Edge and On-Device AI: When to Move Inference Off the Cloud
Latency, privacy, connectivity and cost decide where inference belongs. A practical framework for splitting AI workloads between device, edge and cloud.
Product Design
Interfaces designed from research, validated with prototypes and delivered as systems your developers can actually build.
Beautiful is not the goal — obvious is. We design interfaces where users never have to think about where to click next, grounded in research about how they actually work and validated with prototypes before a line of code is written.
Because designers and engineers sit on the same team, every screen we design is buildable, performant and delivered as a component system — not a folder of disconnected mockups.
Research is sized to the decision, not to a methodology: sometimes five user interviews and a competitor teardown answer the question; sometimes a clickable prototype in front of real users for a week is worth a month of debate. What we do not do is design from taste alone and call the argument settled.
Design debt behaves exactly like technical debt — every inconsistent button and one-off screen makes the next feature slower. Delivering as a token-based component system keeps the product coherent as it grows, and it is why our design work speeds engineering up instead of generating a queue of "can you check Figma" questions.
User interviews, journey mapping and competitive analysis that ground design in evidence.
Clickable prototypes that validate flows with real users before development.
Component libraries with tokens and documentation — consistent at any scale.
Heuristic and data-driven reviews that find where your product leaks users.
Specs, assets and tokens engineered for a frictionless build.
Flows built around the action that matters — signups, purchases, retention.
Prototype testing catches wrong turns while they cost hours, not sprints.
Component libraries keep the product consistent as it grows and teams change.
Designers and engineers on one team — what is designed is what ships.
Case study · Consumer apps
Launching a consumer fitness app to both stores
8 wks MVP to store · 4.8★ Average rating · +60% D30 retention
Latency, privacy, connectivity and cost decide where inference belongs. A practical framework for splitting AI workloads between device, edge and cloud.
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How we work
Requirements gathering, technical feasibility and architecture planning — we define the fastest path to measurable outcomes.
Sprint-based design and engineering with continuous integration and daily communication. No bloat — rapid, transparent, iterative delivery.
Rigorous QA, smooth deployment, performance monitoring and ongoing maintenance — a product engineered to grow.
UX audits start around $1k–$4k; full product design from research through design system typically runs $7k–$22k depending on scope. Fixed quotes after discovery.
Yes — usually starting with a UX audit that identifies the highest-impact fixes, then redesigning incrementally so users are not shocked and metrics stay measurable.
The same metrics as the business: conversion, activation, task completion time, support-ticket volume. We instrument before and after so the impact is visible.
Absolutely — we extend existing brands into product interfaces, or evolve them where they hold the product back, always with your approval at each step.
A UX audit lands in 1–2 weeks. Full product design — research, flows, UI and a handoff-ready system — typically runs 4–8 weeks, deliberately a step ahead of engineering so development never waits on screens.
You have a market even before you have users: competitor products to tear down, prospects to interview, and prototypes that can be tested with people who match your target audience. Pre-launch research is about finding the flow users already expect — which is cheaper to learn before the build than after.
A full documented system, yes. A disciplined token-and-component foundation, no — it costs little at the start and prevents the redesign-every-quarter drift that eats early-stage velocity. We size the system to the team: enough structure to stay consistent, not so much that maintaining it becomes the job.
Tell us what you're building. If it ships software, we can help.