^{The quicker pitch: Quick Pitch RoundUp}

We’ve just launched our paid newsletter for those who want to receive a curated list of every startup you need to know about in the eu ecosystem.

Thanks to those who have already purchased their first month!

They know that sourcing top startups is tough. But to succeed in venture it’s crucial.

Every week we leverage our brand in the ecosystem (and the best tools e.g. Harmonic AI, Specter and Dealroom), to find the best early stage European startups before they become sought after deals.

Only a tiny % of those that apply get featured on Quick Pitch.

Now, we want to offer that leverage to you guys.

Acting as your outsourced deal flow for top tier startups.

Get Access Here

(reply to this email with any questions if you have them)

At a glance

Unityflow AI is a next generation speech recognition platform for underserved languages.

• Huge amounts of speech data are being generated online every day. Unityflow AI for transcription enables you to process this data accurately, cost-efficiently, and in real time.

• Crowdsourced language expansion empowers local communities to build and label models in their dialects, driving adoption and scale at minimal cost while also creating job opportunities.

Quick fire details

• Headquarters: Glasgow, UK

• Employee count: 3

• Business model: SaaS

• Backed by: Raised $50,000 from one angel so far.

• Funding amount: Raising $1.1M in Pre-Seed

The founders

• Cofounder & CEO, Abdullah Al Wasif: Early team at Pathao (Bangladesh’s largest consumer tech startup) launched Uber in Dhaka in 2016 and the former President of Volunteer For Bangladesh (Dhaka District).

• Cofounder & CTO, Shafayat Hossain: CS Grad (AI/ML). Twice regional finalist of ICPC and previously built a speech-to-text project with +72k downloads.

The market

• Underserved languages, particularly in South Asia, Africa and indigenous communities worldwide, face significant barriers in speech technology adoption. For languages like Hindi, Arabic, Bengali and Punjabi the current market is worth ~$30B today and is predicted to hit $80B in under 10 years.

This startup is solving two key problems:

• Many of these languages have rich oral traditions but lack standardised text

  corpora, making it difficult to train accurate speech models. One reason why other competitors are focused on English.

• The B2B market is wildly underserved. Enterprises want speech transcription. They need accuracy and affordability at scale but don’t have a good enough solution yet.

Traction metrics

• 10,000+ users

• 100+ active paid users

• 2000+ waitlisted businesses

• $150,000 in pipeline revenue

• B2B customer include: OxfordLanguages, CYARA, audiocodes, NTTDATA and VUX.

Competitive landscape

• English focussed: Otter.ai, TranscribeMe, scribie

• Unityflow AI focussed languages: None

We’re collaborating with VC’s to offer exclusive allocations into deals. Stay tuned for the launch of quick pitch deal.

In the meantime register below if you’re interested.

Our Take

Appeals

• Proprietary Data Flywheel: Continuously improves accuracy through real-world customer audio, labelling, and model fine-tuning. Adopting speech models to cultural norms, idioms, emotional tones, and even gestures, which becomes defensible design, data IP and patenting domain specific use cases in speech recognition for specific languages over time.

• Localised transcription: Unityflow’s multilingual NLP engine handles noisy environments, mixed-code speech, and diverse accents better than generic solutions, giving them a unique edge in emerging and global markets.

• Vertical integration: Custom workflows (API) for customer support, media, legal, education, healthcare. Including summarisation, translation ensuring seamless integration and scalability.

Risks

• Adoption: Slow adoption of AI transcription tools in developing markets due to limited digital infrastructure.

• Limited GPU control: Dependence on third-party GPU services may affect performance and margins if costs spike or availability drops.

• Expensive tech due to conditions: Language model accuracy may lag in low-resource dialects, requiring significant investment in data collection and annotation.

Last feature’s results

Our readers are have DRAWN with Joggle AI