Solid is an AI-powered data intelligence platform that automatically discovers, documents, and builds business-aware semantic models from your company’s existing data, queries, and tools. It makes your data AI-ready - so every BI dashboard, AI model, or “chat with your data” experience is accurate, explainable, and consistent.

Solid is built for everyone involved in the data journey:
‍

• Data Analysts teams - find, understand, and reuse trusted data and SQLs faster.
  
• Data engineers - automate semantic model creation and reduce manual upkeep.
  
• Data leaders - roll out AI initiatives confidently with governed, consistent data.
• Business users - benefit from clearer, faster, and more consistent outputs.

Those tools focus on breadth, but lack deep semantic understanding — which leads to inconsistent or incorrect answers.

‍

Solid integrates with them, providing trusted semantics and definitions so they become reliable and consistent.

BI tools visualize answers; AI tools make up things if they don’t have semantics. Solid helps teams systematically produce accurate answers. We analyze the metadata, and information about how the data is actually used, to enable discovery of the right data assets and processes, as well as the generation of highly-accurate, reliable, semantic models for the use of AI.

No. Solid integrates seamlessly with your existing stack - Snowflake, Databricks, BigQuery, Looker, Power BI, ChatGPT, Gemini, and more. It enhances what you already use, creating the semantic layer that makes those tools smarter and more consistent.

Solid connects to modern warehouses (e.g., Snowflake/BigQuery/Databricks), BI systems (e.g. PowerBI, Tableau, Looker, Sigma) and reads your internal content about your business and its data. The semantic models generated by Solid can be published to a variety of semantic layer technologies, including Snowflake Semantic Views, Databricks Genie Spaces and Metric Views, BigQuery LookML, ChatGPT prompts, dbt Semantic Layer MetricFlow, and more.

No. Solid integrates with your existing semantic layer and dbt code to both learn from them and enrich them with the semantic models it generates.

Solid doesn’t replace your existing data catalog solution’s governance; it operationalizes it. We ingest your definitions (e.g., metric YAML/dbt models) and bind them to the workflows analysts run. That means each analysis step references the sanctioned calculation and lineage, and the same question always runs through the same definition—by default.

You start seeing value on day one. Once connected, Solid automatically documents and ranks your metadata, then generates semantic models based on real past analyses and business context - no manual setup required.

‍

From there, Solid continuously keeps models up to date through semi-automated maintenance, drastically reducing the time spent on model management.
Your team also gains a chat interface that helps new analysts find the right data and context instantly, cutting onboarding and discovery time to a fraction.

Start by connecting your warehouse, BI, some unstructured documentation (about the data) and (optionally) dbt/catalog. Solid does not require extensive documentation though, as 95% of teams don’t have it anyway.

‍

Our platform then reviews the content, through a mix of GenAI, ML and deterministic software code, to generate a full semantic understanding of your data and business. This takes two weeks.

Pick 2-3 datasets you want to focus on, or a single domain in your organization. Ideally, that would entail several hundred tables and a handful of BI dashboards. Solid will retrieve the required metadata, including SQL queries, for that scope of data and deliver full, in-depth, high-quality documentation and several semantic models out of the box.

Teams using Solid see measurable impact within weeks. By automating documentation, semantic model creation, and maintenance, Solid dramatically shortens the time it takes to make data AI-ready.

‍

Typical outcomes include:

• 10× faster AI deployment - go from months to just a few weeks.
• 90% less manual work - eliminate repetitive modeling, testing, and documentation.
• 100× broader coverage - automatically model thousands of assets instead of a handful.
• More accurate, trusted insights - consistent models and definitions ensure every answer aligns across tools.
  ‍

Overall, Solid helps data teams deliver AI-ready, business-aware data faster, while reducing operational overhead and increasing confidence in every analysis.

Yes. Solid can generate SQL, but unlike generic AI tools, it doesn’t invent queries from scratch.

‍

Solid learns from the actual work of your top analysts - using verified queries, joins, and metric definitions that already exist in your organization.

‍

When it generates SQL, it’s reusing and adapting proven logic, not guessing. That’s how Solid eliminates hallucinated queries and ensures every result is consistent and trustworthy.

• Grounding: Workflows are pre-bound to high-quality datasets/metrics.
  
• Human-in-the-loop: Our own humans review the content generated by Solid, and our customers’ users review it as well. The vast majority of the work is done by AI, but the sign off and final review is by humans.
  
• Testing: We have advanced algorithms for testing the content we produce, and specifically how good the semantic models are to ensure AI uses them to produce the correct results.

Solid does not copy your underlying data. Instead, Solid focuses on metadata and information about the data. Still, we adhere to rigorous security standards, including SOC 2, and Azure’s security best practices.

Yes. Solid supports deployment models that keep data and execution inside your environment (e.g., private VPC on Azure), aligning with your security posture. There is an additional cost for this deployment model.