Product family
Product conceptAI Analytics Copilot
Plain-language questions, governed metrics, transparent queries.
A natural-language interface over approved datasets and metric definitions that generates SQL, shows its working, and stays inside your semantic layer and access rules.
Product concept. This is not a self-serve product yet. There is no public account, live dashboard or price list; we deliver it as a configured build and will show you the architecture, not a staged demo.
/ 01Overview
What it is and why it matters
Business users wait on analysts for questions that should take seconds. Text-to-SQL looks like the answer, but raw schemas are ambiguous: three tables called "orders", revenue defined four ways, columns nobody understands. Without guardrails, an analytics copilot produces confident numbers that are wrong.
The AI Analytics Copilot concept works through a semantic layer of approved metrics, dimensions and joins. Questions are mapped to those definitions first, SQL is generated against curated views only, and the answer shows the query, the definitions used and any assumptions. Row-level security is enforced by the database, not by the prompt.
It is a product concept delivered as a build, and it is only as good as the metric definitions behind it, which is why data modelling is part of every engagement.
/ 02Features
How it works
01
Semantic layer first
Questions resolve to governed metrics and dimensions before any SQL is written.
02
Transparent answers
Every result shows the generated query, filters, time range and metric definitions used.
03
Database-enforced access
Queries run with the user’s permissions so row- and column-level security stays authoritative.
04
Clarifying questions
Ambiguous requests prompt a clarification instead of a guess.
05
Charts and narratives
Results come back as tables and charts with short written summaries, marked as generated.
06
Golden question tests
A suite of questions with known answers is run on every change to catch regressions.
/ 03Actual capabilities vs roadmap
What a first release includes, and what it does not
In scope for a first build
- Natural-language to SQL over curated views
- Metric and dimension catalogue
- Visible queries and assumptions
- Role-based data access
- Evaluation against golden questions
Roadmap, not yet delivered
- Scheduled insight digests
- Anomaly explanations on key metrics
- Spreadsheet and BI tool add-ins
/ 04Use cases
Where it earns its keep
Sales and pipeline questions
Leaders ask about pipeline movement or win rates by segment without waiting for a custom report.
Operations monitoring
Supervisors check volumes, backlogs and service levels in plain language during the day.
Finance self-service
Budget holders explore spend against plan using finance-approved definitions.
Related solutions: Finance & Compliance, Sales & Lead Operations, IT & Engineering
/ 05Integrations
Connects to the systems you already run
Listed systems are integration targets reached through their documented APIs. Their mention does not imply a partnership or endorsement.
Data platforms
- Snowflake
- BigQuery
- Databricks
- PostgreSQL and SQL Server
Semantic and BI
- dbt semantic models
- Existing BI metric definitions
- Power BI or Looker for downstream reporting
/ 06Security and governance
Designed for oversight
- Read-only service paths; the copilot cannot modify data.
- Queries execute under the requesting user’s data permissions.
- Query cost limits and timeouts protect the warehouse.
/ 07Demo
No live product demo yet
There is no live demo of AI Analytics Copilot because it is not a released product. We can walk you through the architecture and a scoped first build instead.
/ 08Pricing or quote
Priced to your scope, never invented
Scoped per engagement. Effort is driven mainly by the state of your data model and metric definitions.
/ 09FAQ
Questions about AI Analytics Copilot
Can it query any table in our warehouse?
By design, no. It works against curated views and defined metrics. Open access to raw schemas is the main cause of wrong answers in text-to-SQL systems.
How do we know an answer is right?
Each answer exposes its query and definitions, and the system is regression-tested against questions with known answers. Analysts can review and correct definitions centrally.
Does it replace our BI tool?
No. It complements dashboards for ad-hoc questions. Recurring reporting usually still belongs in BI.
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Explore AI Analytics Copilot with our team
A 45-minute conversation with an engineer: your workflow, the architecture, the risks and what a first version would include.