diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml index 3ba13e0..26e61f6 100644 --- a/.github/ISSUE_TEMPLATE/config.yml +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -1 +1,5 @@ blank_issues_enabled: false +contact_links: + - name: Questions and usage help + url: https://github.com/intraq-dev-ai/intraq/discussions + about: Ask setup, usage, and roadmap questions in GitHub Discussions. diff --git a/.github/ISSUE_TEMPLATE/connector_request.yml b/.github/ISSUE_TEMPLATE/connector_request.yml new file mode 100644 index 0000000..a93a061 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/connector_request.yml @@ -0,0 +1,61 @@ +name: Connector request +description: Request or help implement a data-source connector. +title: "[Connector]: " +labels: + - enhancement + - type:connector + - area:data-sources +body: + - type: markdown + attributes: + value: | + Use this for public-source data-source connectors only. Do not include credentials, private endpoint URLs, customer exports, or non-public schemas. + - type: input + id: connector + attributes: + label: Connector + description: Which database, warehouse, file store, or API should IntraQ support? + placeholder: DuckDB, BigQuery, Snowflake, Athena, Trino, GCS, etc. + validations: + required: true + - type: dropdown + id: connector_type + attributes: + label: Connector type + options: + - SQL database / warehouse + - Object storage / files + - API / HTTP + - Other + validations: + required: true + - type: textarea + id: use_case + attributes: + label: Use case + description: What kind of reporting or dashboard workflow should this unlock? + validations: + required: true + - type: textarea + id: config + attributes: + label: Expected configuration + description: List only non-secret field names, not real values. + placeholder: | + host + port + database + schema + auth method + - type: textarea + id: acceptance + attributes: + label: Acceptance criteria + description: What should work when this connector is complete? + placeholder: | + - Connection test + - Schema discovery + - SQL Editor query preview + - Analyzer + - Dashboard Builder + diff --git a/README.md b/README.md index 7c29734..c2de367 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@ [![Docker](https://img.shields.io/badge/docker-compose-2496ED?logo=docker&logoColor=white)](docker-compose.yml) [![Node](https://img.shields.io/badge/node-24.x-339933?logo=node.js&logoColor=white)](.nvmrc) -[Website](https://intraq.dev) · [Docs](docs/DEMO_GUIDE.md) · [Quickstart](QUICKSTART.md) · [Configuration](docs/CONFIGURATION.md) · [Contributing](CONTRIBUTING.md) +[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.** @@ -92,6 +92,13 @@ 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. +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 Power BI Power BI is a broad reporting suite. intraQ focuses on natural language SQL, evidence-backed AI answers, and turning operational questions into live dashboard components from SQL-backed models. @@ -168,6 +175,7 @@ Focused workflow docs: - [SQL Editor](docs/SQL_EDITOR.md) - [MCP tools](docs/MCP.md) - [AI provider setup](docs/AI_PROVIDER_SETUP.md) +- [Comparisons](docs/comparisons/README.md) - [Publication checklist](docs/PUBLICATION_CHECKLIST.md) ## License and public source scope diff --git a/ROADMAP.md b/ROADMAP.md index b6d04b7..72a2986 100644 --- a/ROADMAP.md +++ b/ROADMAP.md @@ -10,19 +10,22 @@ This roadmap is intentionally focused on the public source package. - Generic sample sales dataset. - Metadata-grounded Analyzer and Dashboard Builder. - MCP tools for local product workflows. +- Public GitHub issue labels, discussions, templates, and connector roadmap. ## Next +- Fix remaining web type-check debt so the web workspace can become a required CI check. +- Add a real demo GIF showing: ask question → SQL/evidence → chart → saved dashboard component. +- Add smaller `good first issue` tasks around connector work. - More smoke and regression tests for Analyzer, SQL, dashboards, and setup. - Public sample question packs with expected SQL/results. - Better first-run onboarding. -- Smaller web bundles through route-level code splitting. -- More connector documentation. +- More connector documentation and connector test fixtures. - Clearer evidence surfaces for Analyzer and generated dashboards. ## Later -- Public-source connector examples. +- Public-source connector examples and runtimes, starting with DuckDB, BigQuery, and Snowflake. - More generic sample datasets. - Public benchmark reports for sample datasets. - Import/export for metadata models. diff --git a/docs/comparisons/README.md b/docs/comparisons/README.md new file mode 100644 index 0000000..f94ef7d --- /dev/null +++ b/docs/comparisons/README.md @@ -0,0 +1,11 @@ +# Comparisons + +These pages explain where IntraQ fits compared with common BI and AI analytics tools. + +The intent is practical positioning, not claiming that one tool is universally better. + +- [IntraQ vs Metabase](intraq-vs-metabase.md) +- [IntraQ vs Wren AI](intraq-vs-wren-ai.md) +- [IntraQ vs Power BI](intraq-vs-power-bi.md) +- [IntraQ vs Lightdash](intraq-vs-lightdash.md) + diff --git a/docs/comparisons/intraq-vs-lightdash.md b/docs/comparisons/intraq-vs-lightdash.md new file mode 100644 index 0000000..4e982ac --- /dev/null +++ b/docs/comparisons/intraq-vs-lightdash.md @@ -0,0 +1,34 @@ +# IntraQ vs Lightdash + +Lightdash is a BI tool built around dbt projects and metrics defined in code. + +IntraQ is focused on AI-assisted operational BI over SQL-backed data sources, with Analyzer, SQL Editor, Dashboard Builder, and metadata-guided answer generation. + +## Where Lightdash is strong + +- dbt-native analytics workflow. +- Metrics and dimensions defined close to transformation code. +- Strong fit for analytics engineering teams. +- Good version-control alignment when the organization already uses dbt. + +## Where IntraQ is different + +- Does not require a dbt-first workflow. +- Includes an AI Analyzer and dashboard-builder workflow. +- Designed for operational teams working from existing SQL data models, metadata, and dictionaries. +- Emphasizes evidence-backed AI answers and live dashboard components. +- Includes source-available self-hosted setup with local AI provider configuration. + +## Choose IntraQ when + +- You want natural language SQL and AI-generated dashboards over operational data. +- You do not have a mature dbt workflow. +- You need business users to move from question to dashboard component quickly. +- You want AI responses to expose assumptions, source context, and SQL evidence. + +## Choose Lightdash when + +- Your analytics layer is already built in dbt. +- Your team wants BI tightly coupled to analytics engineering workflows. +- You prioritize metrics-as-code over AI-assisted dashboard creation. + diff --git a/docs/comparisons/intraq-vs-metabase.md b/docs/comparisons/intraq-vs-metabase.md new file mode 100644 index 0000000..a2d2221 --- /dev/null +++ b/docs/comparisons/intraq-vs-metabase.md @@ -0,0 +1,34 @@ +# IntraQ vs Metabase + +Metabase is a mature self-service BI tool for querying databases, creating dashboards, and helping teams explore data. + +IntraQ is focused on AI-assisted operational BI: natural language SQL, evidence-backed answers, dashboard generation, and reusable SQL-backed components. + +## Where Metabase is strong + +- Mature query builder and dashboard workflows. +- Broad database support. +- Familiar self-service BI experience. +- Good fit for teams that primarily want classic dashboarding and exploration. + +## Where IntraQ is different + +- AI Analyzer is built into the workflow. +- Answers are expected to stay tied to SQL, selected data source, result rows, and assumptions. +- Dashboard Builder can turn AI answers into live dashboard components. +- Metadata, dictionaries, saved SQL models, relationships, and dashboard context guide AI behavior. +- Public source focuses on self-hosted operational analytics and local AI provider configuration. + +## Choose IntraQ when + +- You want an AI dashboard builder over SQL-backed operational data. +- You want natural language SQL with visible evidence and assumptions. +- You want to capture reusable dashboards from AI-assisted analysis. +- You are building embedded or internal reporting workflows around operational metrics. + +## Choose Metabase when + +- You mainly need a mature classic BI tool. +- Broad connector maturity matters more than AI-assisted answer verification. +- Your users prefer visual query building over AI-guided analysis. + diff --git a/docs/comparisons/intraq-vs-power-bi.md b/docs/comparisons/intraq-vs-power-bi.md new file mode 100644 index 0000000..c33c6fa --- /dev/null +++ b/docs/comparisons/intraq-vs-power-bi.md @@ -0,0 +1,34 @@ +# IntraQ vs Power BI + +Power BI is a broad enterprise analytics suite with a large ecosystem, Microsoft integration, modeling tools, and mature visualization features. + +IntraQ is narrower: self-hosted AI business intelligence for operational SQL-backed data, natural language SQL, evidence-backed answers, and AI-created dashboard components. + +## Where Power BI is strong + +- Enterprise reporting and governance. +- Rich visualization and modeling ecosystem. +- Microsoft 365, Fabric, and Azure integration. +- Large partner and consultant ecosystem. + +## Where IntraQ is different + +- Designed for source-available, self-hosted deployment. +- Focused on SQL-backed operational analytics. +- AI answers are expected to expose SQL, evidence, and assumptions. +- Dashboard components can be created from AI-assisted analysis. +- Local/admin-configured AI providers are part of the public app setup. + +## Choose IntraQ when + +- You want a lightweight operational BI workflow. +- You want AI-assisted SQL and dashboards without committing to a full enterprise BI suite. +- You want to inspect and self-host the source. +- You need product/internal reporting that can be embedded or automated. + +## Choose Power BI when + +- You need enterprise Microsoft ecosystem integration. +- Your organization already standardizes on Power BI. +- You need advanced enterprise governance and visualization breadth today. + diff --git a/docs/comparisons/intraq-vs-wren-ai.md b/docs/comparisons/intraq-vs-wren-ai.md new file mode 100644 index 0000000..ef8bd70 --- /dev/null +++ b/docs/comparisons/intraq-vs-wren-ai.md @@ -0,0 +1,47 @@ +# IntraQ vs Wren AI + +Wren AI focuses on natural language querying over a semantic/context layer and has broad connector ambitions. + +IntraQ focuses on verified operational BI: AI-assisted SQL, evidence-backed answers, dashboard creation, and reusable reporting workflows inside one self-hosted application. + +## Where Wren AI is strong + +- Natural language querying over modeled data. +- Connector breadth is a major product direction. +- Strong positioning around AI-native data access. + +## Where IntraQ is different + +- Combines Analyzer, SQL Editor, Dashboard Builder, data-source management, and MCP workflows in one app. +- Emphasizes answer evidence: SQL, selected source/model, row results, assumptions, and missing-context warnings. +- Turns AI-assisted answers into live dashboard components. +- Includes operational examples for hospitality/POS, energy retail, ecommerce, SaaS, and Postgres dashboards. +- Uses local/admin-configured AI providers rather than an IntraQ managed AI dependency in the public source. + +## Connector roadmap + +IntraQ already tracks connector parity work as public issues: + +- DuckDB +- BigQuery +- Snowflake +- Redshift +- Oracle +- Athena +- Trino/Presto +- Spark/EMR +- Doris +- GCS +- MinIO/S3-compatible storage + +## Choose IntraQ when + +- You want AI BI plus dashboard generation, not only natural language querying. +- You want evidence-backed operational dashboards. +- You want a source-available app that can be self-hosted and inspected. + +## Choose Wren AI when + +- Your top priority is semantic-layer-first natural language querying. +- You need one of its currently supported connectors before IntraQ supports it. +