Challenge
Over the past ten-plus years we have trained a small population of people from around the world in our work, and they are in various life and career stages. This training is not easily replicable. As such we must relocate (partly for security and IP defense), and pay people across a spectrum. They are not replaceable easily. On average our work is the equivalent of a four year stem degree, though certain people have grasped it in less time. In order to secure these people we must have the runway (money) to justify their participation.
Objective
To secure the trained personnel, protect our intellectual property, and deliver the first four volumes of the Runcible Corpus—the computability layer for language reasoning that transforms probabilistic AI into decidable, auditable, and warrantable intelligence.
1. The Challenge
Over the past decade, we have trained a small, globally distributed group of specialists in the Runcible Method—integrating Natural Law, operational epistemology, Socratic dataset construction, and truth-constrained curation.
Each participant represents the equivalent of a four-year STEM education in this discipline. Their training cannot be rapidly reproduced, and their replacement cost is prohibitively high.
To preserve this human capital and maintain continuity of production, we must:
- Relocate key personnel to a secure, consolidated jurisdiction (for IP protection and operational security).
- Provide three years of runway to ensure full output of the first four corpus volumes.
- Fund remaining technical hires and infrastructure to deliver those volumes on schedule.
This is not a “grow-as-you-go” operation. It is a knowledge protection and production mandate—locking the only trained operators capable of constructing truth-constrained, testifiable training data for AI reasoning.
2. Capital and Assets Contributed
Direct and indirect founder capitalization to date:
| Contribution | Estimated Value | Description |
|---|---|---|
| Cash (Friends & Family SAFEs) | ≈ $2M | Early liquidity and operational runway |
| Loan from B. Werrell | $110K | Bridge for infrastructure and relocation |
| Runcible Application Platform (“Oversing”) | ≈ $10M | Engineering replacement value of the platform and stack |
| Curt Doolittle — Foregone Income | ≈ $6M | Personal capital burn across R&D and content production |
| Volunteer & Pro Bono Labor | ≈ $1.0–1.2M | 29,952 hours over eight years across six specialists |
Total Contributed Value: ≈ $19M
These assets constitute the full research phase: the software, the methodology, the training system, and the corpus architecture.
The raise funds operationalization—not discovery.
3. Valuation Ladder
Comparable AI infrastructure and data-layer companies—those with novel dataset or training approaches—have raised at $30–100M pre-money valuations.
Runcible justifies the upper end of that range because it creates computability for language reasoning, the missing layer required for AI alignment and decidability.
Valuation Framework
- Floor (Financial / Generalist VCs): $50–75M pre-money
Reflects standard AI infrastructure comparables and provides entry flexibility for non-strategic investors. - Target (Specialist / AI Infrastructure Funds): $75–100M pre-money
Recognizes the corpus and training method as a defensible and compounding asset—non-scrapable, non-cloneable, and non-autogenerable. - Strategic Premium (Hyperscalers & Integrators): $100M+ pre-money
Justified by the structural alignment and decidability bottleneck Runcible uniquely solves. For strategic acquirers, this is the missing computability layer required for governed reasoning.
This ladder preserves flexibility across investor profiles without conceding strategic value.
Runcible is to reasoning what ImageNet was to vision— the bridge from simulation to computation.
4. Funding Structure
Two funding paths provide flexibility depending on investor preference for risk distribution or operator certainty.
Path A — Milestone-Structured Series A (Risk-Reduced)
- Initial Close: $20M upfront
Enables relocation, IP protection, and retention of 20 senior staff. Funds delivery of Volume 2. - Milestone Tranche (~4 months): $10–15M
Triggered by completion and validation of Volume 2 as proof-of-value—operational training data capable of being test-run by hyperscalers. - Step-Up Round (18 months): $30–50M at $150–200M valuation
Triggered by completion of four volumes, establishing recurring corpus-curation operations.
Use Case: Institutional investors seeking staged deployment with verifiable progress checkpoints.
Path B — Full-Runway Series A (Operator Certainty)
- Series A Raise: $25–30M upfront
Provides three-year runway for 20 senior staff and full operational overhead. CategoryAllocationNotesPersonnel (20 × $250K + 40% OH)≈ $21MCore staff, training, and administrationInfra + Compute + Ops≈ $6MFacilities, legal, travel, cloud, and hardwareContingency & Scalability≈ $3MBuffer for relocation and regulatory cost varianceTotal$27–30MThree-year runway
Use Case: Strategics or AI infrastructure funds prioritizing speed, certainty, and jurisdictional security.
5. Milestones and Deliverables
| Timeframe | Milestone | Outcome |
|---|---|---|
| Month 4 | Volume 2 Corpus Delivered | Proof-of-Value Dataset: test-run by hyperscalers |
| Month 12 | Volume 3 Completed | Demonstrates scalability of method |
| Month 18 | Volume 4 Completed | Full pipeline for governed reasoning data |
| Year 3 | Continuous Corpus Expansion | Transition to ongoing revenue: certification, fine-tuning, and truth-corpus access |
6. Capital Utilization Thesis
The first $20–30M functions as both insurance and ignition:
- Insurance: Secures and protects irreplaceable IP and human capital.
- Ignition: Funds the production of a corpus that enables governed, decidable AI.
Subsequent capital (post-milestone) amplifies market capture and domain expansion rather than speculative research.
7. Long-Term Positioning
Runcible establishes the truth, reciprocity, and liability layer for artificial intelligence.
Our moat is not a model, but a method—one that cannot be scraped, simulated, or imitated without the trained operators who built it.
Every new corpus volume compounds value. Every new domain integrated into that corpus expands computability and revenue.
The result: a defensible, recurring, and auditable foundation for truth-constrained AI across law, medicine, defense, finance, and governance.
Summary
- Raise: $25–30M Series A (3-year runway; optional tranche structure).
- Valuation: $75–100M pre (floor $50–75M; strategic $100M+).
- Milestones: Volume 2 in 4 months; Volume 4 in 18 months.
- Moat: Corpus + Method = Non-replicable computability layer.
- Outcome: Decidable, auditable, and warrantable AI intelligence.