BoxSight LLC is a USA-based technology architecture firm. Nine capabilities on one backend stack. Each entry below states its status explicitly — running in production, pre-launch, or research.
The BoxSight AI Studio
BoxSight isn't a collection of one-off apps — it's a studio. The hard parts — auth, AI, payments-grade data, delivery, marketing — are already built and tested once, as shared building blocks. So a new product doesn't start from zero: it starts most of the way done, assembled from proven parts, validated by multi-model consensus, then shipped to both app stores and given a promo video by the same toolchain. The studio is the moat; each app is an instance of it.
Spec → application
A natural-language specification becomes a deployed app through a six-gate pipeline (ADL), with a five-model consensus engine cross-checking every AI decision.
Pre-built foundation
A unified AI client, shared authentication, config, audit, email and a common UI kit — each one production-tested in live apps and reused by the next, so nothing is rebuilt.
Automated delivery
One toolchain builds each app, generates its store screenshots, pushes to TestFlight, and releases to the App Store and Google Play.
Automated marketing
The same studio generates narrated, captioned promo videos and audio explainers from a storyboard — including the Topos and LIITOS-AI promos on this site.
One toolchain — idea to shipped, promoted product
LIITOS-AI
SpendCity
LIITOS
Topos
Every ✓ block is already built and tested in production. A new product assembles them instead of rebuilding — so it starts most of the way done and ships in a fraction of the time.
Live Intelligence Products
Receipt digitization and expense intelligence. Images are sent to Gemini for OCR and field extraction; structured outputs — merchants, line items, categories, spending patterns — land in a searchable 88-table schema. Features a gamified "Financial City" visualization.
AI/ML research synthesis engine. Ingests 30+ curated sources daily at 03:00 UTC and generates structured briefings via vector search and LLM inference. Running continuously since initial deployment.
Academic collaboration platform for sustainability research. Aggregates 20+ university-affiliated feeds plus curated RSS sources, with AI-generated weekly video briefings, trend detection, researcher profiles, and threaded discussions. Seeded with 20 universities including Lund University.
Tap-first nearby-place finder where travel time replaces distance as the search dimension. Two taps — category plus time radius — return matching places with AI-generated result labels, 22 pre-built situation bundles, and multi-stop trip chaining. Now live on the App Store and Google Play.
Infrastructure & Frameworks
Two-primitive specification language (Entity + Transition) with a 6-gate LLM pipeline and 5-model consensus mechanism. Moves from natural-language intent to deployed application across six target stacks. Used to build six applications with zero human coding after intent submission. ADL-UI — a browser-based spec session that generates a preliminary build plan — is coming to adl.boxsight.ai.
Read the ADL Whitepaper →Multi-model consensus engine using the Model Context Protocol. Queries 5 LLMs simultaneously with Jaccard similarity clustering to surface disagreement and reduce single-model failure modes. Published on GitHub and npm.
A methodology for auditing the rules that hold the internet together — IETF RFCs, W3C specs, OAuth/WebAuthn/JOSE flows — by reading what they MUST/SHOULD/MAY require and then proving where real-world implementations diverge. HUNTER's first novel CVE shipped in 2026: CVE-2026-48522 — a previously-unreported divergence across the JOSE ecosystem (Python/Java/Node) — found by following the same workflow it applies to every protocol.
Now spanning eleven phase milestones across JWT/JOSE, X.509, OAuth 2.0, WebAuthn, CBOR/COSE, DANE (TLSA), TLS 1.3 trust-anchor handling, SAML, and others. Each milestone runs five gates — RFC grounding, probe authoring, library matrix, oracle determinism, and confound audit — before any divergence claim is made, so findings are reproducible by anyone with the spec text and the probe corpus.
Read the HUNTER Capability Brief →Local-first network security monitoring. Distributed agents running local LLMs (Ollama) produce plain-language threat detection and network health reports. No telemetry leaves the perimeter.
Pre-flashed USB for zero-configuration network OS deployment. Plug in, pick an OS, walk away. Installs a PXE boot control center alongside any existing OS with cloud licensing, auto-updates, and telemetry. Supports Linux, macOS, and Windows targets via iPXE, GRUB2, and WinPE. 88 requirements delivered across POSIX Shell, Node.js, Python, and PowerShell implementations.
Why it compounds
This is deliberate architecture, not accidental reuse. Every capability built for one product — vector search, LLM reasoning, multi-model consensus, on-device inference — deploys to the next without re-engineering, so each app ships faster than the last. The same spec-driven methodology even extends to bare-metal infrastructure in LINETBOOT-USB. The studio is the asset; the apps are what it produces.