For Andra and the Airalo data team

Every path to your data runs through Lightdash.

Dashboards, self-service, Lightdash agents, Claude, Slack bots: five ways in, one governance layer behind all of them. What we build for each, and how we run the work.

Bijan Soltani · Founder & Managing Director, Gemma Analytics

Hi Andra,

Good talking through where Airalo's data setup is heading. Self-service in Lightdash, a semantic layer that can carry more weight, and AI enablement on top of Lightdash, with the org-wide BigQuery access line you drew. All three are things we work on every week.

This page makes that concrete: what data access looks like with Lightdash in the middle, and how we'd run the engagement, measured against what you told me matters most.

The usage side

How your teams work with Lightdash

Whoever is asking, and whichever tool they ask from, the request goes through Lightdash's semantic layer and its permissions. No direct BigQuery access, for anyone.

Dashboards

Ready-made, per team.

Self-service

Explore and build directly in Lightdash.

Lightdash agents

Ask questions or sketch charts inside Lightdash.

Claude Code / Codex

Via Lightdash MCP: ask questions, or build dashboards through the API.

Slack bots

Ask in Slack, get the answer from Lightdash.

Lightdash Semantic layer · Metrics + dimensions · Row filters compiled into the SQL

BigQuery + dbt models

What we do for you

Implementation and support

Every path above is something we can build or set up for you.

Dashboards and self-service: Migrate legacy BI and build out the semantic layer

Looker and QuickSight ported over, semantic layer first. Then new metrics and dimensions modeled in dbt, so more of the org's questions have an answer in Lightdash.
Lightdash agents: Configure one agent per team or function

Each with the context its domain needs, so "a year" means the same thing to the agent as to the team using it.
Claude Code / Codex: Set up Lightdash MCP for Claude

Claude only ever goes through the governance layer and inherits the user's access, so someone sees in Claude exactly what they would see in Lightdash.
Slack bots: Build the data-analyst bot

On Lightdash MCP, in the channels people already use. When it can't answer, it opens a ticket for your BI team.
Every path: Make it reliable

Golden records and evaluation sets with expected answers, so you can measure agent quality.

Process

How an engagement with us runs

One named lead, one named team

The person who scopes the work stays accountable for it from the first call to handover, and the team stays on it for the duration, whether that is a short audit or several months. Nobody gets swapped out halfway.

Scope defined before the engagement starts

Broken down into milestones such as modeling, then dashboard building, each with its own out-of-scope line, so the work never blurs into endless iteration.

Price certainty

Fixed price or time and materials, agreed before the work starts and not renegotiated once we are in it.

A weekly feedback cadence

A sync every week, notes in your channel, so problems surface while they are still small.

The first step

A semantic-layer audit

We read the codebase and give you recommendations you can act on with or without us. Small enough to fit a Q4 budget.