Everyone can speak.
Not everyone knows what to say.
I make sure your product does.

UX Writer and Content Designer with 11 years in SaaS.
I work on the decisions behind the words.
What to say, how to say it, and what to leave out.

See what I offer → View my work

How we can work together

Free

Start with a free UX audit

I'll click through your product just like a user would to spot where things are confusing or losing people. Then, I'll send you my plan of action. No strings attached. You'll get a short PDF with my findings and next steps within 3 business days.

Get free audit

From €500

✓ I work with your team to understand the goals of the project
✓ I write the copy for the flow, launch, or screen
✓ I make sure it fits the rest of your product, so it doesn't stand out
✓ I leave notes on what changed and why, so your team understands the copy decisions

Get in touch

1–4 weeks · €1,800–4,500

✓ I get to know your product and brand, and audit how it sounds
✓ I build the style guide: how to write clearly and consistently, words to use and avoid, and patterns for common components
✓ I include examples for high-stakes situations, like errors and onboarding tooltips, and areas where your brand can show some personality
✓ I make sure your product sounds like your brand, but never like a marketing campaign

Your product speaks clearly, consistently, and in your voice.

Get in touch

5–6 weeks · €7,000–9,500

✓ Everything in Content guidelines + voice consistency (style guide, glossary, patterns, brand voice examples)
✓ I prepare the prompt and knowledge for an AI assistant, so your team gets an AI UX Writer trained on your guidelines
✓ I run a workshop so your team knows how to prompt it and judge the output

Your product speaks in your voice, with guidelines ready for AI and your team trained to use it.

Get in touch

Selected work

Featured

Custom AI model · Text

UX Copy Cat: scaling UX writing across 20 teams

Used across 20 teams →

AI voice · Text

Teaching Text's AI Agent
to sound like Text

Brand voice consistency Conversation design

→

Content design · Text

Redefining the role of empty states

Higher setup completion →

Contextual help · Syncron

Making a jargon-heavy product easier to understand

Improved product adoption →

Let's work together.

nataliamialik@gmail.com LinkedIn ↗

Natalia Mialik — UX Writer and Content Designer

About

Hi, I'm Natalia.

I started out as a customer support specialist, which is where I learned what frustrates users, and what makes them stay. That turned out to be the heart of UX writing, and I was already doing it before I knew it was a thing.

Since then I've written for all sorts of apps, from supply chain software at Syncron to an AI-first customer service platform at Text, where being the only writer for 20 teams pushed me to really learn AI. I now use it to scale UX writing, move faster on first drafts, and spend my time on the decisions behind the words.

Outside of work, I'm a digital nomad, discovering new cultures and dissecting languages. I've also built a community of over 30,000 women around eating disorders, so one day I'd love to work on a wellbeing app.

See what I offer → Get in touch

← Back

Custom AI model · Text

UX Copy Cat: scaling UX writing across 20 teams

Featured Used across 20 teams

Background

At Text, I was the only UX writer working with 20 product teams. There were no plans to grow the Content Design team.

Problem

One writer across 20 teams meant I was becoming a bottleneck. Teams started pushing things to production without my input: promo banners that pushed people away instead of encouraging them to try a feature, features that sounded overwhelming instead of enticing.

The style guide was comprehensive but used mostly by me and a handful of designers. Everyone else was in a rush to ship.

Thought process

Guidelines tell you how to write what you've already decided to say, but they don't teach you how to think like a UX writer.

Workshops were another idea, but from previous experience and the rush to ship, I knew not everyone would join. Plus, there's only so much you remember from a workshop.

Since I was using AI every day to help with creative UX thinking, I decided to create a custom GPT model and share it with the teams. Designers were already using AI to write draft copy, but their outputs were worse than mine because they weren't copy specialists. That's how I knew I had to teach them how to prompt and how to judge the output.

Solution

UX Copy Cat: a custom AI model trained on Text's voice, tone principles, and component patterns, with guidelines uploaded so it could help anyone make the right copy decisions, not just write words.

UX Writing Fundamentals: a workshop covering not which words to use, but how to think like a UX writer. What needs to be said, and how it needs to be said. It included exercises for applying the principles and distinguishing UX problems from copy problems, as well as prompting techniques for getting better outputs.

Slides from the UX Writing Fundamentals workshop:

Workshop slide: Using GPT to check copy clarity

UX Copy Cat in action — send the prompt to see what it suggests:

You

This is the current AI Agent report empty state, but it isn't really actionable at first glance. How can I improve it to encourage users to set up AI Agent?

AI Agent resolutions

Nothing to report in this period
Configure AI Agent to see data here.

Set up AI Agent

UX Copy Cat

Outcome

20+ designers across 20 teams using UX Copy Cat instead of generic copy on designs.
Copy arriving to me 60–90% ready.
Significantly fewer screens shipping with copy that worked against the product.

Have a product that needs to speak clearly?

Get in touch See what I offer

← Back

Brand voice · AI · Text

Teaching Text's AI Agent
to sound like Text

Brand voice consistency Conversation design

Background

Text's AI Agent supports customers directly in chat. It handles first contact, answers questions, and helps users get set up.

Problem

The Agent's responses defaulted to what AI defaults to: formal, apologetic, and extremely polite. None of that matches Text's personality: direct, savvy, proactive, and perceptive. The gap between the brand and the product was visible in every conversation.

AI Agent before — overly formal response

AI Agent before — padded welcome-back response

Thought process

I looked at Text's existing brand voice guidelines. The problem was they didn't translate into rules a language model could actually follow. "Be perceptive" doesn't tell an AI what to do when a customer asks a compliance question. What the Agent needed was clear instructions on what good sounds like.

Solution

I wrote a prompt with 13 rules. Each rule targets a specific failure pattern: a direct instruction, a right example, a wrong example. Simple enough for a language model to follow. Clear enough for a stakeholder to review without a UX writing background.

Talk in first person

Use first person singular. Never refer to yourself in the third person or call yourself a bot.

✓ "I can help with that."✗ "The AI Agent can help." / "Our bot can help."
Keep the greeting brief

A short hello or welcome back is enough.

✓ "Hi! What can I help you with?"✗ "It's great to have you here. We're glad to see your enthusiasm."
Ask directly

Drop "please" and "kindly."

✓ "What's your name and email?"✗ "Could you please provide your name and email?"
Speak as Text, not "we"

Avoid using "we," "our," or "us."

✓ "Your data stays private." / "Text keeps your data private."✗ "We protect your data with our security standards."
Answer the question they have right now

Don't front-load company history.

✓ "Sure, let's get you started. First, …"✗ "Welcome to Text. Text is a company that provides software solutions…"
Make one request at a time

✓ "What's your name and email?"✗ "Describe your use case in as much detail as you can, and also please provide your name and email."
Match your tone to what the customer is doing

Arriving → warm. Doing something → plain and step-by-step. Stating a limit → matter-of-fact.

✓ "I work in English. Switch to English and I'll take it from here."✗ "Please note that our bot operates only in English. Kindly continue in English so we can help you effectively."
Keep warmth short and real

✓ "Got it. Thanks."✗ "It's so wonderful to hear from you, and we truly appreciate you reaching out today."
State what's true and strong

Don't volunteer gaps or qualifiers.

✓ "Text is SOC 2 audited."✗ "Text is SOC 2 audited, with ongoing work toward full compliance."
No em dashes

Use a comma or split into two sentences instead.
Format so it's easy to scan

Short paragraphs and lists for steps. Never one long block.
Write the feature name as "AI Agent"

Capitalized, proper noun, no article.

✓ "To turn off AI Agent, go to Team → AI Agents."✗ "To disable the AI agent…"
Swap technical words for plain ones

turn off (not disable) · turn on (not enable) · removed (not revoked) · incorrect (not invalid) · happened (not occurred) · setup (not environment)

Example model responses showing the gap between the original output and the guidelines:

Example model responses, before and after

Outcome

Guidelines ready for implementation, with examples that made the voice gap visible to stakeholders.
A reusable foundation for AI Agent conversations (I left before launch).

Have a product that needs to speak clearly?

Get in touch See what I offer

← Back

Content design · Text

Redefining the role of empty states

Higher setup completion

Background

Text is an AI-first customer service platform. Getting it to work takes setup: connecting channels, feeding the AI Agent knowledge, learning where things are.

Problem

Users coming into Text for the first time kept hitting dead ends. Empty states acknowledged the absence of data, without any explanation or next steps. Many users gave up before reaching their first aha moment.

Thought process

Together with a designer, we mapped out every empty state in the product. I asked the same questions for each:

Why is this screen empty right now?
What can be done to fill it with data?
What do users need to know here?
What is their current understanding of what's happening?

At first, the answers fell into three categories. But "no data yet" turned out to cover two fundamentally different situations: one where the user could take action immediately, and one where they were waiting for data but could still do something to speed it up. That led to four distinct content patterns.

The role of empty states evolved from reporting missing data to guiding users through onboarding.

Solution

I defined 4 reusable content patterns, each designed around a different user situation. Rather than treating every empty state the same, each pattern had a different goal and guided users accordingly.

Onboarding

Header: Show what users can achieve with this feature
Description: Explain what to do and why it matters
CTA: Lead toward setup or exploration

No data yet (system-driven)

Header: State what's missing without implying a problem
Description: Explain what needs to happen for data to appear here
CTA: Suggest a possible next step

No data yet (user-driven)

Header: Encourage to add the first item
Description: Explain the value of adding it
CTA: Create / Add / Upload / Connect + item

No data anymore

Header: Say there's nothing left to do right now
Description: Explain that all tasks are done
CTA (optional): Whenever possible, offer orientation

Here's one empty state, redesigned:

What we started with

The original empty state — it only reports that there's no data.

The original empty state, which only reports there's no data

More value-oriented

Reframed to lead with the value and guide the user toward setup.

Design matched to the copy

The final version, where the visual design lands the message.

The final empty state, with visual design aligned to the copy

Outcome

Empty states became part of the onboarding experience instead of dead ends.
Users understood where they were in setup, why data wasn't there yet, and what to do next.

Have a product that needs to speak clearly?

Get in touch See what I offer

← Back

Contextual help · Syncron

Making a jargon-heavy product easier to understand

Improved product adoption

The screenshot is a mock. Syncron's product is under NDA and cannot be published.

Background

Syncron is a spare parts management platform used across the supply chain. The product is powerful, but it speaks the language of supply chain specialists. Not everyone using it is one.

Problem

Users were scared to use the product because a wrong click in a spare parts system at scale can cost millions. The interface didn't give them enough context to feel confident. The users ranged from supply chain specialists to warehouse employees with no logistics background. For the second group, the product spoke a language they didn't know.

Thought process

I learned supply chain terminology from scratch, the same way a new user would. What made terms click for me was a real-life example using everyday objects.

That became the idea for a pattern. If a familiar example was what made a complex term make sense to me, it could work for a warehouse employee seeing it for the first time.

Solution

I defined a pattern for contextual help across the product: a one-sentence plain language definition followed by a concrete, everyday example. The tooltip was expandable: a specialist could read the one-line definition and move on. A less experienced user could expand it and get the full explanation with an example.

The pattern scaled across hundreds of labels and terms throughout the product.

What's a Bill of Materials (BOM)?

A complete list of every part needed to build or repair a product.

Example: You make bicycles. A wheel needs a rim, spokes, a hub, a tyre, and a tube. That list is the bill of materials.

Outcome

Users had the context they needed at the moment they needed it, without leaving the screen or asking a colleague.
The product stopped feeling like it required specialist knowledge just to navigate.

Have a product that needs to speak clearly?

Get in touch See what I offer