Make your data ready for AI to answer from
For organisations that want to 'talk to their data' with AI and need the governed foundation that makes the answers trustworthy.
Point an LLM at your raw tables and it will answer every question: some of them wrong, all of them confident. It does not know that 'revenue' excludes intercompany, or how two tables should join. That is not an AI problem; it is a missing semantic layer.
We build that layer: the governed definitions, metrics and join logic that turn 'talk to your data' from a demo into something you can actually rely on, on Databricks Genie, Snowflake Cortex, or your BI semantic model.
Understand first, then build
- 1
Understand
We work with your subject-matter experts to pin down what each metric truly means and where the ambiguity lives.
- 2
Define
We encode governed metric definitions, entity relationships and join logic in a semantic layer the AI tools consume.
- 3
Test against reality
We test the AI's answers against known-correct results and tighten definitions until it is reliable, not just plausible.
- 4
Govern
We put change control around the definitions so the layer stays trustworthy as the business evolves.
Concrete deliverables
- Governed metric and dimension definitions
- Entity relationship and join-logic modelling
- Semantic layer for Databricks Genie / Snowflake Cortex / BI models
- Answer-accuracy testing against known results
- Governance and change-control process
Tools we typically use
- Databricks Genie
- Snowflake Cortex
- dbt
- Power BI
- Tableau
Not sure which tool fits? That’s our job. We recommend based on your stack and team, not our preferences.
Talk it through with usRelated work
Large government department
We piloted Databricks Genie Space with a large government department and built the semantic layer needed for reliable conversational analytics.
Fewer ad-hoc analysis requests; analysts freed for higher-value work
Read the case study
Department of Transport and Main Roads (Queensland)
We developed an automated monitoring tool on Snowflake and Tableau to identify South East Queensland road corridors needing intervention and prioritise investment.
Defensible, automated corridor prioritisation
Read the case study
Komatsu Australia
We transitioned Komatsu Australia from a cumbersome manual process to automated financial reporting in Power BI, integrating data from SAP and Palantir Foundry.
50+ staff hours saved every month
Read the case studyThe questions buyers ask us
- What is a semantic layer, and why does AI need one?
- A semantic layer is the agreed, governed definition of your business concepts: what each metric means, how entities relate, how tables join. AI needs it because a language model reading raw tables has no way to know your rules; it will infer them and often get them wrong. The semantic layer gives the AI the correct definitions to answer from, which is the difference between an impressive demo and a trustworthy tool.
- How do you make 'talk to your data' reliable?
- We define the metrics and joins in a governed semantic layer, then test the AI's answers against results we already know are correct, tightening definitions until it is dependable. We did exactly this piloting Databricks Genie with a large government department, which reduced ad-hoc analysis requests once analysts could trust the answers.
- Do you offer AI-ready data consulting in Australia?
- Yes. Motta Consulting is an AI-ready data consultant working with clients across Australia from a Brisbane base. We build the governed semantic layer (metric definitions, entity relationships and join logic) that lets AI answer questions about your business reliably, on Databricks Genie, Snowflake Cortex or your BI semantic model, and test the answers against known-correct results.
- Does this work with our existing tools?
- Yes. The semantic layer sits on top of whatever platform you use (Databricks Genie, Snowflake Cortex, or the semantic models in Power BI and Tableau) so you are enhancing your current investment, not replacing it.
More in Data Engineering
Databricks done right: lakehouse, pipelines and AI
For data teams standardising on Databricks who want a governed lakehouse and reliable pipelines (and, increasingly, trustworthy AI on top).
Learn moreSnowflake consultingA Snowflake platform your whole business can rely on
For organisations building (or fixing) a Snowflake data platform who want reliable pipelines, controlled cost, and one source of truth.
Learn moreReady to talk it through?
For organisations that want to 'talk to their data' with AI and need the governed foundation that makes the answers trustworthy.