Show HN: Hal9 – Low-Code Analytics for the Web

Hello HN from the Hal9 team (https://hal9.com). We’re building a low-code analytics platform based on web-technologies focused on making AI more accessible.

We have worked many years with different ML technologies across many projects, feeling frustrated at the amount of engineering effort required to set up even simple AI projects.

So with Hal9, we are lowering the barrier to AI by providing an integrated platform that can help you build data transformations, visualizations, and predictions from a library of reusable components. You drag-and-drop these components to compose data pipelines, but you are never stuck with us since you can always export the pipelines as JavaScript, R, Python or create your own blocks.

How is it different from existing solutions? Most AI applications require integrating a web frontend with an AI backend usually running Python, PyTorch, TensorFlow, or h2o. One can build the AI backend in Docker, Kubernetes, MLflow, and such; you then set your web stack with say, NodeJS and React or go for a JAMStack. Between them, lies a REST API. This is fine for large projects, but setting this infrastructure, defining the API and maintaining this system is usually quite complex and expensive.

Hal9 solves this problem by integrating the frontend and backend under the same platform, giving you AI templates to start from, and letting us worry about keeping your application running. In many cases, all you have to do is drag-and-drop, in others you write code blocks in JavaScript, NodeJS, Python or R and we deploy the frontend and backend for you.

We found out web developers, business experts, and data scientists can benefit from Hal9. In teams that have all these disciplines, Hal9 becomes the place where they can collaborate from prototype to deployment. For teams that are missing one discipline, Hal9 fills that void by providing ready-to-use templates, blocks and a worry-free infrastructure.

In the short term, while you can do some basic AI like running image classification, regressions and sentiment analysis; we’ve put most of our effort in data transformations and visualizations in the browser. This helps us reduce cloud-compute costs, increase privacy and speed, at the cost of working with datasets of at most 1M rows. It was important to us to have strong building blocks and be able to scale to millions of users, even if it meant having to wait a little longer on proper AI using large datasets. That said, we already have early support for Python and R, and we will keep growing the platform and team to support larger datasets.

In addition, we have an open-core technology and our blocks are open source; so we are also hoping that experts in the community will create components in our GitHub repo for others to reuse. Check out https://github.com/hal9ai/hal9ai to contribute, submit issues, questions, or suggestions.

We’ve come a long way since our alpha release half a year ago (https://news.ycombinator.com/item?id=27334145), and we are very excited to see what you can come up with on the platform. Let us know what you think!