Ilgin Cevik Portfolio

The brief

Pick an AI company. Design an agentic GTM idea.

I ranked 13 companies by where a one-week sprint could change my mind, and picked Vizcom. It sells AI tools to designers, and designers care a lot about craft and authorship. I worked as one for years. AI that posts and fakes engagement would cost Vizcom their trust.

So the assistant never talks to anyone. It listens to public design discussions and hands patterns to a human.

"Not can AI make a sketch look nicer? More: can this change the very early part of design, when an idea is still messy and hard to explain to other people?"

My first-impression notes on Vizcom

Part 1: the assistant

Every rule answers a way AI fails

AI is bad at community research. I designed the assistant's rules and output formats around exactly those failures.

Invents sources, creators, and mentions

Nothing gets named without a link. No evidence means "Not found in reviewed sources."

Sounds confident about weak signals

Every output carries a confidence level and why it isn't higher.

Blurs what people said with what it thinks

Evidence and inference live in separate, labeled fields.

Wants to act on what it finds

Every output ends with a question for a human, not a recommendation.

Writing evals for it

I wrote a six-criteria rubric and scored every run against it: three weekly briefs and three signal alerts. Wherever an output lost points, I changed the rules.

Stays human-ledEvidence disciplineGTM usefulnessField separationCommunity sensitivityOutput quality

Weekly briefs

25–27 / 30

Signal alerts

27–29 / 30

Alerts scored higher because they forced one specific pattern instead of a summary. But they were still long, dense, and slow to review.

The problem it left

A careful AI is useless if nobody can review it

This is a real alert the assistant produced. Everything a reviewer needs is in there, but they'd have to read all of it to find out whether it's worth their time.

GTM Signal Alert

Sources reviewed: 10+ public pages across Reddit, design education, portfolios, and official product/education content.

Signal confidence: Medium. Repeated within industrial-design communities, but not enough fresh cross-platform evidence to call this a broad spike.

Trigger: Multiple 2026 discussions converge on the same distinction: AI-assisted rendering is increasingly acceptable when visible design fundamentals remain evident, while AI that obscures process, proportion judgment, or authorship generates resistance.

Emerging pattern: The debate is shifting from simply "Should designers use AI?" toward "Can I still see what the designer actually contributed?" This maps directly to Vizcom's workflow position around externalizing ideas without replacing designer judgment.

Emotional tone: Cautious acceptance mixed with anxiety about skill erosion, authorship, and AI becoming a shortcut.

…and five more sections

Part 2: the reviewer

Five decisions in under a minute

I mapped what a GTM lead actually has to decide when an alert lands, then checked which parts of the alert answer each one.

Is this real?
Is it new?
Does it matter for us?
Who should see it?
Do I need more evidence?

The raw alert answered "is this real?" and "does it matter?", but buried both in long paragraphs. It didn't answer "is it new?" or "who should see it?" at all.

where the raw alert answers each decision

Is this real?	Confidence, line 2. Sources only as a count.	buried
Is it new?	Nowhere	missing
Matters for us?	Last sentence of the pattern	buried
Who should see it?	Nowhere	missing
More evidence?	Links inline, mid-paragraph	buried

Part 3: the design

The alert where it lands

How I made these: the same rule as the assistant. I owned the judgment, AI did the generating. I wrote the spec, AI rendered it, I iterated. Each spec covered content, hierarchy, and states, and every change after that came from testing.

GTM teams already live in Slack, so the alert became a card there: one-line pattern, a trust row (confidence, sources, platforms, first seen), one real quote, one review question, and three actions.

I tried three orders. Text-first reads like the raw alert again. Trust-first asks people to judge numbers before they know what the pattern is. Quote-first leads with a real designer's words, then the confidence behind them.

GTM Signals app · 9:14 AM

Designers want proof of what they contributed, not just AI renders.

The debate is shifting from "Should designers use AI?" toward whether the designer's contribution is still visible. Medium confidence, 12 sources…

Looks realNeeds more evidenceNot relevant

1 · text first

GTM Signals app · 9:14 AM

Medium confidence10+ sources · 3 platformsfirst seen 4 days ago

Designers want proof of what they contributed, not just AI renders.

Review: Does our messaging separate concept visuals from production work?

Looks realNeeds more evidenceNot relevant

2 · trust first

GTM Signals app · 9:14 AM

Designers want proof of what they contributed, not just AI renders.

“Can I still see what the designer actually contributed?”r/IndustrialDesign

Medium confidence10+ sources · 3 platformsfirst seen 4 days ago

Review: Does our messaging separate concept visuals from production work?

Looks realNeeds more evidenceNot relevant

3 · quote first ← chosen

Evidence on one side, interpretation on the other

The assistant's rule of separating evidence from inference became the layout of the review view. What people said sits on the left. What the assistant thinks sits on the right, in a different visual treatment, so nobody mistakes one for the other.

Designers want proof of what they contributed, not just AI renders.

Medium confidence Repeated in industrial-design communities, not yet across platforms.

What people said

Reddit · r/IndustrialDesign

“Can I still see what the designer actually contributed?”

view thread

Reddit · r/CarDesign

Reviewers keep asking for base sketches alongside AI renders.

view thread

Design education

Portfolio guidance stresses showing process, refinement, and iteration.

view page

What the assistant thinks interpretation

AI rendering is accepted when visible design fundamentals remain. Resistance shows up when AI hides process or authorship.

This fits Vizcom's position: helping designers externalize ideas without replacing their judgment.

Competitor interaction: Not found in reviewed sources.

Designing for when the AI isn't sure

The states most designs skip. Each one had to look intentional, not like an error.

Organic Vizcom mentions

No organic Vizcom mentions found in reviewed sources.Checked 10+ sources, 3 platforms

Not found"No organic Vizcom mentions found in reviewed sources."

Low confidence

Footwear designers discussing AI mood boards

2 sources, 1 platform

Low confidenceMuted, and pushed down the feed.

AI renders in student portfolios

For“fine as a tool”

Against“reviewers look down on it”

Sources disagreeBoth sides shown.

Process proof in portfolios

Only seen on Reddit. May be narrow.

One platform onlyA warning that the signal may be narrow.

Closing the loop

When someone marks an alert "not relevant," one tap says why: wrong audience, already known, or weak evidence. That feedback tunes what the assistant surfaces next.

Why not relevant?

Wrong audienceAlready knownWeak evidence

Got it. Similar signals will rank lower for your team.

Testing

Raw alert vs. designed card

I tested with 4 classmates with PM, marketing, data science, and business backgrounds. Each saw the same alert in both formats and decided whether to act on it, dig in, or ignore it.

~1 min

Raw alertMost skipped most of it and couldn't retrace what it said when I asked. Only the data science classmate read it all, and still skimmed the long sections.

~30s

Designed cardEveryone could recall what the alert was about. 3 opened the source link to check it was real. 1 actually fact-checked it.

The quote mattered more than the AI's read.

A real designer's words shaped their judgment more than the interpretation did.

One person thought a human wrote the review question.

The others only knew it was AI because I'd told them.

The confidence label felt arbitrary to some.

"Who is AI to know what's important?" Others were glad it was there. Showing the numbers behind it would help.

"Not found" made them trust it more.

But it also made them wonder whether the assistant is useful at all. Weak alerts would likely get ignored.

Routing came up on its own: alerts should go to the person who owns this work, not a general Slack channel, and leadership should only see them when someone forwards them.

Iteration

The revised card

Four changes, each from something testing showed. I prototyped it in code with an AI coding tool so it could be clicked through, not just looked at. I haven't re-tested this version yet.

3Direct message to you · community lead

GTM Signals 1AI assistant 9:14 AM

41 of 2 alerts this week · weaker signals go to the Friday brief

try it: switch states, click the buttons

labeled as AI, down to the review question
the numbers behind the confidence, not just a label
sent to one owner, who can forward it
a higher bar, so alerts stay rare

What I took away

Ask what humans should still own.

The project got better once I stopped asking "what can the agent generate?"

Design the failure cases first.

Most of the work was deciding what happens when the AI doesn't have enough evidence.

Honesty earns trust, not attention.

"Not found" made people trust the assistant more, and pay attention to it less. The fix is alerting less, not hiding uncertainty.

#		question I'd test
1	Vizcom	Does it become real design workflow, or stay a great demo?
2	BAML	Is reliability pain urgent enough for a separate layer?
3	Tigris Data	Does the edge beat infra switching costs?
4	Flint	Can it survive school procurement?

Work

I redesigned the HashiCorp Cloud Platform dashboard. It shipped almost exactly as I designed it.

Linking Terraform Stacks so teams can see what a change will affect

Designing how people review an AI that might be wrong

What people I worked with said

Want to talk?

Some other things about me

Right now I'm most curious about AI products, developer tools, and platforms.

A dashboard that didn't answer dashboard questions

One dashboard, two very different jobs

What customers asked for

Every card had to earn its spot

Designing for day 0, day 7, and day 30

Only show what you can use

The design I wanted couldn't ship

Almost exactly as designed.

What I took away

No built-in way for one stack to depend on another

Drawing it out before designing it

No final features, no final flows

A lot of iterations, and a lot of sticky notes

I thought I was on the right road

Testing with 7 Stacks users

See what a plan will set off before you approve it.

The final design, in my own notes

What I took away

What I'd test next

Pick an AI company. Design an agentic GTM idea.

Every rule answers a way AI fails

Writing evals for it

A careful AI is useless if nobody can review it

Five decisions in under a minute

The alert where it lands

Evidence on one side, interpretation on the other

Designing for when the AI isn't sure

Closing the loop

Raw alert vs. designed card

The revised card

What I took away

Turning a flat list into an immunization record people can trust

Shipping a dashboard redesign without new backend work

A 0→1 feature, reprioritized halfway through

Choosing a company, a strategy, and what AI shouldn't do

Want to talk?