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Analytics & Agentic Activation

Dashboards your teams check daily, and AI agents they can question in plain language, both reading from the same governed definitions. Activation is the word that matters: analytics only counts when people act on it.

When to bring us in

The problems this solves

Reports without decisions

Dashboards exist, but the Monday meeting still runs on gut feel and a screenshot from last quarter.

Competing numbers

Every team reports a slightly different figure for the same metric, and reconciling them is someone’s job.

The analyst bottleneck

Leadership waits days for answers that require a person to assemble data by hand.

Adoption stalled

The BI rollout launched with fanfare, usage peaked in week two, and it never recovered.

How it fits together

One set of definitions, every way you ask

A dashboard and an agent answering from the same semantic layer. When the numbers match everywhere they appear, trust follows, and so does usage.

Diagram showing a business intelligence dashboard and a conversational AI agent side by side. The agent answers a plain-language question about net revenue with a governed figure and a link to view the generated SQL. Both the dashboard and the agent draw from a single governed semantic layer of shared metric definitions.

Dashboards for monitoring, agents for questions, one governed semantic layer underneath both.
What’s included

What analytics and agentic activation includes

We build the reporting layer and the conversational layer together, because they live or die on the same definitions.

  • KPI and metric definition workshops that end in agreement, recorded in code
  • Semantic layer design, so definitions live in one governed place
  • Dashboard development in Power BI, Tableau, Looker, or Sigma
  • Conversational AI agents over governed data, with the SQL visible
  • Verified queries for your highest-stakes questions
  • Report rationalization: fewer reports, better ones, trusted
  • Adoption support: training, office hours, and iteration on real usage
  • Usage analytics on the analytics, so we know what is landing
Our approach

How we work

01

Agree the metrics

The hard conversations first: what does revenue mean, and who owns the definition.

02

Model the semantics

Definitions, joins, and synonyms encoded once, governed like the code they are.

03

Build both surfaces

Dashboards for monitoring, agents for questions, reading from the same layer.

04

Drive adoption

Launch is the midpoint. Training, feedback loops, and iteration on what people actually use.

Outcomes

80%

reduction in manual reporting effort for a manufacturing client after we centralized reporting on Snowflake and dbt

Time moved from preparing data to analyzing it. That is the trade every reporting team is trying to make.

Power BITableauLookerSigmaSnowflake Cortex
Questions we hear

Frequently asked questions

Do AI agents replace dashboards?

No, and most organizations will run both. Dashboards are better for monitoring the same view every day; agents are better for the question that did not exist yesterday. They should share one set of definitions.

How accurate are natural-language answers?

Honest answer: not perfect, especially on complex multi-step questions. We ground agents in semantic definitions and verified queries, keep the generated SQL visible, and set expectations with leadership accordingly. Teams that promise infallibility lose the room after one bad answer.

Which BI tools do you work with?

Power BI, Tableau, Looker, and Sigma among others. Support for consuming governed semantic definitions varies by tool, and we will tell you candidly where each one stands before you commit a team to it.

What does adoption support actually look like?

Training sessions, office hours, a feedback loop into the backlog, and honest usage reporting. If a dashboard is not being used, we would rather find out why and fix or retire it than let it decay.

Answers your whole team can trust

If your reporting exists but decisions still run on gut feel, the gap is usually trust and adoption. Let’s close it.

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