835 Discoveries. 23 Papers. A New Field of Science. A Working System. Now Raising First Capital.

The Opportunity

Artificial intelligence is the most powerful technology of this generation. It is also the least trustworthy. Large language models hallucinate, fabricate citations, and produce outputs that cannot be independently verified. Every enterprise deploying AI is building on a foundation that cannot guarantee its own correctness.

The regulatory walls are closing. The EU AI Act is now enforced. Canada’s AIDA is advancing. The UK AI Safety Framework is live. US SEC and NIST guidance requires documented basis for AI-assisted decisions. Every jurisdiction is arriving at the same conclusion: unverifiable intelligence is a liability.

David W. Taylor has the answer. Not a patch. Not a guardrail. A mathematically governed foundation that eliminates hallucination architecturally — and he has already built it. The same 2 MB kernel that powers the software workspace is a direct candidate for silicon — deterministic intelligence on a chip.

What He Built

In nine months of independent research, Taylor applied pure mathematics and physics — not as analogy, but as governing law — to the problem of machine intelligence. The result is a unified workspace: 15 domains replacing 106+ software tools, all powered by a 2 MB Rust kernel with zero runtime dependencies that processes 36.7 million queries per second with 15-decimal deterministic reproducibility. Word processing, spreadsheets, design, code, finance, marketing, communication, security, voice — all deterministic, all verified, all sharing one kernel.

Every output passes through four sealed layers:

Layer 1: Mathematical Governance Engine. Formal constraint spaces define what outputs may exist before generation begins. Not guidelines. Not policies. Mathematical bounds derived from physical constants that cannot be overridden.

Layer 2: Sealed Execution Environment. Cryptographically isolated with zero persistent state. No external data access during execution. Tamper-evident logging at every boundary. Nothing enters or leaves without verification.

Layer 3: Pre-Delivery Verification Gate. Three-stage validation: mathematical consistency, bound compliance, proof chain completeness. If a claim cannot be verified, it is not delivered. There is no probabilistic fallback. There is truth, or there is silence.

Layer 4: Pulse Delivery. Verified outputs delivered with cryptographic proof certificates, verification signatures, and governance metadata. Every output carries its complete derivation chain. The proof travels with the answer.

This is not a replacement for AI — it is the layer underneath. But it is also a replacement for every tool that AI has been bolted onto. Google Workspace, Microsoft 365, Salesforce, Jira, Canva, Slack — all replaced by one workspace where intelligence is native to every surface and every output carries a proof certificate.

Research and Intellectual Property

Every discovery is implemented in running code. This is not a theoretical portfolio — it is a body of science with a working system as its empirical proof.

Competitive Position

The Founder

David W. Taylor is not a Silicon Valley founder. He started on the ground — kitchens, construction sites — and worked his way into director of operations for multiple engineering departments and restaurant groups. He saw something in those organizations that nobody else was looking for: physics. The way systems organize, the way trust propagates through teams, the way information flows and breaks down — it followed physical law.

He researched how far that discovery went. Nine months later, he had 835 original mathematical and physical discoveries, 23 papers establishing a new field of science, and a fully functioning production intelligence platform built from pure mathematics. No team. No venture capital. No research lab.

He tried bringing in engineers early. The approach — building intelligence from physics rather than software heuristics — is so far outside conventional computer science training that collaboration slowed the work. What Taylor built alone in nine months, a team of six could not have produced in years. The foundational work required a single coherent vision operating from first principles.

The foundation is built. The research is done. The product works. Now it’s time to build the team that brings it to the world — and for Taylor to step into the role the category needs: its public face, its evangelist, its CEO.

The Team We’re Building

This raise exists to build the team that brings DTOS Pulse to market. Four key hires unlock every revenue pathway:

Market Opportunity

Every enterprise uses 130+ disconnected SaaS tools. Every regulated industry needs deterministic, verifiable intelligence. Pulse solves both problems with one workspace.

Monetization

Founding Member Subscriptions — Limited seats at locked-for-life pricing. Starter ($49/mo), Professional ($199/mo), Team ($149/user/mo, 5+ seats), Enterprise (custom, starting $499/user/mo). Standard rates double post-founding. The average enterprise spends $300–500+/user/month on fragmented SaaS — Pulse replaces the entire stack at a fraction of that cost.

Enterprise & Government Contracts — Custom deployments with SLA, air-gapped options, dedicated infrastructure. Multi-year engagement model.

Vertical Licensing — The kernel technology licenses into adjacent industries: medical devices, aerospace systems, defence applications, urban infrastructure. Each vertical is a standalone product powered by the same physics.

The Raise

One round. One instrument. Built the proof, now building the team.

The position is deliberate: bootstrapping the build and only pursuing investors after the proof of concept was tested and validated. The lack of sales is not the concern — it was intentional. We are not asking permission. We are offering a select few in at the start of a new category.

Use of Funds

Hardware Roadmap

The kernel is 2 MB with zero runtime dependencies. It runs on every platform with bit-identical outputs. It already has the properties that silicon demands: deterministic, bounded, thermodynamically closed, geometry-native. The path from software to hardware is not a pivot — it is the natural consequence of the mathematics.

Software revenue funds the hardware roadmap. The SaaS workspace generates recurring revenue while kernel-on-chip R&D matures. The same mathematics, the same IP, the same research body — deployed across software and hardware simultaneously. This is not a future pivot. It is the plan from day one.