1. Harmonized context
Through one harmonized semantic layer, the AI understands your organization's exact definitions. No more debate over figures.
Analytics Answers Back: Enterprise-grade Conversational Analytics
No chat interface guessing answers. Using BigQuery and active AI agents, we transform Conversational Analytics from a novel feature into reliable action.
Why was revenue down last week?
Revenue −4.2% week over week. Nearly all of the drop sits in Retail.
Answered straight from your own data, with the table and the SQL behind it · source: BigQuery
Agent action
Agent posts the breakdown and the query in the thread
Orders dipped on Tuesday. What does it mean?
One-off effect: a delivery stop at two distribution centres.
- Operations note of 12 June on the delivery stop
- Carrier notification reporting the delay
- Same pattern in week 14, followed by recovery
Explanation with citations from your own knowledge base and external sources
Agent action
Agent records the explanation against the KPI, so nobody re-investigates it
Every Monday at 08:00: the weekly report.
Weekly report compiled with narrative, ready for review.
- Revenue and margin per channel
- Deviations against forecast, with explanation
- Open actions from last week
Periodic reporting, sent only after human review
Agent action
Agent sends the report to the leadership team once approved
How do we fix this?
Three measures, with expected impact and an owner.
- Reprioritise delivery for DC South, high impact
- Temporary margin agreement with procurement, medium impact
- Pull the Retail campaign forward to week 26, medium impact
Concrete measures and draft actions; a human stays in control
Agent action
Agent stages the actions as drafts in your planning
Why was revenue down last week?
Revenue −4.2% week over week. Nearly all of the drop sits in Retail.
Answered straight from your own data, with the table and the SQL behind it · source: BigQuery
Agent action
Agent posts the breakdown and the query in the thread
Orders dipped on Tuesday. What does it mean?
One-off effect: a delivery stop at two distribution centres.
- Operations note of 12 June on the delivery stop
- Carrier notification reporting the delay
- Same pattern in week 14, followed by recovery
Explanation with citations from your own knowledge base and external sources
Agent action
Agent records the explanation against the KPI, so nobody re-investigates it
Every Monday at 08:00: the weekly report.
Weekly report compiled with narrative, ready for review.
- Revenue and margin per channel
- Deviations against forecast, with explanation
- Open actions from last week
Periodic reporting, sent only after human review
Agent action
Agent sends the report to the leadership team once approved
How do we fix this?
Three measures, with expected impact and an owner.
- Reprioritise delivery for DC South, high impact
- Temporary margin agreement with procurement, medium impact
- Pull the Retail campaign forward to week 26, medium impact
Concrete measures and draft actions; a human stays in control
Agent action
Agent stages the actions as drafts in your planning
Built on BigQuery Cloud Run Agent Development Kit (ADK) Semantic data layer Vertex AI · Gemini Dataform Google Cloud IAM
Why traditional Conversational Analytics falls short
Querying data in natural language is the promise of Conversational Analytics. But a text response is only half the job. When underlying data sources disagree, the AI returns a different number every time. True value only emerges when answers build on one uniform definition and lead directly to follow-up actions in your operations.
Same question Three sources, three answers
The three pillars
From question to governed answer to action
2. BigQuery as the foundation
Conversational Analytics on massive data volumes, processed at speed from a reliable single source of truth.
3. From insight to workflow
Analytics Answers Back: responses do not stay as text on a screen, but trigger actions through integrated AI agents.
The solution
One agent that grows with you, across four levels
Each level builds on the previous one. You start with an answer and end with a draft action, with a human in control.
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Level 1 · Answer
“Why was revenue down last week?”
The agent answers straight from your data, with a table, the numbers behind it, and verifiable SQL.
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Level 2 · Explain
“Orders dipped on Tuesday. What does it mean?”
The agent explains, grounded in your own knowledge base and external sources, with citations.
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Level 3 · Report
“Every Monday at 08:00: the weekly report.”
The agent compiles periodic reports with narrative and sends them automatically, after human review.
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Level 4 · Advise
“How do we fix this?”
Concrete measures and draft actions, ready to execute. A human stays in control.
Data activation
From static reports to instant execution
Automated analysis with one-click approval.
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Proactive context: the AI does not just flag the anomaly, it explains why it happens based on your connected sources.
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Smart recommendations: the agent generates two or three concrete next steps with expected impact.
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Human-in-the-loop activation: one click on Accept and the agent starts the process in your CRM, ERP or marketing tool.
Weekly report · Monday 08:00
Action required
Stock-out risk garden furniture · €38k
Inbound is running 11 days behind the demand forecast. Root cause: delayed delivery confirmation from two suppliers.
Agent recommendation
- Reallocate webstore campaign +60% traffic
- Set stock threshold alert Risk −40%
Accept Dismiss
Business impact
The hard requirements for reliable data intelligence
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100% Fact-based: Thanks to semantic integration, the AI answers strictly on verified source data.
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Enterprise Governance: Access rights and data security from your Google Cloud environment remain fully enforced.
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Direct Decision Making: Meetings start with the decision instead of debating the accuracy of the data.
Approach
How we build it
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Map the data landscape
Sources, definitions and owners on the table. Where do the figures contradict each other?
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Foundation in BigQuery
One modelling layer, governed pipelines and access rights from your own Google Cloud environment.
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Definitions locked in
Metrics and dimensions defined once, so every answer returns the same number.
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Agents in the workflow
Answers wired to actions: alerts, reports and tasks inside your existing systems.
FAQ
Frequently asked questions
Is this just a chatbot on top of our dashboards?
No. The chat is only the entry point. The answer comes from a governed semantic layer on your data in BigQuery, ships with the SQL it used, and can then trigger an action through an agent. Without that layer, every question returns a different number.
Do our access rights still apply?
Yes. Rights and data security come from your own Google Cloud environment. Through Conversational Analytics a user sees exactly what they are allowed to see in the source, and nothing more.
What if our data is not in order yet?
Then that is where the work starts. We map sources, definitions and owners and build the foundation in BigQuery before switching on the conversational layer.
How do you prevent invented answers?
The AI is not free to interpret: metrics and dimensions are fixed in the semantic layer. Answers reference the definition and source used, so every figure is traceable.
What does a first engagement deliver?
A working answer to a real question from your organization, on your data, plus one agent action that follows from it. From there we expand per domain.