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AI-assisted self-service analytics that lets anyone explore data with natural language. Grounded in governed metrics, verifiable logic, and your team's shared definitions.
weekly_revenue
Ask questions naturally and get instant answers. The AI keeps context across follow-up questions - so exploring data feels like a conversation, not a query builder.
Ask questions in natural language. The AI remembers context from question to question, so you can drill deeper without starting over.
Go beyond 'what happened' to 'why it happened.' The AI acts as your analyst - forming hypotheses, running queries, and surfacing root causes automatically.
The AI does far more than just fetch answers - it actually understands the context and generates working solutions, even for edge cases. It's saved me a ton of time already.
A dual interface that meets you where you are. Start with a question in chat, then click into the generated chart to modify, filter, or explore further - no context lost.
EU
Every AI-generated answer is transparent. See the reasoning in plain English, then inspect the underlying query logic - so you never have to take the AI's word for it.
The AI restates its understanding of your question and walks through its reasoning step by step in plain language - so you can confirm it got you right before looking at the answer.
See a readable, high-level query formula - not raw SQL - to verify exactly how every metric is calculated. Compact enough for a human to review in seconds.
The AI prioritizes your team's endorsed metrics and composes new ones from existing definitions - not invented from scratch. Every answer traces back to your source of truth.
I'm seriously impressed with Holistics AI capabilities! While using Search Docs Mode, I realised it does far more than just fetch documentation - it actually understands the content and generates working solutions, even for edge cases not directly covered. Since then, I've been pasting in dashboard and theme code, and it consistently helps me fix or enhance things. It's saved me a ton of time already!
Instead of translating natural language directly to SQL, the AI generates AQL - a high-level, composable analytics language. The result: answers that are more reliable, more readable, and more capable.
metric count_users = count(users.id)dimensions { region: country.region }measures { avg_revenue: top(5, countries.id, by: count_users) | avg(revenue)}
WITH "aql__t3" AS ( SELECT "ecommerce_countries"."continent_name" AS "ecommerce_countries→continent_name", "ecommerce_countries"."name" AS "name", COUNT("ecommerce_users"."id") AS "count_ecommerce_users→id" FROM "ecommerce"."users" "ecommerce_users" JOIN "ecommerce"."countries" …
AQL is compact and high-level, making it easy for humans to read and verify the AI's logic - no deciphering 200-line SQL queries.
The AI works at a high-level language and doesn't worry about database-specific syntax or SQL gymnastics. The AQL compiler handles translation deterministically.
Break complex operations into modular pieces and combine them like building blocks. Prebuilt functions handle period comparisons, running totals, nested aggregations, and more.
I'd score AQL a 9 or 10. It's up there with the best tools I've used. With AMQL, it's really cool that we can now define metrics based on other metrics, stacking them on top of each other.
Models, datasets, metrics, and dashboards are all defined as text-based code. This means AI tools can read, generate, and modify your analytics definitions - and everything gets version control and governance for free.
Use Cursor, Claude Code, or your preferred AI coding tool to develop models and dashboards faster than ever.
Every change is tracked. Review diffs, manage branches, and enforce governance workflows - the same way engineering teams manage code.
AI writes field descriptions, dataset summaries, and tag descriptions - so your semantic layer stays documented without manual overhead.
Auto-generate commit messages and PR descriptions. Focus on the analytics work, not the paperwork.
I really loved that you have all of that in code. When working with ChatGPT, you can just say 'here's the table I have, can you add descriptions to everything?' It makes AI actually useful because it can parse and understand your models.
AI starts smart by drawing from your existing models and definitions. Then go further: customize its behavior at the organization, team, or individual level with programmable context.
AI automatically draws from your semantic models, metric definitions, dashboards, and conversation history. No manual setup required to get accurate answers from day one.
Define your business background, operational knowledge, and response preferences in a central config - so AI speaks your company's language and follows your standards.
Customize AI behavior per team or user with programmable logic. Marketing sees marketing datasets, purchasing sees purchasing datasets - automatically.
The dynamic context - where you can customize AI behavior per user or team - that's really cool. Other tools can't do that easily.
Connect to your existing tools or embed AI-powered analytics directly into your product - no AI stack required on your end.
Use Holistics' querying power inside any external AI tool via the Model Context Protocol. Your AI assistants can query governed metrics natively.
Give your customers AI-powered analytics inside your product. No need to build your own AI - embed the Ask AI experience directly.
To join our beta program for Holistics AI, please visit the following link: Join our waitlist. We will notify you once the beta is ready.
To provide Holistics AI functionality, we share your modeling metadata with third-party AI providers while you use the AI features. Rest assured, these providers or contractors are not permitted to use your data for training their models, and we do not utilize the AI inputs and results from our customers to enhance our service.
Currently, we use Open AI API, adhering to their policy of not utilizing prompts or any inputs as training data. Additionally, we are experimenting with other models to identify those that offer the best performance and user experience.