From fe38b7e062dcb12bf97447ced5692f3906a40b5e Mon Sep 17 00:00:00 2001 From: intraq-dev-ai <251956840+intraq-dev-ai@users.noreply.github.com> Date: Mon, 27 Jul 2026 20:00:34 +1000 Subject: [PATCH] fix: consistent intraQ capitalisation and update title to highlight Knowledge Layer --- README.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index c2de367..cca71a1 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# IntraQ – Self-hosted AI Dashboard Builder & Natural Language SQL +# intraQ — Self-hosted AI BI with a Knowledge Layer [![CI](https://github.com/intraq-dev-ai/intraq/actions/workflows/ci.yml/badge.svg)](https://github.com/intraq-dev-ai/intraq/actions/workflows/ci.yml) [![License](https://img.shields.io/badge/license-IntraQ%20Sustainable%20Use-blue)](LICENSE.md) @@ -7,20 +7,20 @@ [Website](https://intraq.dev) · [Docs](docs/DEMO_GUIDE.md) · [Quickstart](QUICKSTART.md) · [Configuration](docs/CONFIGURATION.md) · [Comparisons](docs/comparisons/README.md) · [Discussions](https://github.com/intraq-dev-ai/intraq/discussions) · [Contributing](CONTRIBUTING.md) -**IntraQ is a source-available AI business intelligence platform that lets teams query SQL-backed operational data using natural language, generate trusted SQL, and build interactive dashboards without traditional BI complexity.** +**intraQ is a source-available AI business intelligence platform that lets teams query SQL-backed operational data using natural language, generate trusted SQL, and build interactive dashboards without traditional BI complexity.** It combines AI analytics, natural language SQL, text-to-SQL workflows, an AI dashboard builder, embedded analytics foundations, MCP tools, RAG-style context retrieval, and a semantic layer built from local metadata, data dictionary entries, SQL models, relationships, dashboard context, and safe result summaries. ![intraQ dashboard builder with AI sidebar](docs/assets/demo/00-readme-hero-ai-sidebar.png) -## Why IntraQ? +## Why intraQ? -Most AI reporting tools stop at “generate a chart” or “write a query.” IntraQ is built around verified operational BI: +Most AI reporting tools stop at “generate a chart” or “write a query.” intraQ is built around verified operational BI: - **Answers stay tied to evidence** — SQL, selected data source, result rows, and assumptions remain inspectable. - **Dashboards come from real queries** — AI-created components are saved as live dashboard elements, not static screenshots. - **Business context is explicit** — dictionaries, SQL models, relationships, metrics, and dashboard context guide the AI before it answers. -- **Unsafe gaps are visible** — when data, filters, or model context are missing, IntraQ should say what is missing instead of inventing an answer. +- **Unsafe gaps are visible** — when data, filters, or model context are missing, intraQ says what is missing instead of inventing an answer. - **Operational use cases come first** — revenue health, product mix, wastage signals, account risk, billing exceptions, and embedded customer reporting. The goal is not just natural language SQL. The goal is AI BI that a team can inspect, reuse, and improve. @@ -94,10 +94,10 @@ intraQ is not trying to replace every enterprise reporting suite. It is focused Detailed comparison pages: -- [IntraQ vs Metabase](docs/comparisons/intraq-vs-metabase.md) -- [IntraQ vs Wren AI](docs/comparisons/intraq-vs-wren-ai.md) -- [IntraQ vs Power BI](docs/comparisons/intraq-vs-power-bi.md) -- [IntraQ vs Lightdash](docs/comparisons/intraq-vs-lightdash.md) +- [intraQ vs Metabase](docs/comparisons/intraq-vs-metabase.md) +- [intraQ vs Wren AI](docs/comparisons/intraq-vs-wren-ai.md) +- [intraQ vs Power BI](docs/comparisons/intraq-vs-power-bi.md) +- [intraQ vs Lightdash](docs/comparisons/intraq-vs-lightdash.md) ### intraQ vs Power BI