From eea231719d32692f4d3a3315f7e6cf2b026bb85e Mon Sep 17 00:00:00 2001 From: intraq-dev-ai <251956840+intraq-dev-ai@users.noreply.github.com> Date: Thu, 30 Jul 2026 09:46:57 +1000 Subject: [PATCH 1/2] docs: add 'Who is this for?' section and comparison table to README --- README.md | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/README.md b/README.md index cca71a1..b646bf3 100644 --- a/README.md +++ b/README.md @@ -36,6 +36,18 @@ The goal is not just natural language SQL. The goal is AI BI that a team can ins - **Embedded analytics foundation** — use intraQ as a reporting layer for product and internal workflows. - **Provider-flexible AI** — configure Codex OAuth, OpenAI, or Gemini from the admin UI. +## Who is this for? + +intraQ is built for teams that want answers from operational data without a dedicated BI function: + +- **Hospitality operators** — restaurants, cafes, bars, hotels, and multi-venue groups analysing POS and PMS data (net sales, covers, food cost, labour, menu mix, occupancy, ADR). +- **Energy retailers** — billing, arrears, credit exposure, and payment analytics. +- **POS / PMS / platform vendors** — embed white-label AI analytics for your own customers instead of building it in-house. +- **SaaS & product teams** — customer-facing embedded reporting over product data. +- **Internal BI & operations** — self-service analytics grounded in governed definitions. + +If your operational data lives in a SQL database and you want plain-English answers with the SQL visible, intraQ is for you. + ## Demo flow ```text @@ -92,6 +104,16 @@ Explore practical question sets in [`examples/`](examples/README.md). intraQ is not trying to replace every enterprise reporting suite. It is focused on operational BI workflows where teams want AI-assisted SQL, local control, and dashboards from governed data models. +| Capability | intraQ | Power BI | Metabase | Lightdash | +|---|:---:|:---:|:---:|:---:| +| Plain-English → SQL | ✅ | Partial | Partial | Partial | +| AI-built dashboards | ✅ | Partial | ❌ | ❌ | +| Semantic / knowledge layer | ✅ | Partial | Limited | ✅ (dbt) | +| Evidence & SQL shown for every answer | ✅ | ❌ | Partial | Partial | +| Self-hosted | ✅ | ❌ | ✅ | ✅ | +| Embeddable / white-label | ✅ | Limited | Limited | Limited | +| No data team required | ✅ | ❌ | Partial | ❌ | + Detailed comparison pages: - [intraQ vs Metabase](docs/comparisons/intraq-vs-metabase.md) From a6b5343186de80b970cd97b848d3fc0af6e6b394 Mon Sep 17 00:00:00 2001 From: intraq-dev-ai <251956840+intraq-dev-ai@users.noreply.github.com> Date: Thu, 30 Jul 2026 09:49:31 +1000 Subject: [PATCH 2/2] docs: make repo positioning industry-agnostic; verticals are commercial-support focus only --- README.md | 30 ++++++++++++++++-------------- 1 file changed, 16 insertions(+), 14 deletions(-) diff --git a/README.md b/README.md index b646bf3..1adc30e 100644 --- a/README.md +++ b/README.md @@ -38,15 +38,17 @@ The goal is not just natural language SQL. The goal is AI BI that a team can ins ## Who is this for? -intraQ is built for teams that want answers from operational data without a dedicated BI function: +intraQ works with **any industry** — if your operational data lives in a SQL database, intraQ adapts to it. There is no vertical lock-in; you define what your data means and intraQ grounds its answers in those definitions. -- **Hospitality operators** — restaurants, cafes, bars, hotels, and multi-venue groups analysing POS and PMS data (net sales, covers, food cost, labour, menu mix, occupancy, ADR). -- **Energy retailers** — billing, arrears, credit exposure, and payment analytics. -- **POS / PMS / platform vendors** — embed white-label AI analytics for your own customers instead of building it in-house. -- **SaaS & product teams** — customer-facing embedded reporting over product data. -- **Internal BI & operations** — self-service analytics grounded in governed definitions. +It's built for teams that want answers from operational data without a dedicated BI function: -If your operational data lives in a SQL database and you want plain-English answers with the SQL visible, intraQ is for you. +- **Operations & business teams** — ask questions in plain English and get trustworthy answers, no SQL or analyst required. +- **Platform & SaaS vendors** — embed white-label AI analytics for your own customers instead of building it in-house. +- **Internal BI & data teams** — self-service analytics grounded in governed, inspectable definitions. + +Connect any Postgres, MySQL, or MSSQL database and start asking questions. + +> Want a tailored, pre-built knowledge base and hands-on support for your domain? That's available commercially — see [intraq.dev](https://intraq.dev). ## Demo flow @@ -89,16 +91,16 @@ The public source includes local dashboards, Analyzer, SQL models, MCP, data-sou It intentionally excludes paid AI Studio, proprietary domain intelligence, control plane, paid release tooling, private operational docs, generated artifacts, credentials, and private operational material. -## Use cases and knowledge bases +## Use cases -intraQ is built for operational reporting where users need more than static charts: +intraQ is built for operational reporting where users need more than static charts — across any industry with SQL-backed data: -- **Hospitality analytics** — revenue health, covers, product mix, wastage signals, outlet performance, PMS and POS reporting. -- **Energy retail reporting** — accounts, billing cycles, arrears, credit exposure, payments, exceptions, and customer risk. -- **SaaS embedded analytics** — customer-facing dashboards over product data. -- **Ecommerce analytics** — revenue, orders, products, channels, margins, and customer behavior. +- **Revenue & sales analytics** — trends, margins, channels, products, and location performance. +- **Operational & performance reporting** — throughput, exceptions, and period-over-period comparisons. +- **Embedded customer-facing analytics** — dashboards over product data inside your own application. +- **Financial & risk reporting** — accounts, balances, aging, exposure, and payment behaviour. -Explore practical question sets in [`examples/`](examples/README.md). +You define the metrics and relationships for your domain; intraQ grounds its answers in them. Explore practical question sets in [`examples/`](examples/README.md). ## Comparisons