Stak Microtasks
Designing how people power AI systems
Stak Microtasks powers AI workflows with human precision.
Clear task patterns and transparent verification reduce errors, while Lightning payouts connect effort and reward instantly. Designed for global scale, short sessions, low-end devices, and evolving job types.
Role
Founding Product Designer
Engagement
2019 ⸺ 2025
Company
Stakwork, USA
Funding
Venture-backed
Proven in production
Elections ballot verification
Scanned ballots are processed in Stak Workflows for AI extraction. Each value is then validated in Microtasks using a three-worker consensus to ensure audit-grade accuracy.
The Challenge
Human clarity inside a machine loop
Stak Microtasks had to make human judgment fast, clear, and scalable. Workers needed to understand tasks immediately, complete them with confidence, and trust the result. At the same time, the system had to support very different kinds of work without becoming harder to learn.
01.
Tasks remain obvious at a glance.
02.
New job types build on familiar patterns.
03.
Verification, progress, and payout stay visibly connected.
04.
The system works across literacy levels, cultures, and bandwidth.
01 — Home
A clear entry point into work
The homepage turns work into a simple loop: pick a job, see time and payout, then start. Balance, level, and progress make earnings feel tangible and keep the system easy to return to.
02 — Tutorials
Learning for Precision
Tutorials clarify expectations before speed begins. Visual examples reduce ambiguity and make the system easier to use across languages, literacy levels, and job types.
On one production task, my redesign of the interface and visual tutorial increased internal accuracy from 54% to 99%.
03 — Rewards
Micro-rewards, zero uncertainty
04 — Jobs
Job patterns
A flexible framework built to support many kinds of human input.
Each solves a different problem, using the same underlying structure.
One system
for every job
I designed a shared layout and common component library that kept each job type familiar while making new ones faster to design, build, and launch. New job types could launch in hours, not weeks, and that flexibility proved itself in production.
Detect & label
Precision tasks need clear expectations and minimal friction. Visual anchors and scoped inputs keep the work fast and accurate.
Guided photo capture
Some jobs need evidence, not clicks.
Named slots help make submissions complete and consistent.
Research-based verification
Trusted links in, one field out. Fast to complete, easy to review.
Audio verification
Short clips in. One clean label out.
The interface keeps attention on the listening decision.
Highlight keywords
Sentences in. Key terms marked out.
The task stays focused on meaning, not interface mechanics.
Boolean
Fast yes / no decisions for high-volume review. Minimal training, reliable QA.
05 — withdrawal & privacy
Work privately. Withdraw instantly.
Workers can participate with minimal personal data and withdraw sats in seconds.
That keeps reward close to effort and makes trust part of the experience.
Outcomes
A framework built to expand
New job types could be introduced without redesigning the core experience.
Faster task recognition
Clear framing reduced hesitation at the point of work.
Lower onboarding friction
Tutorial-first guidance reduced the need for written explanation and repeated support.
A stronger trust loop
Accepted work, visible progress, and instant payouts kept effort and reward connected.
More stability as the platform grows
Reusable patterns helped the platform absorb new workflows without losing clarity.
