A research-driven technology company applying a single network-coordination framework across machine learning, traffic intelligence, and cognitive science — built from scratch, validated on real data, and protected under U.S. patent.
Tier 1 — Core R&D
Original, patent-protected work validated on production data and public benchmarks — the systems that define what Qira is.
A language model built from scratch — a hybrid Transformer–SSM architecture that separates language into surface and latent representations, rather than the standard single-stream approach.
Trained and reproduced across standard datasets on both GPU and TPU, supported by the Google TPU Research Cloud.
Real-time intelligence for Phoenix freeways — corridors monitored around the clock and modeled as a coupled-oscillator network that predicts congestion cascades before they form.
Validated against the standard METR-LA research benchmark, with cascade predictions and crew-dispatch guidance generated continuously from live AZ-511 and HERE routing data.
A mathematical framework for the gap between what people know and what they can express under pressure — one gating function, g(K)=4K(1−K), and three confirmed response types.
Compressors, Expanders, and Suppressors — an empirical study with real subjects. Preprint published on Zenodo; ArXiv endorsement pending.
The coding AI that sees your codebase as a network. Not autocomplete — structural intelligence that understands how every file, function, and dependency connects to everything else.
Qira's network-coordination thesis applied directly to software: treat the repository as a graph and reason over its structure, not just its text.
Tier 2 — Applied & Experimental
Smaller bets, shipped tools, and side projects — built with the same rigor, but separate from the core research program.
Encrypted envelopes and tamper-evident vaults — browser, CLI, desktop, and Android — with policy-aware workflows and signed, verifiable trust semantics.
MIT-licensed and open source — the hardened BRY-NFET-SX engine, productized across four platforms.
On-device privacy for Android: a default SMS app that encrypts texts at rest, an app-wide lock, and text / file / vault encryption — plus root and device-owner power modes. No account, no server, no telemetry.
A marketplace for verified digital goods. Every purchase issues a cryptographically signed receipt and license that anyone can verify — secured by Qira's QEV trust engine.
The prompt stack for solo developers — bring your own key across every major model, ranked by real usage. One workspace, every provider.
Community news with bias comparison and blindspot detection across a wide range of sources — no algorithm, no paywall, no tracking.
A quieter social network for real-life friendship — personality-matched, with end-to-end encrypted DMs, no ads, and no follower counts.
Approach
Qira's research centers on a single insight: the mathematical structures that govern phase transitions in physical networks also describe critical behavior in traffic systems, cognitive expression, and language-model training dynamics.
The same mathematics that detects a freeway congestion cascade also identifies the latent-order dynamics inside a language model and the gating behavior of conscious expression. This is not analogy — it is the same theory, applied at different scales.
By grounding every system in network coordination theory rather than domain-specific heuristics, our models don't merely fit data — they capture the dynamics that generate it.
Mathematical modeling of how information, congestion, and instability propagate through connected systems — Kuramoto coupling, phase synchronization, and operating-band regimes.
Continuous monitoring loops that detect anomalies and predict state changes before they cascade — production systems that run around the clock.
A single theoretical framework — validated empirically across transportation, cognition, and language — rather than three disconnected tools.
Team
A two-person research company — theory and systems, working end to end from first principles to live deployment.
Lead architect of LOLM and originator of the gating functions and boundary conditions that underpin Qira's network-coordination theory. Drives the mathematical architecture connecting traffic dynamics, cognition, and adaptive training, and manages large-scale TPU training.
Systems architect behind Qira's live platforms — data infrastructure, real-time monitoring, and empirical validation. Leads deployment of the Phoenix Traffic Intelligence system and all of Qira's production engineering and cloud operations.
Recognition & Validation
Independent work, patent-protected and externally validated.
Network coordination methods. Filed March 2026.
Granted access for large-scale ML training, 2025–present.
Invited discussion on network-coordination frameworks.
EGC framework published; ArXiv endorsement pending.
Contact
We partner with transportation agencies, research institutions, and technology teams that need rigorous, real-time intelligence for complex systems.