For investors
Jean OS is a local-first, privacy-preserving AI platform already proven across five unrelated domains — with no shared training data, no cloud dependency, and no LLM calls in the cognitive path of two of them.
"We don't ask investors to take our word for anything. Every claim on this page has a methodology behind it, a raw data file, or a video you can watch right now."
We determine whether a capability is real by first asking: is there an industry-standard Software-in-the-Loop (SIL) test for this domain? If yes, we run it and publish the methodology. If not, we define one, document the criteria, and hold ourselves to it. Boundary conditions are named. Failures are reported alongside successes. The result that survives a hostile reader is the only result worth showing.
Raw data — JSON trial reports, Criterion benchmark outputs — is available for independent verification on request. We don't hide the numbers that don't look perfect. We explain them.
The receipts
The same codebase — no retraining, no domain-specific rebuilds — running across cybersecurity, education, medicine, robotics, and autonomous navigation.
Cybersecurity
Tested against APT-class attack vectors using industry-standard SIL methodology. 92.3% block rate with zero false positives across 1,700+ assessments. Runs where your data already lives — no cloud exposure, no third-party visibility into your threat surface.
Education
A working pilot with Sebastian Benavidez III in Parlier, California produced a +22 point student proficiency shift alongside 7–10 hours per week returned to the teacher. Student data never left the classroom. The testimonial is on record.
Robotics
Jean's reasoning ran inside the control loop of a 7-DOF robotic arm — no learned policy, no training data. CARVER grasp selection and ADAPT precision adjustment are deterministic reasoning. The arm threaded standard targets 11/11, refused impossible ones 4/4 with specific reasons, and reported zero false successes across all runs.
Honest boundary: fine-precision threading (0.4mm eye) is not yet achieved — a diagnosed motion-planning limitation, not a reasoning failure. The fix is specified. Closed-loop visual servoing is the next build.
Medicine
Seven consecutive New England Journal of Medicine diagnostic cases — answered correctly, running entirely on local hardware, at roughly 27 seconds and 2 cents of compute per case. No data left the machine. Each case is recorded and publicly available.
Navigation
5,490 live route nodes evaluated across Oakland road networks with zero LLM calls in the cognitive path. Pure deterministic reasoning, posted publicly. The same architecture that handles robotics handles navigation — substrate-invariant by design.
Method
A Software-in-the-Loop test is an industry-standard approach for verifying AI behavior in simulation before physical deployment. For each domain, we first ask: does an accepted SIL standard exist? If yes, we run it. If not, we define the criteria, document them publicly, and hold ourselves to them.
Methodology papers are linked above for cybersecurity, education, and robotics. As we add new SILs — clinical, navigation, and future domains — methodology papers will be added here. Raw trial data (JSON reports, Criterion benchmark outputs) is available for independent verification on request — we don't hide numbers that don't look perfect, we explain them.
To request raw data for independent verification: ir@myasolutions.org
The economics
Most AI gets more expensive as it scales. Jean OS inverts that. Compute runs on the user's device — which means our infrastructure cost approaches zero as our user base grows.
IBM Watson Health: four billion dollars over a decade, divested for one. Our clinical knowledge base: 9.8 megabytes, five dollars. The architecture is the advantage.
Why now
Sovereign AI, edge compute, and privacy-first software are now the fastest-growing segments in enterprise technology. We've been building this architecture since before those terms were headlines.
Governments and enterprises worldwide are demanding AI that runs within their borders, on their hardware, under their control. Jean OS was designed for this from day one — not retrofitted to meet a regulation.
The shift from cloud-first to edge-first is accelerating. Jean's architecture is natively local — the edge is where it lives, not a deployment option added later.
HIPAA, FERPA, CMMC, GDPR — regulated industries need AI that is private by construction, not by policy. Jean OS cannot exfiltrate data because it never touches a network to begin with.
One architecture. Five domains already proven. Every new domain we enter costs us less than the last — because we're not rebuilding, we're extending.
The conversation
We're raising a mission-aligned round — not to survive, but to grow. The proof is on this page. The full briefing, cap table, and financials are available once we've had an initial conversation.
Start here
Tell us who you are, what drew you to this, and what questions the page didn't answer. We respond to every serious inquiry personally — no pitch deck attachment required on your end, no intake form on ours.
Opens your email app · sends to ir@myasolutions.org · we respond personally
If you'd prefer to request raw verification data (JSON trial reports, Criterion benchmarks) before reaching out, email ir@myasolutions.org with the domain you'd like to verify. We'll send it.