Analytify

What Analytify is

Analytify is an open-source GenBI platform. In the company's own FAQ words: "Connect your data, ask questions in plain English, and get share-ready dashboards, no SQL and no BI backlog required." GenBI, it explains, is generative business intelligence: an AI layer on top of your data that writes the queries, builds the charts and explains the results, so anyone on the team can self-serve answers.

It is a dashboard and BI product aimed at 3 readers. Business teams get dashboards without queuing for an analyst. Data teams get a governed layer so AI answers use agreed definitions. SaaS companies get white-label, multi-tenant analytics to embed in their own product. Because the code is open source, it also reaches teams that want to run BI on their own servers. The pages don't state where the company is based or how long it has operated.

What you build and how

Asking and building. The AI assistant is called GenieAIQ. The FAQ describes it: "Type a question the way you'd ask a colleague. Genie generates the SQL, runs it read-only, returns the chart, and leaves the query fully editable so your analysts stay in control." The home page shows the path as connect any source in a few clicks, ask in plain English, then publish a live dashboard. Its short demo videos cover building a dashboard, sharing dashboards securely with filters, and tracking KPI trends.

Governed answers. The company's pitch is "Governed. Not guesswork. Every AI answer is bound to your published metrics and permitted columns, no hallucinated numbers." The mechanism is a semantic layer. On the Superset comparison page, the company says its semantic layer supports drag-and-drop relations and joins to represent complex data. Natural-language queries run on top of that layer, which is how end users get answers "with semantic-layer guardrails."

Skills. The home page shows a skill wizard. You "define a skill once through the 4-step wizard," and the GENIE assistant then uses it to assemble dashboards with consistent metrics, synonyms and guardrails. The example card is an ecommerce category skill listing 12 metrics, 24 synonyms and 6 guard rules.

Adaptive Dashboard. This is the page's answer to bringing your own data into a shared dashboard: connect external databases and sources, reuse existing dashboard templates, blend organizational and personal datasets, with "no coding required." The stated payoff is that every team builds the reports relevant to its work on one shared framework. A diagram also shows a dashboard being sent as a source to a retail partner by invite, labeled "governed" and "revocable." One-click data source switching lets users move between staging, production or client-specific environments with no duplicated dashboards.

Connectors and features. According to the company's comparison with Apache Superset, Analytify ships out-of-the-box connectors for databases, cloud platforms and file systems such as Excel and CSV, offers custom SQL, drill-down inside sheets, no-code customization and branding controls. Treat the comparison as the vendor's view; it describes Superset in terms that favor Analytify.

Embedding and white label. The Superset and Luzmo pages describe "multi-tenant-first architecture," full white-label rebrand and embedded dashboards for SaaS products, with row-level security mentioned for embedded use.

What the people using it get. The home page frames the end-user result as "Minutes. Not tickets.": business users ship governed dashboards themselves, so data teams stop being a request queue. Each team builds reports relevant to its own work on the shared framework, and dashboards can be shared with filters, as the sharing video shows. The site also lists comparison pages against Tableau, Power BI, Superset and Qlik Sense, plus solutions pages by industry.

Choice of model and hosting. "Your model. Your cloud. OpenAI, Claude or Ollama, running in our cloud or fully self-hosted on your own infrastructure." That is a real option for teams that don't want their data going to one AI vendor.

Open source. The home page shows a terminal sequence: git clone analytify-ai/core, then docker compose up -d, then a running instance on port 3000, labeled "MIT licensed," "Self-host anywhere" and "No vendor lock-in." The FAQ says: "Yes. Analytify is open-source and self-hostable, so you can run it inside your own environment." The Superset page adds that you can self-host, audit the code and fork it. There is a free Community Edition for self-hosted deployments, and the Luzmo page calls it "free forever."

Plans, pricing, and what the company claims

• Trial. A 14-day free trial, with the closing banner saying "No credit card."
• Cloud pricing. The Luzmo comparison describes it as per-user, with unlimited embedded viewers and "no per-seat tax on rollout." No dollar figures appear on the pages.
• Community Edition. Free for self-hosted deployments, with a download link on the Luzmo page. The same page invites you to book a 15-minute demo to talk through migration, architecture and pricing.
• Enterprise. The Superset page mentions enterprise support "without DIY ops."
• Security. The home page lists SSO and SAML, role-based access, and the badges "SOC 2 Type II" and "ISO 27001." These are the company's own claims.
• Switching. For Luzmo users the company says moving mainly means rebuilding core metrics in the semantic layer, "usually 1-3 weeks" for key dashboards.

The Luzmo page carries an honest disclosure, saying "Analytify is our product" and asking readers to weigh its comparison with that in mind. Apply the same caution to every comparison on the site.

Who it suits and who it does not

Analytify suits a team that wants natural-language BI without handing governance to the model. A team that needs its metrics defined once, a data team tired of being a request queue, and a SaaS company that wants to embed branded dashboards for customers all fit. It also suits engineering teams who want to read the source, run the stack with Docker Compose and keep data on their own infrastructure.

The caveats are practical. Self-hosting means you run it, including monitoring and upgrades, and the Superset page pitches enterprise support "without DIY ops" as the alternative. Governed answers depend on someone defining the metrics and the semantic layer first; the company says rebuilding core metrics there is the main task when moving from Luzmo. The cloud price isn't published, so budgeting means a conversation with the company. Finally, the comparison pages are written by Analytify, so a team already deep in Tableau, Power BI or Qlik Sense should verify the claims in a trial.