and existing relationships with top-20 pharma accounts.

The most plausible 18-month competitive scenario hinges on execution in one core market. If Ebenbuild can secure its first major pharmaceutical partnership for in-silico trials, leveraging its recent validation study, it could establish a beachhead in the higher-margin, less-regulated pharma services segment. A winner in this scenario would be a firm like Ebenbuild that proves its models can accelerate drug development timelines for a specific pulmonary indication. Conversely, a loser would be any startup attempting to compete directly on the clinical hardware front without a clear regulatory pathway or hospital partnership; a company focusing solely on ventilator algorithm software without the underlying patient-specific model could be easily displaced by integrated offerings from the ventilator OEMs.

Data Accuracy: YELLOW -- Competitive analysis is inferred from market segments and adjacent players; no direct competitor names are confirmed in public sources.

Opportunity

PUBLIC Ebenbuild’s opportunity lies in establishing a physics-based computational standard for managing respiratory disease, a wedge that could unlock a dual-revenue model across clinical care and pharmaceutical development.

The headline opportunity is for Ebenbuild to become the default in-silico lung platform for both critical care and pulmonary drug trials. This outcome is reachable because the company has already validated its core digital twin technology against clinical imaging data, a step that moves the product from academic concept to a tool with demonstrated predictive accuracy. A study published in Nature Communications Medicine, cited in multiple industry reports, showed the company’s models could predict inhaled drug deposition with "excellent agreement" against 3D SPECT/CT scans from a controlled clinical study [BioWorld, 2026] [Mirage News, 2026]. This validation provides the necessary scientific credibility to penetrate two conservative, evidence-driven markets: hospital ICUs seeking to personalize ventilation for ARDS patients, and pharmaceutical companies running costly pulmonary drug development programs.

Two primary growth scenarios outline plausible paths to scale. The first is a land-and-expand motion within academic medical centers and pharmaceutical R&D departments, while the second involves regulatory endorsement that accelerates adoption.

Scenario	What happens	Catalyst	Why it's plausible
Dual-Market Penetration	The company secures lighthouse contracts with major pharmaceutical firms for in-silico trials and parallel pilot programs in university hospital ICUs. Revenue compounds as pharma projects scale and hospital pilots convert to enterprise licenses.	A publicly announced partnership with a top-20 pharma company for a pulmonary fibrosis drug program, validating the commercial use case.	The technology is already positioned for both markets, with public messaging targeting "in-silico trials" for pharma and "ICU decision support" for clinicians [mtec-sc.org] [htgf.de]. The European Commission and German BMBF have awarded significant non-dilutive grants specifically for this technology development, signaling institutional belief in its dual application [BioM, 2026].
Regulatory Pathway as a SaMD	Ebenbuild achieves regulatory clearance (e.g., CE Mark, FDA De Novo) for its clinical decision support software as a Software as a Medical Device (SaMD). This transforms it from a research tool into a reimbursable clinical product in hospitals.	Successful completion of a clinical validation study aimed at regulatory submission, likely supported by EU grant funding.	The company’s long-term roadmap explicitly includes bringing "clinical-grade decision support systems" to market [PharmiWeb.com, 2026]. The €2.3 million grant from the European Innovation Council in 2025 is precisely the type of funding used to de-risk the clinical and regulatory pathway required for such a product [Munich Startup].

Compounding for Ebenbuild would manifest as a data and methodology moat. Each new patient scan processed and each new drug compound simulated would refine the underlying AI and physics models, improving predictive accuracy. More importantly, early adoption by pharmaceutical companies for specific disease models would create de facto industry standards. If a major drug developer uses Ebenbuild’s digital lung to design a clinical trial for a new inhalable therapy, that methodology could become the benchmark for future programs in that indication, creating significant switching costs. The company’s published validation work is an early signal that this flywheel of scientific credibility leading to commercial adoption has begun to turn [DDW-Online, 2026].

The size of the win, should the dual-market scenario play out, can be framed by comparable markets. The global market for clinical decision support systems is projected to reach $10.8 billion by 2030, while the cost of pulmonary drug development runs into the billions, with in-silico trials representing a high-value efficiency tool [Grand View Research, 2023]. A more direct comparable might be the valuation of companies like HeartFlow, which commercialized a coronary artery digital twin and was valued at approximately $1.5 billion following its Series F round in 2021 [Reuters, 2021]. While not a forecast, this comparable suggests that a category-defining, regulatory-cleared digital twin platform in a major organ system could command a valuation in the low billions (scenario, not a forecast).

Data Accuracy: YELLOW -- The core technology validation and grant funding are well-cited. Market size comparables and the specific regulatory pathway are based on broader industry reports and logical inference from the company's stated roadmap.

Sources