Applied AI Architecture
Put intelligence where it belongs.
RiteUp AI architects production AI systems across the intelligence boundary — determining what belongs on-device, what requires frontier models, and how the two work together.
The Intelligence Boundary
Not every problem belongs in the cloud.
The architecture begins by deciding where intelligence should execute — based on privacy, latency, capability, cost, connectivity, and control.
Local Intelligence
On Device
Intelligence close to the user, their data, and the application experience.
- Private by architecture
- Low-latency inference
- Offline capability
- Personal context
- Predictable inference cost
- Apple silicon acceleration
AI
Architecture
Scaled Intelligence
Frontier
Intelligence that benefits from large-scale reasoning, broad context, orchestration, and specialized models.
- Advanced reasoning
- Large context windows
- Agent orchestration
- Tool execution
- Specialized models
- Enterprise-scale workflows
What We Build
Production systems, not demos.
Apple is where we practice the discipline — not the boundary of it. We build private, high-performance AI systems that respect device constraints while meeting enterprise requirements.
On-Device Intelligence
Foundation Models, Core ML, Natural Language, Vision, Speech, and private local inference optimized for Apple silicon and native applications.
Hybrid AI Architecture
Device, private infrastructure, and frontier models working together through intentional routing, policy, evaluation, security, and runtime architecture.
Agentic Systems
Production agents with identity, tools, memory, model routing, evaluation, observability, governance, and clearly defined boundaries of authority.
Native Apple Products
End-to-end iOS and macOS development using Swift and SwiftUI — from system architecture and interface design to AI integration, testing, deployment, and App Store delivery.
Data, Governance & Sovereignty
Master data management, data quality, provenance, policy, evaluation, and sovereign deployment architecture that keeps organizations in control of their data and AI systems.
Selected Work
Architecture made tangible.
We build systems that test the architecture against real users, real data, real security boundaries, and real production constraints.
AI Security
Margah Gateway
Vendor-neutral generative AI security and governance infrastructure for prompt inspection, policy enforcement, redaction, model routing, incidents, and auditability.
Private AI
PromptGuard
Local-first prompt security designed to detect prompt injection, sensitive information, secrets, obfuscation, and exfiltration before information reaches an AI system.
Native Learning
CipherChallenge
A native Apple learning environment for exploring classical cryptography through daily challenges, interactive solvers, historical cases, and guided instruction.
Personal Intelligence
HomeZEN
A local-first household intelligence system combining property data, assets, maintenance, documents, recommendations, and contextual AI into one native experience.
How We Work
From placement decision to production system.
We start with the intelligence boundary, then design the system that actually runs.
Diagnose the boundary
Determine what should remain local for privacy, latency, cost, control, or offline operation — and what genuinely requires frontier-scale capability.
Architect the system
Define models, data, runtime, identity, routing, security, evaluation, governance, and ownership across every part of the architecture.
Build & ship
Implement the production system with native experiences, hardened infrastructure, measurable behavior, and clear operational boundaries.
Operate & improve
Instrument the system with evaluation and observability loops so performance can improve as models, users, data, and requirements evolve.
Why RiteUp
Systems experience applied to modern AI.
RiteUp AI combines enterprise architecture, data, cloud, native application development, and applied AI. The focus is not simply connecting an application to a model — it is designing the surrounding system so the intelligence remains secure, observable, maintainable, and useful in production.
Start Here
Where should your intelligence live?
Whether you are designing an on-device AI product, evaluating a hybrid architecture, modernizing an existing system, or determining where AI fits at all — start with the boundary.