ARJIA
Introducing Nyx
Screen-Aware Assistance
Designed to use on-screen context, OCR, and visual highlights to guide users through tasks.
On-Device, Local-First AI
Designed for local inference, offline use, and greater user control on Apple hardware.
Built for the Apple Ecosystem
A macOS-first project, with iPhone and iPad support planned.
Native Apple Stack
The prototype is being built with Swift, SwiftUI, AppKit, Gemma, and llama.cpp.
Our lineup
A control layer between AI intent and real-world change.
CLU evaluates AI-proposed source changes against local policy before anything is approved or applied.
Each decision binds the proposal, policy, source state, and rollback evidence into a deterministic record—keeping eligibility, approval, and execution deliberately separate.
An adaptive ETH trading engine where every model must earn control.
ARES transforms Ethereum market data into sequence-based trading signals, then evaluates them through chronological walk-forward testing with fees, slippage, and hard risk limits.
One validated model serves as champion. New challengers run through recurring search and validation cycles, but none can replace the incumbent simply because it is new. Promotion happens only when a candidate passes every gate and proves stronger across performance, drawdown, costs, turnover, and stability.
Let’s take you where you want to go.
Built fast. Tested harder.
From Concept to System
Ambitious ideas are broken into clear requirements, working prototypes, and reviewable technical systems.
Tested
Reversible
Evidence Before Claims
Architecture, tests, benchmarks, versioned artifacts, and documented limitations keep the work defensible.
AI-Accelerated. Human-Directed
Modern AI tools accelerate research, development, debugging, and iteration—but architecture, judgment, safety boundaries, and final review remain human-controlled.
Open to Build
Available for software engineering roles, applied AI collaborations, machine-learning infrastructure, product development, and selected client work.
What we help teams build and scale.
AI Systems
Production-focused AI applications, agents, and intelligent workflows built around real business needs—not demo-grade prototypes.
LLM Infrastructure
Memory, context, evaluation, retrieval, and governance systems that make language models more reliable, controllable, and useful.
Quantitative Systems
Research engines, market-data pipelines, backtesting infrastructure, and execution tooling designed to reduce false confidence.
Local-First Software
Private, local-first software designed to keep critical work on the user’s device.
Product Engineering
End-to-end product development across architecture, backend systems, interfaces, APIs, automation, and deployment.
AI Governance
Approval controls, provenance, audit trails, rollback, and evaluation for responsible AI deployment.