Project summary

I built and shipped Stratum, a secure-by-construction programming language and runtime that treats AI safety as a property of the substrate rather than a behavioral patch. The Basic and Pro editions are fully built, packaged, and live. The shipping version already implements SMT-proved capability gating, actor isolation, and runtime anomaly detection via a physics-based signal processing pipeline.

Crucially, the AI Governance Gateway (powered by Stratum Pro) is also fully productized and shipped. It successfully binds local LLMs to a structural cognitive_tick layer that physically refuses fiction-frame jailbreaks and seals deterministic reasoning paths with Ed25519 cryptographic signatures.

AI safety today dangerously relies on training-time alignment (RLHF) or prompt filtering that can fail invisibly. Stratum changes the primitive: cleverness operates *inside* the structure, never *on* it. Because the core architecture is now complete and validated on bare-metal hardware, this funding goes directly toward scaling deployment into real-world AI infrastructure, completing the physical FPGA conditioning experiment, and tackling the next tier of unresolved structural R&D.

What are this project's goals? How will you achieve them?

With the AI Governance layer and biological-logic hardening primitives already built and compiled, the goal is to fund the deployment pipeline and the next frontier of structural safety R&D. I will achieve this through three deliverables:

1. **Empirical Hardware Validation:** Complete the FPGA conditioning experiment to test substrate memory persistence across cold reboots. This validates the key physical prediction underlying the anomaly detection pipeline.

2. **Resolve the Next-Tier Structural R&D:** Develop a hostless transcendental-free RNG to allow truly bare-metal, offline AI execution without relying on a host environment, alongside building the Go and Rust SDK ports for wider enterprise embedding.

3. **Scale AI Governance Deployment:** Sustain full-time operational focus to embed the existing, shipped Governance Gateway into active AI-agent infrastructures, proving that structural boundaries can replace fragile prompt-guardrails in production environments.

How will this funding be used?

The funding dictates the speed and scope of the R&D and deployment scaling.

* **At the $15,000 minimum tier:** Funds focus strictly on the FPGA conditioning experiment execution and baseline deployment outreach ($300 hardware, $8,000 focused implementation time, $700 paper submission, $1,000 contingency).

* **At the $30,000 tier:** Converts me to full-time focus for six months. Covers stipend ($22,000), self-employment tax obligations ($5,000), FPGA hardware ($1,500), software/submission ($1,000), and contingency ($500). This allows dedicated focus on embedding the Governance Gateway into real-world pilot sponsorships.

* **At the $50,000 full funding goal:** Six months full-time with an expanded research scope. Includes stipend ($35,000), self-employment tax ($4,500), FPGA experiment ($1,500), compute/software ($3,000), paper/conference ($2,000), publications/documentation ($3,000), and contingency ($1,000). This fully funds the development of the hostless RNG and expanded SDK ports.

Who is on your team? What's your track record on similar projects?

I am Alexander Kalyniuk, an independent researcher operating solo in Edmonton, Alberta, with no formal training in physics or computer science. I built Stratum from concept to a live, byte-identical WebAssembly and AST production deployment in six months.

I operate FSME Logic commercially with documented validation on public datasets: 9-month lead time on ESA spacecraft telemetry, 6.3-hour warning on NASA Curiosity Rover actuator binding, and a 78% detection rate across 509 NASA C-MAPSS jet engines. The Stratum AI Governance layer has already passed 16/16 penetration tests and has proven its ability to structurally refuse logic-trap jailbreaks offline.

What are the most likely causes and outcomes if this project fails?

The FPGA conditioning experiment has a sealed prediction protocol with falsification criteria documented in advance. If the experiment yields negative results (e.g., signal collapses on shuffle audit, or no persistence across cold reboot), it informatively constrains the theoretical framework underlying the anomaly detection pipeline.

On the software engineering side, the fundamental structural constraints and the AI Governance layer are already operational and packaged. The risk is no longer fundamental failure, but rather adoption friction—getting AI developers to route local models through a strict, capability-gated runtime requires excellent documentation and sustained integration support, which represents a go-to-market hurdle rather than a technical one.

How much money have you raised in the last 12 months, and from where?

I have raised $0 in external funding. The project has been completely self-funded, with $2,825 CAD in equipment, $200 CAD cash invested, and approximately $58,000-$64,000 CAD in documented sweat equity over 780 hours.