Draft — EIC pre-accelerator

MOTIVATION LETTER

The CCT model, a tripartite pharmacological framework for reward-memory encoding prevention in addiction, is a deep-tech innovation with five pre-registered hypotheses confirmed through Bayesian MCMC calibration of 14 free parameters against a 1,847-record literature screen. The model demonstrates posterior super-additivity of 13 to 22 percentage points across versions, representing a validated scientific foundation for a drug-development tool targeting a global societal challenge. The EIC pre-accelerator's mandate to increase the innovation potential of deep-tech startups in widening countries aligns with the CCT model's trajectory from validated computational research to a commercial platform for addiction therapeutics.

My multidisciplinary profile as a pharmacist, computational modeler, and software engineer is the asset that moves this model from preprint to product. Three sole-authored preprints are under review at peer-reviewed journals including International Addiction Research and Treatment, Progress in Neuro-Psychopharmacology and Biological Psychiatry, and Neuroscience and Biobehavioral Reviews. A co-authored paper is under review at Alcohol, Elsevier. The scientific endorsements from Kent Berridge at Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU confirm the model's theoretical grounding in reinforcement learning and affective neuroscience.

The commercial pathway is a digital health platform that predicts individual vulnerability to addiction relapse and guides pharmacotherapeutic selection. The technical infrastructure is already built: a receptor-to-behavior simulation engine called neurocascade couples pharmacokinetics to receptor binding to Wilson-Cowan circuit dynamics, with 62 of 62 tests passing. The engine is calibrated for mu-opioid, D2 dopamine, and GABA-A receptor systems. This is a working computational pipeline with a validated core, not a concept.

The EIC pre-accelerator's focus on deep tech and societal impact matches the CCT model's dual value proposition: a scientific tool for pharmaceutical R&D and a clinical decision-support system for addiction medicine. The programme's coaching and network access would accelerate the transition from research code to a regulated medical device or software-as-a-medical-technology product. My enrollment in the M.Sc. Digital Health program at Hasso Plattner Institute and University of Potsdam, beginning Winter Semester 2026/27, provides the European academic anchor for this venture.

The widening countries dimension of the EIC pre-accelerator is a strategic fit. I am a Nigerian national, and the programme's emphasis on increasing innovation potential in regions with emerging deep-tech ecosystems mirrors the opportunity to build addiction-treatment infrastructure in African markets where opioid and stimulant use disorders are under-served. The CCT model's computational nature means it can be deployed anywhere with a cloud connection, making it a scalable solution for both European and African healthcare systems.

The CCT model is ready for the EIC pre-accelerator's validation, business-model development, and investor-readiness support. The science is published or under review, the code is tested, and the societal need is documented. What remains is the translation layer: market analysis, regulatory strategy, and founder development. That is precisely what this programme provides.

RESEARCH STATEMENT

The Conjunctive Consolidation Threshold model addresses a specific failure in addiction pharmacology: existing treatments target receptor occupancy but do not prevent the encoding of reward-memory associations that drive relapse. The model formalizes a tripartite mechanism across three coupled axes: dopaminergic reward prediction error, NMDAR-dependent long-term potentiation, and affective contrast. These are implemented as a system of ordinary differential equations solved with RK45 integration. The model was calibrated using Bayesian MCMC with the PyMC DEMetropolisZ sampler, fitting 14 free parameters against priors elicited from a systematic screen of 1,847 published records.

All five pre-registered hypotheses, H1 through H5, were confirmed. The posterior analysis shows super-additivity of 13 to 22 percentage points across model versions, meaning the combined effect of the three axes exceeds the sum of individual contributions. This is the core scientific claim: preventing reward-memory encoding requires simultaneous modulation of all three axes, not a single-target intervention. The model is documented in three sole-authored preprints hosted on OSF and Zenodo, each currently under review at a peer-reviewed journal.

The translational value is a computational screen for drug combinations that hit the conjunctive threshold. A pharmaceutical company or academic lab can input candidate compounds, and the model predicts whether the combination achieves the threshold for blocking reward-memory consolidation without abolishing normal learning. This is a testable, falsifiable prediction engine, not a descriptive framework.

The broader research program extends this dynamical-systems approach across adjacent problems. The TOPOLOGIX project uses ESM-2 protein-language-model delta-embeddings combined with Morgan and ECFP drug fingerprints in a Random Forest classifier to predict drug-resistance mutations from sequence alone. On the Platinum benchmark of 553 mutations, TOPOLOGIX achieves an AUROC of 0.804 with a standard deviation of 0.025, and 0.634 on SKEMPI 2.0. This beats structure-based baselines such as mCSM-lig at approximately 0.70 while covering 100 percent of mutations compared to roughly 18 percent for structure-limited tools. The implication is that sequence-based representations carry resistance signal that interface geometry does not.

Two pre-registered negative results strengthen the program's credibility. A cardiotoxicity topology study tested whether bipartite persistent homology predicts hERG cardiotoxicity from protein-ligand interface geometry. The pre-registered, powered replication found topological features do not beat a plain descriptor baseline, AUROC 0.8426 versus 0.8782, settling a comparison the published literature had never actually run. An interface-topology-for-resistance study found the same class of topological constructs carry almost no signal for drug-resistance prediction, AUROC 0.425 and 0.485 on the Platinum benchmark. These results rule out interface geometry as the driver and motivated the sequence-representation approach now used in TOPOLOGIX.

The ergofluids project extends Koopman-operator and Dynamic Mode Decomposition methods with a Mori-Zwanzig memory kernel to model macromolecular drug-vehicle transport through dense, non-Newtonian tumor tissue. The pre-registered, gated validation pipeline passed synthetic-data gates, but the first real-data gate against digitized published figures did not meet its primary pre-registered criterion. That result is reported directly rather than reframed. This is methods-validation research, not a venture, and the negative result is a contribution to the literature on the limits of data-driven dynamical models in biological transport.

The research program is unified by a methodological commitment: pre-registration, Bayesian calibration with explicit priors, and honest reporting of negative results. The EIC pre-accelerator's emphasis on deep-tech innovation with scientific merit aligns with this program's track record of falsifiable, quantitative claims. The CCT model is the flagship venture candidate, and the surrounding projects demonstrate the methodological rigor that de-risks the science.

APPLICATION ESSAY: INNOVATION AND MARKET POTENTIAL

The CCT model is a computational engine that predicts whether a drug combination prevents reward-memory encoding in addiction. The scientific validation is complete: five pre-registered hypotheses confirmed, Bayesian-calibrated with 14 free parameters against a 1,847-record literature screen, and posterior super-additivity of 13 to 22 percentage points. The innovation is the conjunctive threshold itself: the model specifies the minimum simultaneous modulation of dopaminergic reward prediction error, NMDAR-dependent long-term potentiation, and affective contrast required to block consolidation. No existing tool in addiction pharmacology provides this predictive capability.

The market is defined by two segments. First, pharmaceutical R&D: companies developing addiction therapeutics need a computational screen to prioritize combination candidates before animal trials. The global addiction treatment market is valued in the tens of billions of dollars, and the failure rate for CNS drugs in clinical trials is among the highest in the industry. A predictive model that reduces late-stage failures has direct economic value. Second, clinical decision support: the model can be deployed as a digital health platform that guides pharmacotherapeutic selection for individual patients based on their receptor and circuit profiles. This aligns with the EIC's focus on societal challenges in health.

The competitive landscape is sparse. Existing computational tools in addiction neuroscience are descriptive, not predictive. The CCT model's combination of dynamical-systems modeling, Bayesian calibration, and pre-registered validation is a defensible technical moat. The codebase is already built: the neurocascade engine couples pharmacokinetics to receptor binding to Wilson-Cowan circuit dynamics with 62 of 62 tests passing, and the CCT model itself is implemented as a documented ODE system with RK45 integration.

The business model is a software-as-a-service platform for pharmaceutical R&D teams, with a pathway to a regulated clinical decision-support tool. The EIC pre-accelerator's coaching on business model development and investor readiness is the specific support needed to translate the validated science into a commercial offering. The widening countries dimension is relevant: the platform can be deployed in African markets where addiction treatment infrastructure is limited, creating a social-impact angle that aligns with EIC priorities.

APPLICATION ESSAY: TEAM AND FOUNDER READINESS

The venture is solo-founded, and the founder profile is the team. I hold a B.Pharm from the University of Ibadan with a German equivalent grade of 1.9, am a PCN-licensed pharmacist, and am enrolled in the M.Sc. Digital Health program at Hasso Plattner Institute and University of Potsdam beginning Winter Semester 2026/27. The multidisciplinary training spans pharmacy, computational modeling, and software engineering, which means the scientific and technical execution of the CCT model is entirely in-house.

The technical skill set is production-grade. Python with scipy, numpy, ODE and RK45 integration, PyMC for MCMC calibration, pandas for data handling, R for statistical analysis, TDA tools including Ripser and Gudhi, NEURON and Brian2 for neural simulation, AlphaFold and RDKit for protein and drug representation, GROMACS and AutoDock for molecular dynamics and docking, and Nextflow and SLURM for HPC workflows. The applied infrastructure includes four independent DuckDB-based ingest-to-analyze pipelines across life-sciences, tech and AI, and social-science domains, self-hosted local LLM serving with llama.cpp, and production systems operations including Linux VPS, systemd, Caddy TLS, CI/CD, and automated backup and disaster recovery.

The scientific track record includes three sole-authored preprints under review at peer-reviewed journals, a co-authored paper under review at Alcohol, Elsevier, and endorsements from Kent Berridge at Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU. The pre-registration discipline across the CCT model, the hERG cardiotoxicity study, and the ergofluids project demonstrates a commitment to falsifiable science.

The gap the EIC pre-accelerator fills is business development. The science is done, the code is tested, but the market analysis, regulatory strategy, and investor pitch are not yet built. The programme's coaching and network access would provide exactly that. The founder is ready to execute on the technical side and open to structured support on the commercial side.

CHECKLIST

- [ ] Verify EIC pre-accelerator eligibility for a Nigerian national not registered as an EU startup; confirm whether a partner entity in a widening country is required
- [ ] Confirm the programme's definition of deep-tech startup and whether the CCT model qualifies as a product or requires a spin-off entity
- [ ] Prepare a one-page executive summary of the CCT model with the five confirmed hypotheses and the 13 to 22 percentage point super-additivity range
- [ ] Compile the three preprint links from OSF and Zenodo, plus the co-authored Alcohol paper status, as evidence of scientific validation
- [ ] Draft a business model canvas for the CCT model as a software-as-a-service platform for pharmaceutical R&D
- [ ] Prepare a technical architecture document describing the ODE system, RK45 integration, PyMC calibration, and the neurocascade engine
- [ ] Confirm the M.Sc. Digital Health enrollment at Hasso Plattner Institute and University of Potsdam for Winter Semester 2026/27 and include the acceptance letter
- [ ] Gather endorsement letters or statements from Kent Berridge, Samuel Gershman, Nathaniel Daw, and Marcelo Mattar
- [ ] Prepare a budget outline for the 10,000 to 100,000 dollar range, specifying use of funds for regulatory consultation, market analysis, and cloud infrastructure
- [ ] Confirm whether the programme requires a demo or prototype; if so, prepare a video walkthrough of the CCT model running on a test dataset

EDITOR NOTES

- Eligibility risk is the primary flag: the EIC pre-accelerator targets startups registered in EU Member States or Horizon Europe associated countries, specifically widening countries. Eniola is a Nigerian national and the venture has no EU registration. The application must either identify a partner entity in a widening country or confirm the programme's international dimension. This needs verification before submission.
- The widening countries list must be checked: Germany, where Eniola will be enrolled at HPI and University of Potsdam, is not a widening country. Portugal, Poland, and Greece are examples of widening countries. If a partner is needed, the partner must be in a widening country, not just any EU state.
- The programme type is an accelerator, not a grant. The application should emphasize the venture's readiness to benefit from coaching and investor-readiness support, not just the scientific merit. The current draft leans heavily on research validation; the commercial readiness section may need strengthening.
- The CCT model's status as a product versus a research output needs clarification. The application should state explicitly whether the venture is a spin-off company, a licensing opportunity, or a platform that Eniola will operate independently. The current draft implies a platform but does not specify the legal entity.
- The budget range of 10,000 to 100,000 dollars is wide. The application should include a specific budget breakdown, even if provisional, to demonstrate financial planning. The current draft does not include a budget section, which may be a required field on the submission page.

MOTIVATION LETTER

The CCT model, a tripartite pharmacological framework for reward-memory encoding prevention in addiction, is a deep-tech innovation with five pre-registered hypotheses confirmed through Bayesian MCMC calibration of 14 free parameters against a 1,847-record literature screen. The model demonstrates posterior super-additivity of 13 to 22 percentage points across versions, representing a validated scientific foundation for a drug-development tool targeting a global societal challenge. The EIC pre-accelerator's mandate to increase the innovation potential of deep-tech startups in widening countries aligns with the CCT model's trajectory from validated computational research to a commercial platform for addiction therapeutics.

My multidisciplinary profile as a pharmacist, computational modeler, and software engineer is the asset that moves this model from preprint to product. Three sole-authored preprints are under review at peer-reviewed journals including International Addiction Research and Treatment, Progress in Neuro-Psychopharmacology and Biological Psychiatry, and Neuroscience and Biobehavioral Reviews. A co-authored paper is under review at Alcohol, Elsevier. The scientific endorsements from Kent Berridge at Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU confirm the model's theoretical grounding in reinforcement learning and affective neuroscience.

The commercial pathway is a digital health platform that predicts individual vulnerability to addiction relapse and guides pharmacotherapeutic selection. The technical infrastructure is already built: a receptor-to-behavior simulation engine called neurocascade couples pharmacokinetics to receptor binding to Wilson-Cowan circuit dynamics, with 62 of 62 tests passing. The engine is calibrated for mu-opioid, D2 dopamine, and GABA-A receptor systems. This is a working computational pipeline with a validated core, not a concept.

The EIC pre-accelerator's focus on deep tech and societal impact matches the CCT model's dual value proposition: a scientific tool for pharmaceutical R&D and a clinical decision-support system for addiction medicine. The programme's coaching and network access would accelerate the transition from research code to a regulated medical device or software-as-a-medical-technology product. My enrollment in the M.Sc. Digital Health program at Hasso Plattner Institute and University of Potsdam, beginning Winter Semester 2026/27, provides the European academic anchor for this venture.

The widening countries dimension of the EIC pre-accelerator is a strategic fit. I am a Nigerian national, and the programme's emphasis on increasing innovation potential in regions with emerging deep-tech ecosystems mirrors the opportunity to build addiction-treatment infrastructure in African markets where opioid and stimulant use disorders are under-served. The CCT model's computational nature means it can be deployed anywhere with a cloud connection, making it a scalable solution for both European and African healthcare systems.

The CCT model is ready for the EIC pre-accelerator's validation, business-model development, and investor-readiness support. The science is published or under review, the code is tested, and the societal need is documented. What remains is the translation layer: market analysis, regulatory strategy, and founder development. That is precisely what this programme provides.

RESEARCH STATEMENT

The Conjunctive Consolidation Threshold model addresses a specific failure in addiction pharmacology: existing treatments target receptor occupancy but do not prevent the encoding of reward-memory associations that drive relapse. The model formalizes a tripartite mechanism across three coupled axes: dopaminergic reward prediction error, NMDAR-dependent long-term potentiation, and affective contrast. These are implemented as a system of ordinary differential equations solved with RK45 integration. The model was calibrated using Bayesian MCMC with the PyMC DEMetropolisZ sampler, fitting 14 free parameters against priors elicited from a systematic screen of 1,847 published records.

All five pre-registered hypotheses, H1 through H5, were confirmed. The posterior analysis shows super-additivity of 13 to 22 percentage points across model versions, meaning the combined effect of the three axes exceeds the sum of individual contributions. This is the core scientific claim: preventing reward-memory encoding requires simultaneous modulation of all three axes, not a single-target intervention. The model is documented in three sole-authored preprints hosted on OSF and Zenodo, each currently under review at a peer-reviewed journal.

The translational value is a computational screen for drug combinations that hit the conjunctive threshold. A pharmaceutical company or academic lab can input candidate compounds, and the model predicts whether the combination achieves the threshold for blocking reward-memory consolidation without abolishing normal learning. This is a testable, falsifiable prediction engine, not a descriptive framework.

The broader research program extends this dynamical-systems approach across adjacent problems. The TOPOLOGIX project uses ESM-2 protein-language-model delta-embeddings combined with Morgan and ECFP drug fingerprints in a Random Forest classifier to predict drug-resistance mutations from sequence alone. On the Platinum benchmark of 553 mutations, TOPOLOGIX achieves an AUROC of 0.804 with a standard deviation of 0.025, and 0.634 on SKEMPI 2.0. This beats structure-based baselines such as mCSM-lig at approximately 0.70 while covering 100 percent of mutations compared to roughly 18 percent for structure-limited tools. The implication is that sequence-based representations carry resistance signal that interface geometry does not.

Two pre-registered negative results strengthen the program's credibility. A cardiotoxicity topology study tested whether bipartite persistent homology predicts hERG cardiotoxicity from protein-ligand interface geometry. The pre-registered, powered replication found topological features do not beat a plain descriptor baseline, AUROC 0.8426 versus 0.8782, settling a comparison the published literature had never actually run. An interface-topology-for-resistance study found the same class of topological constructs carry almost no signal for drug-resistance prediction, AUROC 0.425 and 0.485 on the Platinum benchmark. These results rule out interface geometry as the driver and motivated the sequence-representation approach now used in TOPOLOGIX.

The ergofluids project extends Koopman-operator and Dynamic Mode Decomposition methods with a Mori-Zwanzig memory kernel to model macromolecular drug-vehicle transport through dense, non-Newtonian tumor tissue. The pre-registered, gated validation pipeline passed synthetic-data gates, but the first real-data gate against digitized published figures did not meet its primary pre-registered criterion. That result is reported directly rather than reframed. This is methods-validation research, not a venture, and the negative result is a contribution to the literature on the limits of data-driven dynamical models in biological transport.

The research program is unified by a methodological commitment: pre-registration, Bayesian calibration with explicit priors, and honest reporting of negative results. The EIC pre-accelerator's emphasis on deep-tech innovation with scientific merit aligns with this program's track record of falsifiable, quantitative claims. The CCT model is the flagship venture candidate, and the surrounding projects demonstrate the methodological rigor that de-risks the science.

APPLICATION ESSAY: INNOVATION AND MARKET POTENTIAL

The CCT model is a computational engine that predicts whether a drug combination prevents reward-memory encoding in addiction. The scientific validation is complete: five pre-registered hypotheses confirmed, Bayesian-calibrated with 14 free parameters against a 1,847-record literature screen, and posterior super-additivity of 13 to 22 percentage points. The innovation is the conjunctive threshold itself: the model specifies the minimum simultaneous modulation of dopaminergic reward prediction error, NMDAR-dependent long-term potentiation, and affective contrast required to block consolidation. No existing tool in addiction pharmacology provides this predictive capability.

The market is defined by two segments. First, pharmaceutical R&D: companies developing addiction therapeutics need a computational screen to prioritize combination candidates before animal trials. The global addiction treatment market is valued in the tens of billions of dollars, and the failure rate for CNS drugs in clinical trials is among the highest in the industry. A predictive model that reduces late-stage failures has direct economic value. Second, clinical decision support: the model can be deployed as a digital health platform that guides pharmacotherapeutic selection for individual patients based on their receptor and circuit profiles. This aligns with the EIC's focus on societal challenges in health.

The competitive landscape is sparse. Existing computational tools in addiction neuroscience are descriptive, not predictive. The CCT model's combination of dynamical-systems modeling, Bayesian calibration, and pre-registered validation is a defensible technical moat. The codebase is already built: the neurocascade engine couples pharmacokinetics to receptor binding to Wilson-Cowan circuit dynamics with 62 of 62 tests passing, and the CCT model itself is implemented as a documented ODE system with RK45 integration.

The business model is a software-as-a-service platform for pharmaceutical R&D teams, with a pathway to a regulated clinical decision-support tool. The EIC pre-accelerator's coaching on business model development and investor readiness is the specific support needed to translate the validated science into a commercial offering. The widening countries dimension is relevant: the platform can be deployed in African markets where addiction treatment infrastructure is limited, creating a social-impact angle that aligns with EIC priorities.

APPLICATION ESSAY: TEAM AND FOUNDER READINESS

The venture is solo-founded, and the founder profile is the team. I hold a B.Pharm from the University of Ibadan with a German equivalent grade of 1.9, am a PCN-licensed pharmacist, and am enrolled in the M.Sc. Digital Health program at Hasso Plattner Institute and University of Potsdam beginning Winter Semester 2026/27. The multidisciplinary training spans pharmacy, computational modeling, and software engineering, which means the scientific and technical execution of the CCT model is entirely in-house.

The technical skill set is production-grade. Python with scipy, numpy, ODE and RK45 integration, PyMC for MCMC calibration, pandas for data handling, R for statistical analysis, TDA tools including Ripser and Gudhi, NEURON and Brian2 for neural simulation, AlphaFold and RDKit for protein and drug representation, GROMACS and AutoDock for molecular dynamics and docking, and Nextflow and SLURM for HPC workflows. The applied infrastructure includes four independent DuckDB-based ingest-to-analyze pipelines across life-sciences, tech and AI, and social-science domains, self-hosted local LLM serving with llama.cpp, and production systems operations including Linux VPS, systemd, Caddy TLS, CI/CD, and automated backup and disaster recovery.

The scientific track record includes three sole-authored preprints under review at peer-reviewed journals, a co-authored paper under review at Alcohol, Elsevier, and endorsements from Kent Berridge at Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU. The pre-registration discipline across the CCT model, the hERG cardiotoxicity study, and the ergofluids project demonstrates a commitment to falsifiable science.

The gap the EIC pre-accelerator fills is business development. The science is done, the code is tested, but the market analysis, regulatory strategy, and investor pitch are not yet built. The programme's coaching and network access would provide exactly that. The founder is ready to execute on the technical side and open to structured support on the commercial side.

CHECKLIST

- [ ] Verify EIC pre-accelerator eligibility for a Nigerian national not registered as an EU startup; confirm whether a partner entity in a widening country is required
- [ ] Confirm the programme's definition of deep-tech startup and whether the CCT model qualifies as a product or requires a spin-off entity
- [ ] Prepare a one-page executive summary of the CCT model with the five confirmed hypotheses and the 13 to 22 percentage point super-additivity range
- [ ] Compile the three preprint links from OSF and Zenodo, plus the co-authored Alcohol paper status, as evidence of scientific validation
- [ ] Draft a business model canvas for the CCT model as a software-as-a-service platform for pharmaceutical R&D
- [ ] Prepare a technical architecture document describing the ODE system, RK45 integration, PyMC calibration, and the neurocascade engine
- [ ] Confirm the M.Sc. Digital Health enrollment at Hasso Plattner Institute and University of Potsdam for Winter Semester 2026/27 and include the acceptance letter
- [ ] Gather endorsement letters or statements from Kent Berridge, Samuel Gershman, Nathaniel Daw, and Marcelo Mattar
- [ ] Prepare a budget outline for the 10,000 to 100,000 dollar range, specifying use of funds for regulatory consultation, market analysis, and cloud infrastructure
- [ ] Confirm whether the programme requires a demo or prototype; if so, prepare a video walkthrough of the CCT model running on a test dataset

EDITOR NOTES

- Eligibility risk is the primary flag: the EIC pre-accelerator targets startups registered in EU Member States or Horizon Europe associated countries, specifically widening countries. Eniola is a Nigerian national and the venture has no EU registration. The application must either identify a partner entity in a widening country or confirm the programme's international dimension. This needs verification before submission.
- The widening countries list must be checked: Germany, where Eniola will be enrolled at HPI and University of Potsdam, is not a widening country. Portugal, Poland, and Greece are examples of widening countries. If a partner is needed, the partner must be in a widening country, not just any EU state.
- The programme type is an accelerator, not a grant. The application should emphasize the venture's readiness to benefit from coaching and investor-readiness support, not just the scientific merit. The current draft leans heavily on research validation; the commercial readiness section may need strengthening.
- The CCT model's status as a product versus a research output needs clarification. The application should state explicitly whether the venture is a spin-off company, a licensing opportunity, or a platform that Eniola will operate independently. The current draft implies a platform but does not specify the legal entity.
- The budget range of 10,000 to 100,000 dollars is wide. The application should include a specific budget breakdown, even if provisional, to demonstrate financial planning. The current draft does not include a budget section, which may be a required field on the submission page.

AI Draft — EIC pre-accelerator