Capabilities — 09 systems · one studio

Nine capabilities.
One studio.

Production-deployed AI systems, developer frameworks, and security research — built with one engineering practice. Each entry below states its status explicitly: running in production, published, or research.

PROVEN PARTS → ASSEMBLED, NOT REBUILT
The BoxSight AI Studio

The studio is the moat.
Each app is an instance.

The hard parts — auth, AI, payments-grade data, delivery, marketing — are already built and tested once, as shared building blocks. A new product doesn't start from zero: it starts most of the way done.

[SPEC → APP]

Spec to 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.

[FOUNDATION]

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.

[DELIVERY]

Automated delivery

One toolchain builds each app, generates its store screenshots, pushes to TestFlight, and releases to the App Store and Google Play.

[MARKETING]

Automated marketing

The same studio generates narrated, captioned promo videos and audio explainers from a storyboard, with AI B-roll on demand.

One toolchain

Idea in.
Shipped product out.

Every checked 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.

Idea — described in plain language
↓
1 · Generate & validate
ADL · 6-gate spec → app Consensus engine · 5 LLMs in parallel
↓
2 · Pre-built foundation — built & tested once, reused by every app
✓ Unified AI client✓ Shared auth✓ Config & DB pooling✓ Audit & tracing✓ Email service✓ Shared UI kit
↓
3 · Ship & promote — automated
✓ One-command store deliverystore delivery · auto screenshots✓ Promo-video generator✓ Audio/video explainers
↓
Products — every one an instance of the studio
SpendCityLIITOSTopos

BoxSight LLC is a USA-based technology architecture firm. Nine capabilities, one engineering practice.

Live intelligence products — 03

In production today.

SpendCity Live deployment

Reads receipts, invoices and bills entirely on your phone: on-device text recognition and document reading, no account, and no server of ours receives your data. The database and the documents are encrypted on the device. Budgets, search, tax reports and exports are built in, beside the "Financial City" view of your spending.

On-device AI · No account · Encrypted on the device · Free · iOS + Android

LIITOS Live deployment

A daily news and research briefing in four areas, kept separate: AI, Sustainability, Legal and Economics. Each area has its own feed from curated sources, a daily briefing, a weekly audio round-up and a monthly video briefing; AI's briefing is written from the sources and topics the user chooses. Every item shows where it comes from and links to the original. Legal and Economics carry news and commentary from public sources, not legal or financial advice.

Four areas · Daily briefings · Weekly audio · Monthly video · iOS + Android

Topos Live deployment

Tap-first nearby-place finder where travel time replaces distance as the search dimension. Two taps — what you need plus a travel time, on foot or by car — return matching places on a map and a list with opening status, and hand off to the maps app for navigation. A free account adds curated situations, favourites and multi-stop routes that can be saved and run again. Live on the App Store and Google Play.

Time-based nearby search · Places and travel times · iOS + Android

Infrastructure, frameworks & research — 05

The machinery behind the apps.

READ THE SPEC → PROVE THE DIVERGENCE
ADL — Application Description Language Published spec

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.

v1.5 specification · 6 target stacks · Multi-model validation

Read the ADL whitepaper →
cross-review-mcp Open source

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.

Node.js · MCP protocol · Multi-provider (OpenAI / Gemini / DeepSeek / Mistral)

HUNTER Research framework

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).

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 run through five gates before any divergence claim is made.

Python · Multi-language probe harness · Spec-differential testing · Deterministic oracles

Read the HUNTER capability brief →
HomeSentinel Edge computing

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.

Python · Multi-node agent fabric · Local LLM (Ollama) · MIT licensed

LINETBOOT-USB IoT product

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.

POSIX Shell · Node.js · Python · PowerShell · iPXE · GRUB2 · Docker · 88 requirements delivered

Why it compounds The moat

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.