Reducing blind spots in GenAI applications

🦄 Unicorner Startup of the Week:


✍️ Notes from the Editors

Today’s article is brought to you by THELAB. Founders: we know your deck sucks. THELAB is your pitch and raise source to get you funded.

Today’s story on Manot would be incomplete without a preface into Unicorner lore.

In case we haven’t dropped evidence in the past 3+ years of these weekly covers, we’ve been involved in the budding Armenian tech community (yup, it’s a thing). Manot has been on our radar since the early days of Unicorner through our time working in Armenian tech. CEO and Co-Founder Chinar Movsisyan has joined us at several events, including our SF Happy Hour in early 2023.

And so, things have finally come full circle—we hope you enjoy this week’s deep dive into Manot!

P.S. If you’re interested in a Unicorner cover of your company, coming to one of our events is a great opportunity to pitch. 🚀

- Ethan and Arek 🦄

Reducing blind spots in GenAI applications

When developing generative AI, or GenAI, applications, businesses often fail to accurately capture their evolving needs, leading to misalignments in AI outputs. Feedback from customers, crucial for model improvement, is frequently unstructured and overwhelming, with AI teams dedicating 60% of their time to its analysis. The current solution to this challenge is to identify and correct errors only after deployment, which is both costly and time-consuming. Manot’s solution pinpoints GenAI's blind spots by suggesting targeted training data, significantly enhancing application reliability and reducing error correction time from weeks to minutes. Manot's solution aims to streamline the overall maintenance process, ensuring GenAI applications better meet business and customer expectations efficiently.

🔗 Check it out:

💰 Business Model

Manot is focusing on GenAI providers for enterprises. Its ideal customer profile (ICP) consists of businesses that handle a vast amount of unstructured data daily, require high-precision GenAI due to domain-specific knowledge, and are at a stage where they are ready to scale and require automation. It has carved out a niche in providing document processing solutions specifically designed for the fintech and legal AI sectors. To address this need, it offers an annual on-premise licensing solution at a flat rate pricing model.

📈 Traction and Fundraising

  • Raised $1 million in pre-seed funding

  • Participated in Berkeley SkyDeck in 2022/2023

  • Successfully deployed several POCs and actively talking with many companies

👫 Founders

📖 Founder Story

Chinar Movsisyan, Manot CEO and Co-Founder, brings over seven years of experience in deep learning to the team. She has led AI projects in areas such as health care, drone technology, and satellite systems, moving them from research to practical use. Her PhD research was on applying machine learning to identify and classify cardiovascular problems.

Haig Douzdjian, Manot COO and Co-Founder, has a background as a software engineer, including time spent at Alteryx's AI Innovation Lab and Zest AI. Most recently, he was head of product at Spectral, a machine learning company in its Series B funding stage, with investments from Social Capital, Google’s Gradient Ventures, and General Catalyst. His expertise lies in developing AI solutions for the finance sector.

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💼 Opportunities

Manot is actively seeking investors. To request an intro, please reply to this email.

🔮 Our Analysis

The generative AI market is on the brink of significant growth, driven by the application of large models across diverse sectors. This expansion underscores the necessity for businesses to adapt foundational models to their specific needs, a process that demands precise data collection to ensure optimal performance in production settings. Manot positions itself as a key player in this evolving landscape, offering solutions that not only pinpoint where models fall short for specific use cases but also compile datasets tailored for model refinement. This enhances performance and addresses model vulnerabilities. Distinctively, Manot prioritizes the acquisition of 'the right data' over 'big data,' streamlining the finetuning process by focusing on efficiency, reducing training costs, and shortening the timeline for redeployment.

📚 Further Reading

Written by Abhi Sharma

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Nothing in this email constitutes investment or legal advice. Readers should conduct their own research before making investment decisions.