Frequently Asked Questions

We focus on five areas: on-device AI, agentic systems, model optimization, private AI architecture, and practical AI adoption. Each engagement starts with a concrete production constraint or operating goal, not a generic promise to add AI.

On-device AI (also called edge AI) runs machine learning models directly on the user's device, such as a phone, tablet, IoT sensor, or embedded system, instead of sending data to the cloud. Use it when you need real-time speed, data privacy, offline capability, or lower cloud costs. Common use cases include mobile apps, healthcare, finance, and industrial IoT. We specialize in ExecuTorch and React Native ExecuTorch for cross-platform on-device deployment.

An agentic system is software where an AI agent runs a business process end to end: reading inputs, deciding what to do, and acting through tool calls against your systems. Traditional automation wins when every step follows a clear rule; agents win when inputs are messy, like free-text emails, inconsistent documents, or requests that need interpretation before routing. Production-grade agents combine both: deterministic code for steps with a correct answer, LLM reasoning only where judgment is needed, plus guardrails, evaluation suites, and human approval on consequential actions.

Private AI means designing explicit boundaries for data, models, and compute. Depending on the threat model, that can mean on-device inference, an on-premise deployment, isolated cloud infrastructure, or a hybrid architecture. It is an architecture decision, not a compliance badge.

We work with PyTorch, TensorFlow, JAX, and ONNX for model development. For on-device deployment, we specialize in ExecuTorch and React Native ExecuTorch for cross-platform mobile and edge inference. Our team is proficient in Python, Rust, TypeScript, Swift, and Kotlin. We deploy across mobile (iOS/Android), web, IoT, and embedded systems using Docker, Kubernetes, and modern cloud infrastructure.

Timelines depend on the system boundary, deployment target, data access, and evidence required before launch. A focused audit or prototype is shorter than a production implementation or organization-wide rollout. After discovery, we propose milestones with explicit success criteria.

We begin by defining the operating problem, system boundary, constraints, and evidence that would make the project worth shipping. From there we recommend a focused audit, prototype, implementation, or adoption plan with clear ownership and milestones.

We work with organizations of all sizes. Startups get production-ready AI architecture from day one so they can ship MVPs faster. Scale-ups get infrastructure optimization for growth. Enterprises get strategic AI adoption, team enablement, and organizational transformation. Our engagement models range from fixed-scope projects to ongoing retainers and embedded team augmentation.

No consultancy can make that guarantee from architecture alone. Compliance also depends on contracts, operating procedures, access controls, staff behavior, and legal interpretation. We design the technical boundary, document data flows and controls, and work with your security and legal specialists on the evidence they need.

We assess the work that could benefit from AI, the systems and data involved, and the team's ability to operate the result. Then we choose a small number of credible opportunities, define validation milestones, run focused prototypes where needed, and establish ownership, evaluation, and governance practices.