AI-native mobile • operational workflows
AI in the workflow — not a chat demo.
We embed AI into real operator flows: capture → triage → decision. Each touchpoint has clear inputs/outputs, fallbacks, and measurable acceptance criteria.
SpecificDefined tasks: extraction, routing, assist, automation
MeasurableTest sets, accuracy targets, latency budgets
GovernedExplicit data boundaries and audit-friendly hooks
What you receive
- Cross-platform app (React Native or Flutter) with production discipline
- AI touchpoints integrated into the flow (not a separate “AI screen”)
- Acceptance criteria + test checklist + instrumentation
- Governance notes: sensitive paths, controls, and ownership
How we de-risk
- Week 1: workflow map, risks, prototype, fixed scope
- Weeks 2–4: build + internal pilot readiness
- Evaluation: test sets, failure modes, fallback strategy
- Governance: explicit boundaries, audit-friendly logging hooks
FAQ
Short answers — no slide-ware.
What does “AI in the workflow” mean?
AI is placed at specific steps with clear I/O, fallbacks, and acceptance criteria.
What are typical AI touchpoints?
Extraction, classification, suggestions, routing, and operator assist — measured end-to-end.
How do you avoid “AI theatre”?
We define success metrics, test sets, and stop conditions. Reliability wins.
Do you support on-device AI?
Yes — when privacy, latency, or offline constraints require it.