Vocal Image Uses AI to Train Better Public Speaking

From personal struggle to AI-powered voice coaching

Estonia-based Vocal Image has turned a founder’s personal challenge into a mass-market product. CEO Nick Lakhoika grew up in Belarus, didn’t speak English until he moved to Estonia, and once battled speaking anxiety. That journey — and a vocal coach who taught him voice can be trained — inspired a YouTube channel that evolved into an app focused on affordable, at-home coaching.

The mobile app delivers guided journeys: tongue twisters, breathing drills, gesture tips and presentation practice. It mixes interactive exercises with growing AI-driven feedback so users can practice privately and iterate quickly.

Traction, funding and team

Vocal Image reports 4 million downloads and about 160,000 active users. After early accelerator support from Startup Wise Guys, the company scaled efficiently: it reached $6.5M ARR on under $1M pre-seed, later raised a $3.6M seed led by Educapital, and now claims $12M ARR with roughly 50,000 paid users. The 20-person team includes a majority of Belarusian exiles who relocated after political repression at home.

Why Vocal Image stands out

• Large, GDPR-compliant dataset: ~1M voice samples and 35,000 daily recordings.
• Community labeling via Voice Rating gives human-grounded labels for traits like confidence.
• Product-market fit across professional skills, leadership training, and personal confidence.

The labeled dataset is particularly strategic. Voice samples annotated for perceived traits are the raw material voice-AI models need to improve automated coaching and to fine-tune synthetic voices. That creates options beyond B2C subscriptions — think licensing speech models or powering enterprise training tools.

Competition and expansion

Vocal Image faces rivals: edtech players are adding AI speech trainers and large platforms keep improving voice tools. The startup is responding by increasing localizations (English, Spanish, German, French, Ukrainian, Russian and more planned) and by leaning into AI accuracy and privacy to keep trust high.

Recognition came from industry programs too: Vocal Image was one of five winners in a European AI startup program run by Hugging Face, Meta and Scaleway. That endorsement helps with tooling and visibility as the team pushes into deeper AI feedback and potential enterprise use cases.

What this means for leaders and developers

Vocal Image is a good case study for organizations looking to build human-centered voice AI: combine real-world labeled audio, clear privacy practices, and product flows that map to job-focused outcomes (presentations, leadership). For developers, labeled perception data speeds model tuning. For business leaders, it opens B2B avenues like corporate training partnerships or voice cloning services for accessibility.

Vocal Image’s journey—from a founder’s personal voice work to a data-rich AI product—highlights how lived experience, community labeling and strong compliance practices can produce a defensible product in a crowded market. The next challenge is converting data and downloads into sustainable enterprise revenue while preserving trust and model quality.

For tech leaders, the takeaway is clear: voice is not just a feature—it's a dataset, a compliance challenge, and a product wedge. Companies that treat voice samples as strategic assets, not just telemetry, will win the next wave of human-AI communication tools.