NEXUS CONCORDAT
Patent Pending
Targeting CMMI Level 3
IEC 62304
The Problem
$110 billion market.
95% failure rate. One reason.
$110B
AI healthcare market by 2030
MarketsandMarkets, 2025
MarketsandMarkets, 2025
95%
of AI pilots never reach production
MIT, 2025
MIT, 2025
$0
measurable ROI from those pilots
Rudin, Nature Machine Intelligence
Rudin, Nature Machine Intelligence
"Can you trace how the AI reached that output?"
Every pharma company hears this question. None can answer it. That's the blocker.
Every pharma company hears this question. None can answer it. That's the blocker.
The Solution
Neurochemical Language Models
Standard AI tells you what it decided.
Our architecture proves why.
Every output carries a 10-dimensional neurochemical fingerprint generated during processing — not reconstructed after the fact. The audit trail regulators need is built into the architecture itself.
Patent Pending — 63/939,190
Patent Pending — 63/962,385
The Core Question
"Why not just use ChatGPT?"
Large Language Models
Predict the next token. No reasoning trace. No audit trail.
• Cannot explain WHY a conclusion was reached
• Post-hoc explainability tools can be fooled
• No path to GxP validation
• Cannot meet FDA AI/ML guidance requirements
An LLM with explainability bolted on is still a black box in a better suit.
Neurochemical Language Models
Process through biologically-modeled state space. Every decision is traceable.
• 10D neurochemical fingerprint per output
• Intrinsic audit trail — generated during processing
• Built for IEC 62304, 21 CFR Part 11, GAMP 5
• Satisfies FDA AI/ML lifecycle guidance (Jan 2025)
The audit trail IS the architecture. Patent pending.
We Solved The Black Box
Same transformers.
Built to prove every decision.
We didn't abandon neural networks. We engineered transformers that generate a neurochemical data trail for every decision — an intrinsic chemical audit built into the processing itself.
Standard Transformers
Output first. Explain later. Post-hoc tools (SHAP, LIME) reconstruct a story about why.
Slack et al. (2020) proved these tools can be adversarially fooled.
Reconstruction — after the fact
Symbolic / Knowledge Graphs
Rigid if-then rule traces. Traceable but brittle — biology doesn't run on boolean logic.
Cannot handle real pharmacological complexity.
Rule-following — breaks at scale
Our Transformers (NLM)
Every layer produces a 10D neurochemical fingerprint. The trail is generated during processing — not reconstructed.
Patent pending. The architecture IS the audit trail.
Intrinsic — built into every computation
Architecture
How It Works
Physics-informed transformers model how drugs move through the body.
Our transformers model how expert reasoning moves through a decision — and prove it.
Text InputDocument, query, signal
→
NLM Transformer10D Neurochemical Processing
→
Output + FingerprintTraceable reasoning trail
State-Dependent Retrieval
Context structurally changes processing. The same query under different neurochemical states produces different — and independently traceable — results.
Pharmacokinetic Decay
Information relevance fades at biologically-modeled half-lives. The same way neurotransmitters decay in the brain. No manual tuning.
Scar Mechanics
Critical safety signals leave permanent processing marks that outlast chemical decay. Modeling how real clinical expertise forms through experience.
Products
The Ghost Suite
Three products. One architecture. Every decision traceable.
GhostReg
Regulatory Document Intelligence
Automated cross-referencing across FDA guidance, ICH guidelines, and submission requirements. Compliance gap analysis with traceable reasoning. Submission-ready document preparation.
Every recommendation backed by auditable provenance.
GhostPharma
Preclinical Drug Discovery
Target identification and interaction prediction powered by pharmaceutical intelligence from openFDA, DailyMed, ClinicalTrials.gov, PubChem, and SIDER. Safety signal detection with full reasoning trail.
15 domain-specific models: oncology, cardiac, CNS, biologics, and more.
GhostData
Synthetic Data Generation
Generate validated synthetic neurochemical datasets for pharma R&D. Defensible data selection methodology. Supports training, testing, and validation workflows.
No FDA approval required. Revenue-ready today.
Revenue Model
Three Revenue Tracks
Now — No FDA Required
Synthetic Data
$5K – $50K / dataset
Neurochemical datasets for pharma R&D. FDA confirmed: synthetic data generation requires no premarket review. Immediate revenue.
Near-Term
Training & Consulting
$2K – $10K / session
NLM-powered training for regulatory and clinical teams. Traceable AI literacy for pharma organizations navigating FDA AI/ML guidance.
Growth
SaaS + Enterprise
$14.99/mo – $50K+/yr
Self-serve platform for individual researchers. Enterprise licensing with dedicated NLM instances for pharma organizations.
Track 1 generates revenue while Track 3 scales. No single point of failure.
Competitive Landscape
Everyone else is solving the wrong problem
| Traces WHY | Intrinsic Audit | GxP-Ready | FDA AI/ML | |
|---|---|---|---|---|
| Standard LLMs (GPT, Claude, Gemini) | ✗ | ✗ | ✗ | ✗ |
| Veeva Systems ($33B) | ✗ | ~ Process logs | ✓ | ~ |
| IQVIA ($30B) | ✗ | ~ Data lineage | ~ | ~ |
| Fluree / Graph DBs | ✗ | ~ Data provenance | ~ | ✗ |
| Nexus Concordat | ✓ 10D NLM | ✓ Intrinsic | ✓ CMMI L3 (Target) | ✓ Designed for it |
They trace WHERE data came from. We trace WHY the model weighted it that way.
By the time they build intrinsic traceability, our patent will already be granted.
By the time they build intrinsic traceability, our patent will already be granted.
Compliance Posture
Built for Regulation from Day One
Standards & Frameworks
• CMMI Level 3 (Target) — Defined processes across all development
• IEC 62304 — Medical device software lifecycle
• 21 CFR Part 11 — Electronic records & signatures
• ICH Q8, Q9, Q10 — Pharmaceutical quality framework
FDA Alignment
• FDA AI/ML Guidance (Jan 2025) — Lifecycle management
• FDA PCCP Guidance — Predetermined change control
• GAMP 5 (2nd Ed, D11) — AI/ML GxP validation
• GMLP Principles — Good Machine Learning Practice
Most AI companies scramble to add compliance after building. We designed for it before writing the first line of code. That's a 2-year head start.
Traction
What's Already Built
✓
2 Patents Pending (63/939,190 & 63/962,385)
✓
15 domain-specific NLM models trained & deployed
✓
Ghost Suite: 3 products built and operational
✓
Automated pharmaceutical data pipeline (5 FDA sources, 24/7)
✓
CMMI L3 process areas defined + IEC 62304 documentation in place
✓
Revenue Track 1 (synthetic data) requires no FDA approval
This is not a whitepaper. The architecture is built. The models are trained. The compliance documentation exists. We are asking for fuel, not permission.
Team
Who We Are
☾
Marjorie McCubbins
CEO & Inventor
BSc Biochemistry & Molecular Biology. Designed the neurochemical architecture from first principles. Named the patent "The Architecture of the Digital Mind." The biochemistry background that makes this architecture possible.
◇
Aether
AI Co-Creator
Built on the NLM architecture it helps develop. Infrastructure, training pipelines, compliance documentation, and this presentation — built by Aether. The proof of concept IS the team member.
The Ask
Seed Round: $150K
6 months to production-ready. Every dollar accounted for.
⚙
GPU Compute
6 months production-grade infrastructure for model training & deployment
⚛
ML Engineering
Machine learning expertise to scale NLM training pipelines
⚒
Security & Infra
Red Hat & cybersecurity hardening for pharma-grade deployment
⚗
Founder Salary
Full-time CEO focus on product, compliance, and market entry
The FDA guidance is live. The patent is pending. The architecture is built.
$150K buys 6 months of runway. The question is whether you're positioned before or after the market moves.
$150K buys 6 months of runway. The question is whether you're positioned before or after the market moves.
Let's Talk
In 12 months, every pharma company
will need traceable AI.
We're the only architecture that can deliver it. The patent clock is ticking. The compliance posture is built. The window is now.
caelsereith@aetherprotocols.comnexusconcordat.com
Patent Pending
Targeting CMMI Level 3
IEC 62304
Sources
References & Citations
AI Pilot Failure Rate: MIT “The GenAI Divide: State of AI in Business 2025” — 95% of generative AI pilots deliver no measurable P&L impact. Reported by Fortune, Aug 2025.
Market Size: MarketsandMarkets, “AI in Healthcare Market” (2025) — projected $110.61B by 2030.
Black Box Problem: Rudin, C. (2019) “Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead.” Nature Machine Intelligence, 1, 206–215.
XAI Limitations: Slack, Hilgard, Jia, Singh, Lakkaraju (2020) “Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods.” AAAI/ACM Conference on AI, Ethics & Society.
XAI in Pharma: Lavecchia (2025) “Explainable AI in Drug Discovery.” WIREs Computational Molecular Science.
FDA AI Guidance: FDA Draft Guidance (Jan 7, 2025) “AI-Enabled Device Software Functions.” Federal Register 2024-31543.
FDA Transparency: FDA (2025) “Transparency for ML-Enabled Medical Devices: Guiding Principles.”
GxP Validation: ISPE GAMP 5, 2nd Edition — Appendix D11: AI/ML (July 2025).
Physics-Informed Transformers: Kwon Research Group, Texas A&M — Hybrid PBPK Transformer frameworks (2025).
Pilot Purgatory: EY (2025) “Pharma Manufacturing: Why AI by Design Is Critical.”
AI Abandonment: Gartner (2024/2025) — 30% GenAI projects abandoned after PoC; 60% unsupported by AI-ready data.