Tanzil

MUKHT

Tanzil

MUKHT

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MUKHT

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Designing a system where disagreement becomes training data. Not noise.

WordSmart Annotator Workspace

The question wasn't "how do we annotate Indic content at scale?"

It was "why does every existing approach produce culturally flat data?" Answering that question — and designing around the real answer — is what this project was.

MY ROLE

Lead UX/UI Designer (research, IA, interaction, visual system, prototyping)

COMPANY

Wordwise Language Labs

TEAM

3 designer, 4 engineers, 1 PM,

1 AI-ML engineer.

TURNAROUND TIME

16 weeks, 8 two-week sprints · Figma, FigJam, Maze, Dovetail, Notion, React + Tailwind

MY ROLE

Lead UX/UI Designer (research, IA, interaction, visual system, prototyping)

TEAM

3 designer, 4 engineers, 1 PM,

1 AI-ML engineer.

COMPANY

Wordwise Language Labs

TURNAROUND TIME

16 weeks, 8 two-week sprints · Figma, FigJam, Maze, Dovetail, Notion, React + Tailwind

MY ROLE

Lead UX/UI Designer (research, IA, interaction, visual system, prototyping)

TEAM

3 designer, 4 engineers, 1 PM,

1 AI-ML engineer.

COMPANY

Wordwise Language Labs

TURNAROUND TIME

16 weeks, 8 two-week sprints · Figma, FigJam, Maze, Dovetail, Notion, React + Tailwind

What is WordsSmart?

WordSmart is a cultural annotation platform that produces the training data India's sovereign AI models actually need — not grammatically correct Indic text, but regionally authentic content verified by matched cultural experts, with a built-in audit trail for government compliance.

impact

Pilot v0 → v1.3

Improvements at a glance

Metric	Before	After	Change
Time to first submitted annotation (new annotator)	41 min	12 min	−71%Improved
Consensus rate (reviewers within ±1 CAI)	52%	81%	+29 ptsImproved
Annotations rejected at QA	23%	7%	−16 ptsImproved
Median time per text artefact	6 min 40 s	3 min 05 s	−54%Improved
Week-4 annotator retention	38%	74%	+36 ptsImproved
Disputes resolved without escalation to a linguist	44%	86%	+42 ptsImproved

overview

India trains AI on data that is linguistically correct and culturally hollow. A model can transliterate “ಬಡಗುತಿಟ್ಟು” into “Badagutittu” and still have no idea it names a northern Yakshagana style with its own costume grammar, drumming and role conventions. That gap — fluency without fidelity — is what WordSmart exists to close.

WordSmart is an annotation workspace where native speakers tag cultural meaning, not just entities. Annotators mark idioms, proverbs, sarcasm, code-mixing, festival and ritual references, folklore, caste and religious sensitivity; they score each artefact on a Cultural Authenticity Index (CAI, 1–10); and every artefact passes through a 2–3 reviewer consensus before it can enter a training set.

THE ONE-LINE VERSION

I designed a judgement tool, not a labelling tool — and the whole product changed once I accepted that difference.

What I shipped

A three-modality annotation workspace (text spans, image bounding boxes, audio waveform regions) built on one shared annotation grammar, so an annotator learns the interaction once.
A Cultural Authenticity Index scoring system with an inline rubric, forced evidence capture and confidence self-reporting.
A consensus review surface that compares reviewers side by side, isolates the exact point of disagreement and requires a written resolution.
An AI assistance layer — tag suggestions, draft CAI with reasoning, pre-submission quality check, disagreement explainer, guidelines chat — deliberately positioned as advisory, never authoritative.
A quality leaderboard reframed around consensus alignment rather than volume, after volume ranking produced measurable harm in testing.
A public landing page that had to sell credibility to two very different audiences: a ministry procurement officer, and a 24-year-old Kannada speaker who saw the link on Instagram

Too Much Reading? Skip to Design!

Previous work flow

What do Geniuses & Technicians say?

"Here's a great little video which is maybe 5sec long, rather than me having to scrub through a video, I don't know how many 3 min or whatever to find a specific part of it".

"You're breaking down the barrier between articles — the title of the article no longer be a hard requirement to actually getting the content".

"It's kind of a visual feedback, like click point saying skip-to-this-part — like chapter markers".

"Not everybody have the capability to translate the technology to normal, interactive or relatable conversation".

"Sometimes I can't get to them — so you have to have a smart little AI assistant to help out in those situations".

"The video for, like, removing the glue on iPhones, it's not really that credible when it comes to the actual real-life interaction with a device that has, like, a one or two year old display".

"Ability to save it in the section, if you Bookmark it on your section... That could be really nice".

Where the Problem lies

Technicians waste time switching between multiple tools and searching through complex documentation, making repairs slower and increasing the risk of missed steps or errors.

Fragmented Access to Knowledge

Technicians switch between multiple systems - GSX, Atlas, videos, PDFs - to find a single answer.

Leads to context switching and workflow interruptions

Technicians switch between multiple systems - GSX, Atlas, videos, PDFs - to find a single answer.

Leads to context switching and workflow interruptions

Cognitive Overload During Repails

Critical data (like warnings, tool specs, or calibration steps) are buried in long-form documents.

Results in missed steps, slower task completion, or over-reliance on memory.

Critical data (like warnings, tool specs, or calibration steps) are buried in long-form documents.

Results in missed steps, slower task completion, or over-reliance on memory.

Inconsistent User Experience Across Tools

Information is presented differently across interfaces, what's clear in one tool is buried in another.

Reduces efficiency and confidence during live repair.

Information is presented differently across interfaces, what's clear in one tool is buried in another.

Reduces efficiency and confidence during live repair.

Limited Real-Time Support

When something unexpected occurs, technicians must escalate or search manually.

Causes delays, downtime, and frustration in high-volume repair environments.

When something unexpected occurs, technicians must escalate or search manually.

Causes delays, downtime, and frustration in high-volume repair environments.

The users we are Designing for?

Specialists, Lead Genius, Genius and Technicians

[Click on images to expand]

Decoding User-stories

Before any wireframes, every interview quote was synthesized into an opportunity matrix, user need, pain point, concept, and UX opportunity per row. Each story produced 2–5 response-pattern options, letting me evaluate the response system before committing to layout.

Image 1 of 6

Final Conclusion

"Build an

AI Powered, integrated Chat experience

for

APPLE Geniuses and Technicians Worldwide

APPLE

APPLE Genius

with a

and Technicians Worldwide

with a

Intent to organize fragmented Articles

Geniuses and Technicians Worldwide

with a

Intent to organize

Intent to organize fragmented Articles and Information across GSX

fragmented Articles and Information across GSX

and Information across GSX

and

deliver instant, synthesized answers,

deliver

eliminating context-switching and accelerate decision-making

eliminating context-switching and

in high pressure work flow".

in high

instant, synthesized answers,

pressure work flow".

accelerate decision-making

in high pressure work flow".

Who we are designing for?

What do we want to achieve!

Where it collects the required information

Why it is absolutely required

Integration of Features

Ideating structural directions

The chosen direction is a hybrid: conversational entry escalating into a video-anchored workspace with an overlay panel system once an active repair begins.

Image 1 of 4

Split screen (watch + ask, zero switching, costs horizontal space)

Edge-cases

An interface can be perfect and the product still fails if the AI behaves badly at the edges.

Working with the AI/ML engineers on the pod, we specified failure modes — 13 conversational, 2 diagnostic, each with a recommended system behavior, message copy, and lo-fi recovery flows.

An interface can be perfect and the product still fails if the AI behaves badly at the edges.

Working with the AI/ML engineers on the pod, we specified failure modes — 13 conversational, 2 diagnostic, each with a recommended system behavior, message copy, and lo-fi recovery flows.

An interface can be perfect and the product still fails if the AI behaves badly at the edges.

Working with the AI/ML engineers on the pod, we specified failure modes — 13 conversational, 2 diagnostic, each with a recommended system behavior, message copy, and lo-fi recovery flows.

Image 1 of 8

The Skeleton transformation

Protype Glimpse

Free-text stays available the whole time — structure is offered, never forced.

Final Design

What shipped: a familiar chat shell, with a repair workspace underneath it.

Image 1 of 6

Thank You…

Hope you like case-study

Testimonials

Design Lead
Apple
Tanzil took a highly technical, messy user problem and turned it into a clean, frictionless experience. He delivered ahead of schedule and helped us hit our adoption goals instantly.
Founder
Wordwise Language Labs
WORKING WITH Tanzil WAS SMOOTH FROM START TO FINISH. HE UNDERSTOOD the vission and User NEEDS QUICKLY AND BUILT A SOLID, RELIABLE PRODUCT.
Product Manager
NeoTech
SUPER RELIABLE AND DETAIL-ORIENTED. he can TAKE OWNERSHIP AND SHIP HIGH-QUALITY WORK WITHOUT HAND-HOLDING.

BEHANCE

MEDIUM

Bengaluru, India

00:36

IST

Testimonials

Design Lead
Apple
Tanzil took a highly technical, messy user problem and turned it into a clean, frictionless experience. He delivered ahead of schedule and helped us hit our adoption goals instantly.
Founder
Wordwise Language Labs
WORKING WITH Tanzil WAS SMOOTH FROM START TO FINISH. HE UNDERSTOOD the vission and User NEEDS QUICKLY AND BUILT A SOLID, RELIABLE PRODUCT.
Product Manager
NeoTech
SUPER RELIABLE AND DETAIL-ORIENTED. he can TAKE OWNERSHIP AND SHIP HIGH-QUALITY WORK WITHOUT HAND-HOLDING.

BEHANCE

MEDIUM

Bengaluru, India

00:36

IST

Testimonials

Design Lead
Apple
Tanzil took a highly technical, messy user problem and turned it into a clean, frictionless experience. He delivered ahead of schedule and helped us hit our adoption goals instantly.
Founder
Wordwise Language Labs
WORKING WITH Tanzil WAS SMOOTH FROM START TO FINISH. HE UNDERSTOOD the vission and User NEEDS QUICKLY AND BUILT A SOLID, RELIABLE PRODUCT.
Product Manager
NeoTech
SUPER RELIABLE AND DETAIL-ORIENTED. he can TAKE OWNERSHIP AND SHIP HIGH-QUALITY WORK WITHOUT HAND-HOLDING.

BEHANCE

MEDIUM

Bengaluru, India

00:36

IST