From probabilistic guessing to geometric reasoning. We build the infrastructure that makes AI outputs traceable, verifiable, and accountable.
We're neurodivergent industry veterans who escaped our own versions of the corporate matrix. We see things differently. Literally.
Is AI the problem, or is it the ethos of the humans who have held the power and money behind it thus far?
We met through the YC community, realized we were approaching the AI context gap from opposite directions, and decided to build the solution together.
We're building the antithesis of untrustworthy, untraceable, hallucinogenic, "tech-bro-ism" AI. Technology that is intersectional, human-centered, and accountable rather than extractive.
We won't get it perfect. But we will get it safer and more evolved.
No verification architecture exists.
Current systems capture statistical patterns but lose the structure that enables reasoning. Hierarchies, causality, temporal order: destroyed during encoding.
Vector embeddings rebuild context from scratch every query. Like re-reading an entire book to answer each question about it.
No persistence across sessions. No learning accumulation. Redundant computation every time.
Systems generate outputs without proof of correctness. Can't trace an output back to its source. Can't prove reasoning was sound.
Structure over statistics. One primitive. Three applications.
Symbolic High-Dimensional Context Compression Packet. A 64-bit geometric encoding that captures complete context position with cryptographic verification.
Vector embeddings lose this. We keep it.
One primitive. Multiple domains. Click any card to request a pilot partnership.
Molecular grammar verification, steric constraint checking
Energy conservation enforcement, phase coherence
Physics-informed verification
Protein folding verification, toxicity constraint checking
Clinical decision support verification, patient safety audit trails
Citation verification, precedent validation, research audit trails
Trading rationale verification, underwriting audit trails, regulatory compliance
Clinical trial data verification, adverse event tracking
Claims adjudication rationale, underwriting verification
FOIA-compliant decision logging, OMB M-25-21 compliance
Audit workpaper verification, financial statement reasoning trails
Assessment verification, adaptive learning path validation
Provenance tracking, ESG compliance verification
Underwriting decision verification, fair lending compliance
Hiring decision audit trails, bias detection verification
Fact-checking verification, source attribution
Verified inference on resource-constrained devices
Real-time verification for navigation, manipulation
Autonomous vehicle decision verification
Mission-critical decision support with audit trails
Different disciplines, same problem.
3x founder, systems test engineer by trade. 12+ years in aerospace (Blue Origin), medical/mechatronics (Philips, Volcano, NeuroVision), enterprise SaaS (Udemy, Adobe), and now AI. Shipped products impacting 26M+ users.
Electrical engineer and metrologist, self-taught in quantum computing and neuro-symbolic AI. Conceived the topological encoding approach. Built a working prototype before funding.
The same geometric substrate enables verification, coordination, and native cognition.
Compression + cryptographic proof of reasoning. For enterprises blocked by cost and risk. For scientific computing that needs physical constraint verification.
DNS for agents. The same coordinates used for data become coordinates for agent identity. Cross-registry discovery and interoperability.
Models trained to think in geometry. Verified reasoning paths become computationally cheaper than unverified ones.
We acknowledge we can't do it alone, and we don't want to.
$4M Seed round opens January 2026. Minimum check: $250K. We'll send the deck within 48 hours.