🦄 Crunch #041: Turn your Google Drive Folders into Knowledge Centers, Better Computer Vision Models and Datasets

Bite-sized startup insights

🇺🇸 kbee: Turn your Google Drive folders into an organized knowledge base. (Founded 2020, Total Funding Amount: bootstrapped)

How it works

Turn your existing Google Drive folders and files into a professional wiki. The wiki comes with a full-text search across all of your content. Edit and collaborate on your wiki articles without leaving Google Drive.

The Metrics

Stripe verified metrics, including weekly, monthly, and yearly revenue growth.

The Team

Based in San Francisco, US, co-founded by Sai Arora and Sandeep Dinesh.

“Google Docs is the genesis of all of our help center articles already. Instead of copying them over to another Help Center tool, we figured why not build a help center on top of Google Docs.” — Kbee team

The backstory

We built Kbee to cut out the endless copy & paste, and publish a full Google Drive folder as our help center.  

Since our initial launch, we discovered folks using Kbee to power their internal wikis, customer help centers, employee onboarding processes, and more.

Kbee is the perfect tool to turn content you already have into a professional wiki or help center in just a few clicks.


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🇧🇪 Segments.ai: Build better computer vision models by building better datasets. (Founded 2020, Total Funding Amount: undisclosed seed)

Segments.ai is a platform for computer vision experts to iterate more quickly between data labeling, model training, and failure case discovery.

How it works

Visualize and compare ground truth labels and model predictions. Investigate where your model is working well and where it is failing. Find failure and edge cases through confidence scores and embeddings.

On top of that, segments.ai has a unique pricing model. It’s called Labeling-as-a-service solutions. In a nutshell, it’s similar to pay-as-you-go pricing models.

The Team

The backstory

We believe in an iterative approach — A well-curated dataset is more important than a fully optimized model.

We help you to curate your data better by leveraging your model predictions in an iterative approach.

Our mission is to help you build better computer vision models.


🏁 You’re all set!

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