Hi HN, Nikhilesh here — c0founder of Helical Insight.
We've been building Helical Insight for few years now as an open source BI platform, used for interactive dashboards, pixel-perfect/paginated reporting, embedded analytics, and now AI-powered conversational analytics.
Today we've made a major change to our open source strategy.
Instead of keeping advanced capabilities behind an Enterprise Edition, we've made the Community Edition feature-complete. It now includes AI analytics (bring your own LLM), embedding, SSO, multi-tenancy, row-level security, white labeling, scheduling, report bursting, dashboards, and pixel-perfect reporting. Source code present on Github.
The only differences are:
Community Edition includes a small "Helical Insight" branding on charts and exports that cannot be removed. Community support is provided through our forum, while Enterprise customers receive SLA-backed support and can remove the branding.
Our reasoning is simple: developers and organizations should be able to evaluate and use the complete platform instead of an artificially limited edition. We'd rather build our business around support, services, and enterprise SLAs than around feature gating.
The project is primarily Java-based & react based, supports self-hosting, Docker, Kubernetes, and integration with multiple databases and identity providers. For AI, we lately introduced Python also.
I'd genuinely appreciate feedback from anyone using Superset, Metabase, Redash, Lightdash, JasperReports, Pentaho, Crystal Reports, or SSRS.
What would stop you from adopting an open source BI platform today? Happy to answer any technical or architectural questions.
How we will make money: We plan to make money by providing services. There is an enterprise version wherein the "Helical Insight" attribution will not be there.
We have built the product (we are based out of Hyderabad, India), right now it's on version 7. Our product tech stack is primarily Java, React and for the AI it has Python. For AI analytics usage, you can use any LLM of your choice like OpenAI, Gemini, Claude etc.
Our product also supports paginated canned reports also like invoices, bank statements, salary slips etc, interactive dashboards with drill down drill through, as well as AI assisted chat driven analytics.
There is also support of embedding, white labeling and SSO, hence allowing to embed these analytics easily.
Interesting approach. Since you're no longer differentiating on features, what's the incentive for companies to pay for the Enterprise Edition?
there are a few incentives why companies might go for enterprise version - if they might need continous enterprise support - second is especially if you are embedding the analytics into your product, then in those cases the analytics will be exposed to end users. In those cases, enterprises may not like to have Helical Insight branding. Then they might go for enterprise version
Does the AI part send data to OpenAI?
First of all, you have the option to use any LLM like Gemini, Claude, OpenAI.
Secondly, when a person types a question, we send that question as well as some other information which is defined at the agentic layer like synonmys, custom calculations, joining information etc. The LLM will help in creating the base query, we get the base query from LLM. Then we add further required where clause to make sure there is row level data security. And then the user sees the data.
So far, no DATA as such has gone to LLM. Now once our application generates a viz in response to asked question, then on the chart (in the chat window) there is also an option called "Explain this chart". When clicked, then ONLY this aggregated query response data goes to LLM and gets some context about the generated response.
Hence this approach helps with - making sure there is less token usage and keep cost under control - secondly DATA as such never goes to the LLM. It goes ONLY when a person clicks on explain the chart