Agentic analytics pilot
Agents that answer correctly, because the semantic layer is correct.
4-week pilot to deploy Conversational Analytics, Looker MCP or Gemini in Looker on one business domain, with agents wired to a verified semantic layer and the guardrails that make answers reliable. Quoted after the audit.
The problem
An analytics agent has no common sense: it answers with whatever the semantic layer gives it. On clean LookML, Conversational Analytics answers correctly and fast; on a neglected project, it confidently invents joins and totals. The pilot picks one domain (sales, for instance), verifies and enriches the semantic layer for agents (descriptions, synonyms, allowed measures), configures Conversational Analytics or a Looker MCP server for your tools (Claude, Gemini, Slack), writes the expected-question tests, and measures the exact-answer rate before opening up to more users. This is Neuravoid's "AI" offer: it is only sold after an audit or a semantic layer, never on an unverified project.
Who it is for
- Teams already on Looker who want to open data to business users without training everyone on explores
- Leaders who tried a BI chatbot and got wrong answers
- Product teams who want analyses in Slack or in Claude through Looker MCP
- Groups that want a governance frame before rolling out Gemini in Looker
What you receive
- Domain selection and 50 reference questions with business users
- Review and enrichment of the semantic layer for agents (descriptions, synonyms, excluded fields)
- Configuration of Conversational Analytics, Dashboard Agents or a Looker MCP server
- Test set: questions, expected answers, measured accuracy rate
- Usage rules: who may ask what, logging, costs
- Written wrap-up: accuracy rate, costs, extension plan
Out of scope
- Building the semantic layer itself if it does not exist (BigQuery, dbt and Looker stack offer)
- Custom application development outside Google tools and MCP
- Gemini and Looker licences, BigQuery costs
Prerequisites
- A Looker instance with an audited LookML project (or the LookML audit first)
- A business domain and a sponsor who answers in writing
- Gemini enabled on the Google Cloud project
How it runs
01
Week 1: scope and questions
Domain, 50 reference questions, state of the semantic layer.
02
Week 2: semantic preparation
Descriptions, synonyms, exclusions, allowed measures, tests.
03
Week 3: agents
Configuration, tool connections, first pilot users.
04
Week 4: measurement and decision
Accuracy rate, costs, feedback, documented extension decision.
StackLooker · Conversational Analytics · Gemini in Looker · Looker MCP · BigQuery · Claude / Slack (via MCP)
Frequently asked questions
Why not start directly with the agents?
Because results depend entirely on the semantic layer. Launching an agent on unverified LookML produces wrong answers and destroys trust. The pilot therefore requires a prior audit.
What accuracy rate should we aim for?
Above 90% on the 50 reference questions before opening beyond the pilot group. Below that, we fix the layer, not the agent.
Can we use Claude or another assistant instead of Gemini?
Yes, through Looker MCP: the server exposes the semantic layer to Claude Desktop, an in-house agent or Slack. Governance stays in Looker.
Does this replace dashboards?
No. Agents answer ad-hoc questions, dashboards remain the steering ritual. Both rely on the same layer.
Guides
6 min read · 18 September 2026
Looker MCP and Agentic Analytics: Semantic Layer First
Looker MCP server, Conversational Analytics and the Next '26 agents explained: what shipped, why most agentic projects fail, and the six LookML prerequisites.
Read the guide →9 min read · 5 September 2026
Conversational Analytics in Looker: 6 Prerequisites
What Conversational Analytics, Gemini in Looker and Looker MCP do in 2026, why they need a clean semantic layer, six prerequisites and a four-week pilot plan.
Read the guide →6 min read · 18 September 2026
Looker Consultant in 2026: Scope, Rates, Deliverables
What a Looker consultant costs in the US and UK in 2026, what a good one delivers, the red flags to avoid, and when a fixed-price audit beats an hourly rate.
Read the guide →Describe your context
Three lines are enough: your systems, what is broken, the deadline. Written answer within one business day.
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