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Work info
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Role:
Lead Product Designer
Timeline:
Jun 2023 - Apr 2024
Scope:
UI Design, UX Research and GTM collaboration
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Context
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Evaluating the API required a developer. Most buyers weren't developers.
Teams wanted to evaluate Arabic and Indian-language speech APIs before committing to integration. The only way to do it was to write code. I designed a no-code web platform that removed that barrier — from zero to launch.
Outcomes
500+
active users within 24 hours of MVP launch
+8
enterprise customers onboarded
<3 min
taken for a buyer to evaluate the API
90% of language AI is built for European languages. NeuralSpace was built for everyone else.
NeuralSpace had strong speech APIs for Arabic and Indian languages — markets most AI companies ignored. The problem was access. Testing the API required writing code. Sales demos were slow, engineering-dependent, and hard to repeat. Non-technical buyers had no way to evaluate quality on their own.
VoiceAI was the product that closed that gap. As the lead product designer, I took it from early definition to MVP launch and into post-launch iteration — working daily with engineering and aligned with sales and marketing from day one.
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Process
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I ran alignment sessions to decide what to cut, not just what to build.
Early sessions with the CEO, CTO, product lead, and engineering lead surfaced two competing instincts: ship broadly to show range, or ship narrowly to prove core value. I pushed for narrow. If we couldn't demonstrate accurate transcription clearly, adding more features would dilute the signal.
I brought sales and marketing in at this stage too. Their input shaped one important structural decision: API integration sat below the output, not above it. See the result first, then integrate.
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MVP
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Lo-fi: most iteration happened on two screens
The homepage and the output screen got the most iteration — and for different reasons.
The homepage determined whether a buyer would start or leave. I tested three structures before landing on the final approach: two primary workflow entry points as the dominant visual, configuration options accessible but secondary.
The output screen determined whether the product felt trustworthy. This is where a buyer decided if the transcription was good enough to integrate. I tested how results were surfaced, where speaker labels sat, and how much information was visible without scrolling.
Everything else in lo-fi was relatively straightforward once these two screens were resolved.
Hi-fi: aligining with rebrand and shipping the MVP
After lo-fi, I moved into high-fidelity using NeuralSpace's rebrand and design system. The focus was minimal UI and output screens easy to scan — especially during live demos where a buyer is deciding in real time.
Four workflows in the MVP
Real-time transcription — the primary evaluation use case
File-based transcription — async, for longer content
Sentiment analysis and transcript summaries
Translation with side-by-side language comparison
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Testing
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We assumed speaker separation was a power-user feature. Testing proved it was everyone's first move.
After launch I ran task-based usability testing and tracked two feedback channels: in-app bug reports and the NeuralSpace Slack community.
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Post MVP
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We fixed the moments that made users doubt.
A faster way to get value from transcripts
Users in the Slack community were copying transcript text into ChatGPT to ask questions about their recordings.
I built a native Q&A layer — AMA — directly on top of the transcript output to remove that step.
No more guessing during uploads
Drop-off data showed users abandoning during long uploads around the 15-second mark.
No system feedback — the product just looked stuck. I added explicit in-progress states so users always knew the system was working.
Speaker separation moved to where users actually looked
Direct result of the testing finding. Moved from the config panel to a default dropdown on the output screen. Time to access went from 4 clicks to 1. Users stopped reporting it as a missing feature.
Beyond transcription — adding Text-to-Speech
Enterprise sales flagged repeated buyer requests for TTS in Arabic and Hindi dialects — 6 inbound asks in a single week. I designed TTS as a standalone workflow once the signal was clear, supporting NeuralSpace's focus on dialect and pronunciation accuracy for underserved languages.
Together, these changes shifted VoiceAI from a transcription tool into a flexible environment for exploring speech-based workflows.
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Reflection
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Beyond the interface — designing the go-to-market
I also worked with marketing to create demo scripts, landing page copy hierarchy, and launch assets. The demo script structured the product walkthrough around one use case: an Arabic call center evaluating transcription accuracy before signing a contract. That framing reduced what sales had to explain in a first call.
What did I learn?
Early GTM involvement makes launch easier
Having sales and marketing aligned from the start meant the product and the demo story were never out of sync. No last-minute scrambling before launch.
Narrow focus, test often
Shipping a focused MVP and testing at every step gave us a clearer picture of what to build next. Trying to do everything at once would have made it harder to see what was actually working.
