Evaluation Summary Report
Score: 1.00 / 3Positives
- Integrated platform combines legal, market, and valuation workflows in a single execution layer.
- Go-to-market path is staged with pilots, partner channels, and compliance planning.
- Technical roadmap links product maturity to measurable deployment milestones.
- IP posture includes FTO coverage plus trade-secret and trademark controls.
Negatives
- Commercial assumptions remain optimistic relative to evidence of paid demand.
- Some risk mitigations lack trigger thresholds, owners, and response timelines.
- Several scale-up and budget assumptions need stronger operational justification.
- Impact claims are directionally strong but not yet supported by robust methodology.
Criterion 1 - Excellence
1. Novelty and breakthrough character of the innovation
The clarity and pertinence of the proposed project’s objectives, and the extent to which the proposed work is breakthrough and goes beyond the state of the art are average.
The product demonstrates a high degree of innovation, surpassing existing asset-tech and auction-specific solutions by integrating data from multiple international sources, performing AI-powered legal and financial assessments of potential transactions, and offering market forecasts and projected returns on investment—alongside a real-time auction interface. Notably, the ambition to assess legal risks using unstructured data from diverse sources stands out as particularly bold and technically challenging. However, the proposal lacks sufficient detail regarding the specific outputs of the AI-based legal assessment, leaving it unclear what actionable insights or decisions this component is expected to generate.
The clarity and pertinence of the proposed project’s objectives, and the extent to which the proposed work is breakthrough and goes beyond the state of the art are very good.
The application describes the industrial asset technology solutions available on the market very clearly and with justification - why they don't provide sufficiently sophisticated solutions to the complex challenges of the distressed industrial asset auction market. The AssetPulse platform is a deep technology solution that offers significantly more comprehensive AI-based solutions than its competitors. It is designed to revolutionize the industrial asset auction market around the world. The added value that the innovations brings to the market compared its competitors is clear and it also has been described clearly in the application: 1) increasing market transparency, 2) improving investment decision-making and 3) accelerating the sale of distressed assets. Despite the fact that the innovation is not "the only one" on the market, its content is much more comprehensive than competitors one and it brings significant added value to customers compared to competitors. All of this has described very good in the application. AssetPulse platform also offers different ways to search information about industrial asset. Information can be searched based on text, image, video, floor plan or sketch images. In particular, the recognition based on floor plan, image and video is unique way to search destination and makes the platform solution breakthrough. Another breakthrough factor is cross-border legal integration. This allows investors to participate in auctions acrossmultiple countries without having to manually interpret local laws and regulations. AssetPulse is able to predict industrial asset valuations and investment risks more accurately than any competing platform.
The clarity and pertinence of the proposed project’s objectives, and the extent to which the proposed work is breakthrough and goes beyond the state of the art is very good.
The platform is an AI-driven innovation designed to transform transactions, offering continuous access to distressed asset opportunities and enabling data-driven investment decisions through AI-powered insights. By integrating legal, financial, and market data across multiple jurisdictions, AssetPulse-AI enhances decision-making with predictive models and automated risk assessments, tackling market fragmentation. The project stands out with proprietary AI models capable of interpreting complex, unstructured data at unprecedented speed and scale. Its ability to provide asset value predictions, legal risk evaluations, and real-time actionable insights showcases its technological sophistication. By leveraging AI, machine learning (ML), neural networks (NN), and natural language processing (NLP), the platform surpasses players like legacy marketplace incumbents, particularly in integrating legal, financial, and market analyses. This deep-tech solution offers cross-border investors transparent legal due diligence and financial forecasting. Innovations include multimodal search capabilities, enabling users to find assets via text, images, floor plans, and sketches. Additionally, NLP for multilingual legal text processing and an AI-powered risk assessment engine, supported by predictive analytics and Monte Carlo simulations, provide unmatched investment insights. AssetPulse-AI's integration of diverse data, real-time risk assessments, and cross-border capabilities makes it a unique solution. Its technological edge creates a competitive barrier, requiring 2–3 years for rivals to replicate. The project’s innovation and successful proof-of-concept trials further demonstrate its disruptive potential in industrial asset auctions.
1.1 Challenge topic applicability
2. Timing
The effectiveness and pertinence of the timeline to bring the technology to the market is average.
The applicant argues that the timing is optimal for launching the product, citing three global trends: rising foreclosure rates and growing volumes of non-performing loans (NPLs), increasing global demand for cross-border industrial asset investments, and rapid advancements in AI technologies. While the enabling role of AI is a persuasive point, the other two arguments are less compelling. Specifically, the proposal does not convincingly demonstrate that industrial asset investors interested in cross-border opportunities would be drawn to distressed industrial asset auctions. Large institutional investors typically pursue sizable, low-risk deals facilitated through established sales channels. Meanwhile, it is questionable whether individual investors would be willing to engage in high-risk investments involving distressed assets often burdened by complex legal challenges.
The effectiveness and pertinence of the timeline to bring the technology to the market is very good.
As we know, the situation in the industrial asset market is weak overall due to various reasons. The industrial asset sector holds over 750,000 legal auctions annually in Europe and in the United States, the equivalent number exceeds 2,000,000 auctions annually. In its application, the applicant company has presented a very good analysis based on market knowledge and clearly justified why now is the right time to bring the innovation to the market. The applicant company has responded to market needs and created an AI-based platform to address the needs of the market. Their AssetPulse platform addresses the presented challenges by transforming the underutilized market into an easy-to-use and transparent investment environment. Based on the forecast, the platform is estimated to increase the participation of potential investors in legal auctions by up to 30%. The increase into the numbers of participants has a significant impact on the success of the auction. The success rate is estimated to increase by 25%, which is a remarkable increase in a challenging market situation. At the same time, the innovation is predicted to reduce 50 % the time that will be spend on transactions. In addition to time, it also generates significant financial savings. The developed technology 1) is the only platform that combines data in real time, 2) it includes artificial intelligence based legal and financial risk assessment, 3) allows investors to participate in legal auctions from multiple countries, 4) predictive analytics for asset values and market conditions, and 5) has user-centric design at the core of the innovation. Based on the above, it can be stated that the innovation creates new markets and is at the forefront of technological trends.
The effectiveness and pertinence of the timing to bring the technology to the market is very good.
The project is an AI-driven innovation transforming industrial asset transactions by addressing legal complexities and fragmented data in a large distressed-asset segment. The platform simplifies cross-border auctions, particularly benefiting smaller investors, and aligns with growing demand for data-supported investment decisions. It democratizes auction access by enabling AI-powered, data-driven investment decisions, increasing transparency, reducing friction, and fostering fairer market conditions. It also improves market mobility by unlocking underutilized assets. Technologically, AssetPulse-AI integrates machine learning, NLP, neural networks, cloud computing, and big data analytics. Its multimodal search (text, images, floor plans, sketches), real-time legal and financial risk assessments, and predictive models provide strong market insights. The project has reached TRL 6 with successful proof-of-concept tests and aims for TRL 8 through model refinement, multimodal search improvements, and pilot testing. A well-defined roadmap, proven technological feasibility, and disruptive potential position it as a strong contender in AI-driven industrial asset auctions, making the effectiveness and pertinence of the timeline to bring the technology to the market very good.
3. Technological feasibility
The credibility of the pathways taken to test and validate the technology and complete all aspects of TRL 5 is average.
The applicant states that the product has been validated through rigorous pilot programs and Proof of Concept trials, demonstrating its capacity to process large volumes of data. It also reports participation in two real-life auctions, resulting in the acquisition of two assets at a significant discount to market valuation. However, the acquisition of two assets alone does not constitute proof of the quality or reliability of the product’s legal and financial assessments, nor does it validate the overall attractiveness of the pricing model. The technology described is ambitious, and the claimed results are impressive—but several remain insufficiently explained. For example, a key achievement cited is the "standardisation of legal data across multiple jurisdictions." This concept is not clearly defined, and the proposal does not clarify the extent to which this standardisation mitigates legal uncertainty or what residual risks remain for investors engaging in cross-border distressed asset transactions.
The credibility of the pathways to test and validate the technology and complete all aspects of TRL6 is very good.
The proposed stack is already validated in a relevant environment and the transition logic from TRL6 to later stages is coherent. The dossier explains architecture choices and expected gains compared with current practice. A remaining weakness is limited evidence on reproducibility across jurisdictions and data regimes before full rollout. Add benchmarked pilot results and clearer acceptance thresholds for each validation gate.
The credibility of the pathways taken to test and validate the technology and complete all aspects of TRL 5 is very good.
AssetPulse-AI has been securely developed and validated, reaching TRL 6. The platform operates on a cloud-based architecture with robust security and compliance measures, including GDPR-aligned controls and independent security reviews. Strict access controls, multi-factor authentication (MFA), and data encryption ensure security. Nexora Labs conducts ethical audits and data protection impact assessments to maintain compliance. Successful Proof-of-Concept (PoC) tests in recent cycles demonstrate real-world effectiveness. The platform participated in pilot auctions, showing measurable pricing improvements on selected assets and validating its AI-driven auction strategy, legal risk analysis, and price assessment capabilities. Pre-production pilots with institutional clients (Months 15-24) will further validate scalability and real-world applications. The roadmap targets additional security and privacy certifications before TRL 8. Automated validation, peer reviews, and data integrity protocols reinforce the reliability of results. The detailed PoC objectives, measurable outcomes, and real-world application confirm that the testing and validation pathways are highly credible, ensuring a strong foundation for further technological progression.
4. Intellectual Property Strategy
The soundness of the arguments proving the proposal’s intellectual property strategy is average.
The applicant company retains full ownership of all intellectual asset rights, including copyright and commercial exploitation rights related to the source code. Protection of proprietary information is ensured through trade secrecy protocols, non-disclosure agreements, and trade secret protection agreements. While the company has not yet filed for patents, it is actively exploring patenting opportunities, though it notes that these efforts are constrained by the provisions of the European Patent Convention. A Freedom to Operate (FTO) opinion ... indicating that there are no identified risks of IP infringement at this stage has been provided.
The soundness of the arguments proving the proposal’s intellectual property strategy is very good
Nexora Labs owns the copyright and commercial exploitation rights to the AssetPulse source code. The company has defined its intellectual asset strategy to ensure a competitive advantage in the market. The aim of protecting the source code is to ensure long-term control and freedom to operate as the company seeks global growth. Additionally, the team has explored patents for key technical innovations with external IP counsel. The company's core expertise lies in the software codebase and the technical and strategic knowledge of the team. The operating model requires versioned filing of all developed code. Each software developer working for the company follows this model, and compliance is reviewed systematically.
The soundness of the arguments proving the proposal’s intellectual property strategy is very good.
Nexora Labs has a strong IP strategy, protecting its innovations through copyrights, trade secrets, and trademarks, while acknowledging that software and AI methods are not easily patentable under the European Patent Convention (EPC). The company fully owns AssetPulse's source code and has trademarked its brand and logo. A Freedom to Operate (FTO) analysis ... confirmed no infringement risks. An external legal opinion supports this, stating that AssetPulse does not currently present a clearly patentable solution under EPC and does not infringe on third-party patents. Employees and contractors are bound by NDAs and Trade Secret Protection Agreements, reinforcing security. Nexora Labs has also completed design and trademark registrations in relevant jurisdictions. Additionally, the company is exploring patents for innovations like its AI-powered legal risk engine and predictive valuation models. If they demonstrate an "additional technical effect," patent applications will be pursued. Overall, the IP strategy is very strong, ensuring legal protection while proactively mitigating risks.
Criterion 2 - Impact
5. Customer Demand
The soundness of the arguments proving the proposal’s competitiveness and added value is poor.
The arguments presented in the proposal—particularly concerning the appetite of institutional investors for small, distressed assets dispersed across less attractive locations and multiple jurisdictions—are not convincing. It remains unclear whether this type of asset aligns with the investment strategies of institutional players, who typically seek scale, lower risk, and streamlined operations. Similarly, the proposal does not provide compelling evidence of demand from individual investors willing to engage in cross-border investments in high-risk, legally complex industrial asset. At this stage, there is no clear demonstration of market traction or willingness to pay, which weakens the commercial validation of the concept.
The soundness of the arguments proving the proposal’s competitiveness and added value is very good
AssetPulse's real-time and comprehensive AI-powered platform addresses a challenge identified in the market. It enables international investors to access and view distressed industrial asset at scale. The solution brings much-needed added value to the market and increases potential customers' interest in the developed solution.The applicant has also clearly justified the advantages and added value of the developed solution compared to the competitors.
The soundness of the arguments proving the proposal's competitiveness and added value is very good.
AssetPulse-AI offers significant added value, with a strong competitive edge, addressing critical challenges in the distressed industrial asset auction market, such as data fragmentation, legal complexity, and market opacity. Its innovative features include real-time data aggregation, AI-driven risk assessments, cross-border capabilities, and multimodal search, which set it apart from existing solutions. The platform promises quantifiable benefits, such as reducing transaction time by 98% and increasing auction success rates by 25%, and improving recovery rates for banks by 20-30%, while reducing legal and transaction costs by up to 50%. The company has gained initial traction, with an active early user base and multiple non-binding Letters of Intent from industry participants, reflecting strong market interest. Its clear value proposition addresses the unmet needs of investors, banks, legal professionals, and government agencies by centralizing real-time data and simplifying complex processes. Unlike traditional platforms, it integrates legal, financial, and multimedia data with real-time risk assessments. The company's comprehensive go-to-market strategy, which includes digital marketing, strategic partnerships, and targeted sales efforts, is designed to engage potential customers effectively. AssetPulse-AI growth projections further highlight its potential to generate significant demand. The solution is positioned well to attract international investors and expand its customer base.
6. Market development
The credibility of the market development plan and targeted innovation area description are poor.
The applicant has identified the Total Addressable Market (TAM), Serviceable Available Market (SAM), and Serviceable Obtainable Market (SOM), and has provided a general overview of market conditions. However, the primary driver cited for market growth—namely, the increase in non-performing loans (NPLs)—is inherently cyclical and may not support sustained long-term expansion. The target of capturing 5–10% of the distressed asset auction market within three years appears overly ambitious, especially given the fragmented nature of this market and the significant challenges in attracting both supply- and demand-side users. Furthermore, the projection of 300,000 monthly active users ... seems highly unrealistic and inconsistent with the stated objective of participating in 1,000 auctions, suggesting a disconnect between user engagement assumptions and actual transaction volume. As a result, the credibility of the accompanying financial projections is undermined.
The credibility of the market development plan and targeted innovation area description are very good.
Despite current market pressure, the applicant company has strong potential to develop new markets and improve access for new customer segments. According to independent market analysis, the industrial asset sector is valued in the trillion-euro range. The market needs solutions to persistent challenges: 1) data fragmentation, 2) legal complexity, and 3) limited transparency in legal auctions. The applicant company can transform this market by shortening transaction times, increasing participation rates, and delivering real-time multi-source information to users. The innovation may also broaden access by enabling smaller investors to participate in auctions. The applicant company has set ambitious growth goals ... aiming for large monthly active user volumes and mid-single to low-double-digit market share. The innovation's differentiated positioning and commercialization strategy provide credible conditions for pursuing these goals.
The credibility of the market development plan and targeted innovation area description are very good.
The innovation has significant potential to transform the distressed asset auction market by democratizing access, facilitating cross-border investment, and introducing features such as co-investment workflows. It simplifies the process, making it more inclusive and accessible to smaller investors, while overcoming legal and linguistic barriers across jurisdictions. The digital transformation of auctions enhances speed, transparency, and efficiency, using AI and machine learning for risk assessments and asset valuation, which can stimulate further innovation in the competitive landscape. The market for distressed assets is estimated in the low-trillion-euro range globally, with the regional judicial auction segment in the high-hundreds-of-billions range. The plan assumes high-single-digit growth rates over the next five years. AssetPulse's TAM, SAM, and SOM estimates indicate a large addressable opportunity, with target share assumptions in the low-double-digit range over a multi-year horizon. The goal to capture meaningful share through phased market entry is supported by competitive advantages such as AI-driven risk assessment and cross-border capabilities. There is strong willingness to pay for the platform's value, driven by cost savings, risk reduction, and efficiency. Overall, AssetPulse's market development plan is well-structured, with a clear understanding of market potential and growth opportunities, making the plan highly credible.
7. Commercialisation strategy
The soundness of the arguments proving the proposal’s commercialisation strategy is poor.
The applicant has identified key strategic partners, including financial institutions, law firms, and auction organizers, with a few already participating in the programme. However, the proposal lacks partnerships with established institutional investors in the industrial asset sector—an important gap given the product’s positioning and target market. Furthermore, commercial traction has yet to be demonstrated, as willingness to pay remains theoretical and no paying customers have been secured to date. The proposed revenue model combines subscription fees for access to the platform’s services, transaction fees on successful auctions, and additional fees for ancillary services such as legal support and asset inspections. Legal and regulatory considerations have been addressed, with a focus on compliance with GDPR and AML requirements.
The soundness of the arguments proving the proposal’s commercialisation strategy is very good.
The commercialization approach is phased and internally consistent, combining pilot onboarding, partner channels, and targeted acquisition. The plan shows a practical sequence for converting pilots into paid deployment. One gap is insufficient detail on unit economics by customer segment and limited evidence for post-pilot retention assumptions. A stronger country-by-country compliance and pricing matrix would improve confidence.
The soundness of the arguments proving the proposal's commercialisation strategy is very good.
The project has a comprehensive strategy, including a clear business model, phased market plan, regulatory compliance, and strong partnerships, positioning AssetPulse-AI for successful commercialization. AssetPulse operates a B2B subscription model with dynamic pricing for micro-products and data services, targeting asset funds, legal operators, specialist investors, and selected public-sector stakeholders. Subscription plans span entry-level to enterprise tiers, with additional revenue from transaction-linked fees and premium analytical services. The phased market entry begins in selected European jurisdictions, followed by expansion to additional regions through partnerships with auction operators and financial institutions. The platform aims to progress through TRL milestones with large-scale testing and legal certifications. The project prioritizes GDPR compliance, KYC controls, and ISO-aligned security/privacy practices. AssetPulse-AI has established key partnerships with legal partners, financial institutions, auction operators, and major cloud/data infrastructure providers. It has obtained Letters of Intent (LOIs) from large funds, asset agencies, and investors, and collaborates with research groups for continued platform development.
8. Scale up potential
The soundness of the arguments proving the scale up potential is average.
From a technological perspective, the platform appears to be designed for scalability, supported by a robust structure and the use of cloud-based infrastructure. However, from a business development standpoint, the primary risks lie in market acceptance and the associated high customer acquisition costs, which may ultimately outweigh the perceived benefits of the platform. Furthermore, the platform’s ability to effectively navigate the complexities of distressed asset transactions and deliver meaningful value to users has yet to be demonstrated under full-scale market conditions. The applicant has submitted several non-binding letters of intent from venture capital funds, indicating potential interest in supporting the project at a more advanced stage of development.
The soundness of the arguments proving the scale up potential is very good.
Scale-up assumptions are credible for an initial regional rollout and the feature scope is broader than many incumbents. The roadmap links product maturity to expansion milestones and partner capacity. The weak point is aggressive market-share timing without enough operational proof. Add explicit gates for support capacity, data onboarding speed, and sales-cycle length.
The soundness of the arguments proving the scaleup potential is very good.
The innovation shows strong scalability potential, driven by its cloud-native architecture on major commercial cloud infrastructure, enabling real-time data processing, large-scale machine learning, and secure data storage. The platform plans staged expansion across selected European distressed asset markets, followed by broader regional rollout. Diversification into adjacent auction categories will broaden the user base and revenue streams. AssetPulse-AI's subscription and transaction fee model supports recurring revenue growth, while its network effects will strengthen through partnerships with financial institutions and asset-focused intermediaries. Proprietary AI, NLP, and machine learning models differentiate the platform by automating processes, providing real-time insights, and offering predictive capabilities. To support commercialization and scale-up, AssetPulse-AI is negotiating a seed funding round; public co-funding is expected to complement private investment, accelerating platform development and market expansion. The business model is designed to generate revenue through subscriptions and transaction fees, with profitability projected after the scale-up phase. The company is also enhancing partnerships with stakeholders and strengthening its team with expertise in AI, machine learning, cloud computing, and industrial asset workflows. Technological infrastructure, including multi-cloud capacity and high-performance compute resources, will support growth and large-scale data processing. In conclusion, AssetPulse's technology base, expansion strategy, and scalable business model position it well for success, with active efforts to secure resources for growth through both private and public funding.
8.1 Grant Only support applicability
9. Broader impact
The soundness and relevance of metrics supporting the innovation’s potential societal/economic/environmental/climate impact is poor.
The proposal describes the positive societal impact of the product as facilitating greater access to industrial asset by enabling more assets to enter the market, thereby contributing to housing affordability for younger families and first-time buyers. However, this claim is unconvincing given the limited scale of operations—targeting approximately 1,000 judicial auctions annually starting from 2030—across several major European markets. A similar concern applies to the claimed environmental benefits of reactivating existing assets instead of promoting new developments. While conceptually appealing, the proposal does not quantify the potential environmental impact or provide supporting data. The narrative around the "democratisation" of access to judicial auctions for young families and middle-income buyers also lacks credibility, as these assets are often legally complex and may not be suitable for risk-averse or inexperienced buyers. Overall, aside from a general reference to employment effects, the proposal does not provide concrete metrics or measurable targets to substantiate its impact claims.
The soundness and relevance of metrics supporting the innovation’s potential societal/economic/environmental/climate impact is very good.
From the application states clearly how the successful commercialization of innovation creates positive economic impacts and what kind of benefits it creats to the potential interest groups . Instead, it would be good to describe the environmental impacts of the innovation more broadly. The application mentions the reduction in travel and influence the resulting of emissions. In the application the applicant has defined clearly both the quantitative benefits and the key KPI-indicators. From a technology perspective, the applicant company has defined key KPIs as short response time, increased transaction success rates, improved recovery rates for banks and reduction in legal and transaction costs.The applicant company justifies the importance of the innovation by, among other things, saying that the number of participants in legal auctions will increase 30 %, the time that takes to complete transaction will reduce by 50%, a reduction in the time what takes to the entire process by up to 75-90% and the banks' rate of return will improve by 20-30 %. In addition, the platform will enable small investors in particular to access distressed industrial asset investments. At this time the international investors hold 30% of all industrial asset investments in Europe. Of cource the company has mentioned other metrics as company's market share, turnover and amount of staff.
The soundness and relevance of metrics supporting the innovation's potential societal/economic/environmental/climate impact is very good.
The innovation has substantial potential for societal, economic, environmental, and climatic impacts if commercialized successfully. It democratizes access to industrial asset auctions, especially for smaller investors, and can help reactivate underutilized assets. It increases transparency in judicial and public auctions, reducing fraud risk and improving cross-border participation efficiency. AssetPulse-AI can materially improve process speed and recovery outcomes through AI-supported screening and valuation workflows. The platform also reduces legal transaction costs and can increase market participation. It is expected to create jobs in industrial asset, legal services, and asset management. Additionally, by revitalizing vacant or deteriorating assets, AssetPulse can reduce pressure for new construction and lower related environmental impacts. The platform's success will be measured by KPIs such as auction success rates, recovery rates, and reduced transaction times and costs. A more detailed methodology is still needed for fully quantified impact evaluation across societal, economic, and environmental dimensions.
Criterion 3 - Level of risk, implementation, and need for Union support
10. Team
The capacity and role of each team member, the measures in place to ensure motivation and the extent to which the proposed team, as a whole, brings together the necessary knowledge and expertise are average.
The team is sizable and demonstrates strong technological expertise, which is clearly aligned with the product’s development goals. However, it is notable that none of the key founders or senior managers possess significant experience in the industrial asset sector, either as sellers or investors. This represents a potential risk, as it may hinder the company’s ability to effectively understand and address client needs—particularly when engaging with institutional investors operating in complex international markets. While some team members do have industrial asset experience, none appear to have worked at the scale or depth required to fully grasp institutional processes and priorities. Interestingly, the applicant does not explicitly recognize this as a gap, aside from plans to recruit legal and compliance specialists with industrial asset expertise. Additionally, the team currently shows a significant gender imbalance, particularly at the leadership level. However, the applicant acknowledges this issue and has set a target of achieving 55% female participation by 2026—a positive commitment, though its feasibility will depend on concrete actions and recruitment practices moving forward.
The capacity and role of each team member, the measures in place to ensure motivation and the extent to which the proposed team, as a whole, brings together the necessary knowledge and expertise are very good.
Although the core team is relatively small, they have the required level of skills and expertise in artificial intelligence, industrial asset and legal frameworks. These create a good starting point for bringing innovation to market. A broad and highly skilled advisory team has been assembled to complement the core team. The core team has also identified the needed additional resources and is committed to recruiting the necessary resources. To further strengthen their team, they are goint to actively recruiting legal and regulatory experts, regional sales- as well as international Sales and business development managers and AI & machine learning engineers. The company is also committed to increasing diversity in their leadership and ensuring a balanced representation of men and women in line with EU diversity initiatives.
The capacity and role of each team member, the measures in place to ensure motivation and the extent to which the proposed team, as a whole, brings together the necessary knowledge and expertise are very good.
The Nexora Labs team has strong capability and motivation to implement their innovation proposal and bring it to market. Their leadership combines expertise in AI, industrial asset workflows, and legal structures, with two founders (CEO and COO) who have entrepreneurial experience in cloud systems and project execution. The CTO and CSO bring deep technical and research expertise, while other team members add skills in finance, product design, data engineering, and law. The company's advisory board includes experts in venture capital, strategy, and asset investments, and letters of intent from potential partners show confidence in the team's potential. An Employee Stock Option Plan (ESOP) is in place to align employee interests with the company's success, and the work culture emphasizes innovation, collaboration, and work-life balance. Nexora Labs is committed to diversity and gender balance, with a GEP in place and aims for at least 55% female participation by 2026.
11. Risk level of the investment
The soundness of the arguments proving the risk level of investment is average.
The applicant argues that EIC participation can de-risk execution and accelerate validation milestones. The funding plan references active discussions with private investors and a blended financing path. However, the financing schedule depends on non-binding interest and cash runway assumptions are not stress-tested under slower sales conversion. A downside scenario with revised burn and hiring triggers is needed.
The soundness of the arguments proving the risk level of investment is very good.
The EIC Fund's investment is significant for the company's future. The company has described the growth potential and justified the need for financing. Despite the growth potential, the company must overcome the identified risks that they have presented in the application. Identified risks includes: 1) Financial risk - possible problems with cash flow, 2) Risks with GDBR - Changing data of privacy regulations, 3) Risks with commercialization - Resistance from traditional industrial asset investors and legal professionals to adopting AI-drivensolutions for auctions and legal assessments, 4) Risks on the different markets - economic situation in the market, 5) Competition - Competitor action, 6) Risks with techonolgy - potential disputes arising from artificial intelligence and delays & difficulties in combining data 7) Risks with cybersecurity - Reputational damage resulting from a data breach or security breach, 8) Risks with the team - Loss of key team members and problems regarding personnel management and 9) Societal challenges - challenges in practice due to cultural differences. All the above risks affect the willingness of potential investors to invest in the company alone. All six potential investors are interested in the developed innovation, but they are waiting for a financing decision before they are ready to continue discussions. Based on the perspectives that have presented in the application, the compeny hasn´t any possibility of obtaining the necessary funding without an EIC support during the next coming two years.
The soundness of the arguments proving the risk level of investment is very good.
Nexora Labs demonstrates that the level of investment risk in its AssetPulse platform justifies EIC Fund support. Despite initial private investment, the company faces challenges in securing sufficient capital for full development and commercialization. A €2.5M seed round is underway but is insufficient for European expansion and AI development. The fragmented distressed asset market requires significant resources for AI models, legal compliance, and system integration. Unlike typical PropTech startups, AssetPulse needs substantial R&D investment before generating revenue at scale. EIC support bridges the gap between early-stage development and market entry, enhancing the ability to attract private investment, serving as a quality seal and de-risking the opportunity. The company is in active talks with investors and plans a future financing strategy, including equity rounds, public funding, and strategic partnerships. EIC funding is essential to address early-stage financial gaps, accelerate market entry, and boost investor confidence, ensuring Nexora Labs can secure the remaining funding within two years.
12. Risk mitigation
The soundness of the risk mitigation strategy is poor.
The risk register covers multiple categories, but technology quality risk is still under-specified, especially around model drift, false positives, and legal-data ambiguity. Mitigation actions are often high-level rather than operationally measurable. The plan should define owner-level controls, trigger thresholds, and response SLAs, plus pre-defined rollback criteria for production incidents.
The soundness of the risk mitigation strategy is very good
The team identifies core delivery risks and provides a structured mitigation framework with named workstreams. Preventive controls are mostly sensible and aligned with the operating model. To strengthen execution, add clear trigger thresholds and time-bound contingency actions for the highest-impact risks.
The soundness of the risk mitigation strategy is very good.
Nexora Labs identifies key technological, market, financial, and regulatory risks with clear mitigation strategies. Financial risks, such as delayed revenue from long customer acquisition cycles, are managed through long-term contracts, cash reserves, and diversified revenue streams. Regulatory risks, like GDPR changes, are addressed via legal monitoring, a regulatory advisory board, encryption, and compliance measures. Market risks include slow adoption by investors, banks, and legal professionals, mitigated by targeting high-volume distressed asset markets and providing educational resources. Competition from AI-driven models with lower pricing is countered through continuous innovation, exclusive features, and long-term partnerships. Concerns over AI legal risk assessments are addressed via expert validation and insured risk assessment reports. Technological risks, including inaccurate AI risk assessments and delays in integrating legal and auction data, are mitigated through AI improvement, expert oversight, local partnerships, and integration teams. Cybersecurity threats, such as data breaches harming client trust, are managed with security protocols, multi-factor authentication, and response plans. Team-related risks involve the loss of key AI and data personnel, mitigated through employee stock options (ESOP), talent pipelines, and knowledge-sharing systems. Growth challenges in a distributed workforce are handled with remote work policies, leadership training, and collaboration tools. Societal risks, including cultural and legal differences, are managed through localization and partnerships with industry and legal experts. The implementation plan has very good credibility. Risks are well-identified, categorized, and proactively mitigated. Nexora Labs’s focus on regulatory compliance, technological advancement, and market adaptability strengthens its robustness.
13. Implementation plan
The soundness and credibility of the implementation plan are poor.
The proposal presents a structured implementation plan across eight work packages with generally coherent sequencing. Concern remains around late-stage scope expansion beyond core validation objectives, which could dilute focus before commercialization readiness. Budget assumptions for several roles also appear above market without sufficient justification. The plan should tighten WP8 to validation-critical outcomes, clarify blended-finance consistency, and recalibrate staffing rates against benchmarked ranges.
The soundness and credibility of the implementation plan are very good.
The company has a clear and credible implementation plan, where they have clearly defined the goals and efforts for each work packages as well as the resbonsible person. All eight work packages (WP1: Management, WP2: Market Entry Preparation, WP3:Fine-Tuning of Pre-trained Language Models, WP4:Multimodal Chatbot Search for Property Retrieval and Matching, WP5:Property Data Intelligence Based on Media Input, WP 6: Preparation for TRL 9+ Management, WP7: Market Entry and Scale Up and WP8: Nexora Labs Further Development) which are written in the application aim to remove obstacles to commercialization and enable business scaling . The sequence of work packages is logical and they are consistent with each other.
The soundness and credibility of the implementation plan are very good.
The proposal shows that the implementation plan is well-structured, with clear milestones, work packages (WPs), and deliverables, supported by realistic resource allocation and timelines, being built around eight WPs. WP1 focuses on project management and innovation while advancing TRL 7 to 8. WP2 prepares for market entry through stakeholder engagement and pilots. WP3 refines AI language models for industrial asset market analysis. WP4 develops a multimodal chatbot for asset retrieval and matching. WP5 enhances asset data intelligence using media inputs like video and floor plans. WP6 manages the commercialization phase after reaching TRL 9. WP7 handles market entry and AssetPulse platform scaling, while WP8 supports ongoing development and expansion. Each WP has defined objectives, a lead participant, duration, and allocated person-months (PM). For example, WP3, led by Nexora Labs, spans 22 months (M1-M22) with 53 PM allocated to advancing AI models for market analysis. Detailed resource tables (Tables 9.1a, 9.1aa, and 9.1b) outline WP types, objectives, leaders, PM allocations, and budget details for grant and investment components. Deliverables, such as the Project Management Plan (D1.1), Innovation Roadmap (D1.2), and Data Management Plan (DMP) (D1.3), are listed in Table 3.1c, specifying WP links, leaders, dissemination levels, and delivery dates. Milestones in Table 3.1d indicate completion dates, verification methods, and WP objectives. For instance, Milestone 1 marks the completion of a reinforced AI model (WP3) by month 22, verified through industrial asset dataset testing and user feedback. Gantt charts visually map innovation (TRL 7-8) and market (TRL 9+) activities, showing task interdependencies. Task descriptions within WPs specify leaders, PM efforts, and costs, reinforcing the feasibility of resources and timelines.