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Use cases

Reference architectures for real AI workloads

Each use case connects runtime layers, contracts, failure handling, security, observability, evaluation, and operating metrics.

Enterprise Knowledge Assistant

A governed retrieval-and-generation system that answers from authorized enterprise sources, preserves tenant and data-classification boundaries, and exposes evidence for every material answer.

Platform architects, AI infrastructure engineers, security and governance leaders, and application developers

Coding Agent

A repository-scoped agent that can inspect code, propose patches, run bounded commands and tests, and produce reviewable evidence without receiving unconstrained workstation or credential access.

Application and agent developers, developer-platform teams, security engineers, and engineering managers

Customer-Support Action Agent

A support system that may read account context and propose or perform bounded actions while preserving customer identity, authorization, idempotency, human review, and customer-visible evidence.

Support-platform architects, application developers, security and governance teams, and technical product leaders

Scientific or Analytical Workflow

A reproducible analytical runtime that binds datasets, code, environments, intermediate artifacts, citations, validation, and result provenance into a reviewable execution record.

Researchers, technical analysts, ML systems engineers, data-platform teams, and research-governance leaders

Local Private Assistant

A device-local assistant that keeps model execution and storage on the user’s device by default, with explicit capability, update, and hosted-fallback boundaries.

Developers evaluating local runtimes, privacy architects, desktop engineers, and technical decision-makers

Browser AI Application

A progressively enhanced web application that executes eligible models in the browser while accounting for capability variance, model delivery, caching, privacy, offline behavior, and server fallback.

Web developers, ML systems engineers, privacy architects, and developers evaluating browser runtimes

Edge or Mobile System

An on-device runtime that packages, accelerates, updates, monitors, and safely degrades models under memory, battery, thermal, connectivity, and hardware-backend constraints.

Mobile and embedded engineers, ML systems engineers, edge-platform architects, and technical product leaders