Recommended Architecture
1. Purpose
The purpose of this financing is to convert founder-financed proof into institutional-scale execution.
Runcible has completed the discovery phase: method, corpus architecture, trained operators, platform foundations, and early proof surfaces.
The next stage requires capital to secure the trained team, protect the intellectual property, harden the runtime, produce the first institutional corpus volumes, and execute vertical proof deployments.
2. Why This Is Not Ordinary SaaS Financing
Runcible is not being financed as another assistant, workflow app, or model wrapper.
It is institutional AI infrastructure: the adjudication and qualification layer that converts AI-generated hypotheses into testable, reviewable, certifiable, and actionable Decidability Records.
That requires a different capital structure than ordinary SaaS because the scarce assets are not only software. They include:
- the Runcible Method
- the Decidability framework
- the corpus architecture
- trained protocol operators
- institutional grammar
- Decidability Record design
- RDL / runtime architecture
- Oversing platform foundations
- early proof surfaces
3. Scarce Asset Protection
Over the past decade, we have trained a small, globally distributed group of specialists in the Runcible Method.
This training integrates Natural Law, operational epistemology, Socratic dataset construction, adjudication protocols, and truth-constrained curation.
These operators are not rapidly replaceable. Their training represents years of accumulated method, vocabulary, discipline, and judgment.
The financing secures this human capital and converts it into an operating protocol factory.
4. Founder-Financed Contributions
Founder and early-supporter capitalization has already funded the discovery phase.
Estimated contributed value:
- Friends & Family SAFEs: approximately $2M
- Bridge loan from B. Werrell: $110K
- Oversing application platform: approximately $10M replacement value
- Curt Doolittle foregone income: approximately $6M
- Volunteer and pro bono labor: approximately $1.0–1.2M
Total contributed value: approximately $19M
These assets constitute the research, method, platform, training system, and corpus architecture.
The raise funds operationalization, not discovery from zero.
5. Financing Objective
The financing objective is to secure three years of runway for institutional-scale execution.
Primary uses:
- secure and relocate key trained personnel
- protect IP and operational continuity
- harden Runcible runtime architecture
- produce the first four corpus volumes
- build the protocol factory
- fund infrastructure, cloud, hardware, and security
- execute first vertical pilots
- prepare strategic partnership or acquisition pathways
6. Preferred Funding Structures
Path A — Milestone-Structured Series A
Initial close: $20M upfront
Purpose:
- relocation
- IP protection
- retention of senior staff
- delivery of Volume 2
- runtime hardening
- first pilot preparation
Milestone tranche: $10–15M
Triggered by completion and validation of Volume 2 as proof-of-value.
Step-up round: $30–50M at $150–200M valuation
Triggered by completion of the first four volumes and establishment of recurring corpus-curation operations.
Best for investors who prefer staged deployment and verifiable checkpoints.
Path B — Full-Runway Series A
Raise: $25–30M upfront
Purpose:
- three-year runway
- secure 20 senior staff
- relocate critical personnel
- fund platform hardening
- fund corpus production
- fund vertical pilots
- fund infrastructure and security
Best for strategic investors or AI infrastructure funds prioritizing speed, certainty, security, and control of the category window.
7. Valuation Framework
Floor: $50–75M pre-money
Appropriate for generalist or financial investors using standard AI infrastructure comparables.
Target: $75–100M pre-money
Appropriate for specialist AI infrastructure investors who understand the corpus, method, protocol, and Decidability Record system as compounding assets.
Strategic premium: $100M+ pre-money
Appropriate for hyperscalers, model companies, enterprise platforms, or integrators for whom Runcible solves a strategic bottleneck: the passage from AI capability to governed institutional action.
8. Milestones
Month 4:
Volume 2 delivered as proof-of-value corpus and testable protocol asset.
Month 12:
Volume 3 completed, demonstrating scalability of method and production system.
Month 18:
Volume 4 completed, establishing the initial governed-reasoning corpus pipeline.
Year 3:
Continuous corpus expansion, protocol package development, certification pathways, fine-tuning support, and Decidability Record infrastructure.
9. Capital Utilization Thesis
The first $20–30M functions as both protection and ignition.
Protection:
- secures irreplaceable trained operators
- protects IP and corpus continuity
- prevents loss of method-specific human capital
Ignition:
- funds protocol production
- hardens the runtime
- produces validated corpus volumes
- enables vertical pilots
- prepares enterprise and strategic integrations
Subsequent capital funds market capture, domain expansion, and platform scaling.
10. Long-Term Positioning
Runcible establishes the adjudication and qualification layer for institutional AI.
The moat is not only a model or application.
The moat is the method, protocol corpus, trained operators, Decidability Records, institutional grammar, and workflow embedding required to make AI-generated hypotheses testable, warrantable, auditable, and actionable.
- Every new corpus volume compounds the asset.
- Every new domain expands the protocol system.
- Every Decidability Record strengthens the institutional memory.
Summary
Raise: $25–30M Series A, with optional milestone structure.
Valuation: $75–100M pre-money target; $50–75M floor; $100M+ strategic premium.
Milestones: Volume 2 in four months; Volume 4 in eighteen months; continuous corpus operations by year three.
Moat: method, corpus, trained operators, protocol architecture, Decidability Records, and institutional workflow embedding.
Outcome: the adjudication and qualification layer that converts AI-generated hypotheses into testable, reviewable, certifiable, and actionable institutional work.