Looker

Looker is the BI layer for companies that take their data seriously. Built on LookML, a code-based semantic modelling language, it turns your warehouse into a governed source of truth that every team queries from, not their own copy-pasted spreadsheets. Google acquired it in 2019, folded it into Google Cloud, and has since rebuilt it around Gemini for conversational and agentic analytics.

What Looker Does

Looker sits on top of your cloud data warehouse and pushes queries down rather than extracting data into its own engine. That means dashboards reflect live warehouse state, and your metric definitions live in version-controlled LookML alongside the rest of your codebase.

• LookML semantic layer: a single, version-controlled definition of every metric, dimension, and join across your business.
• Explores and dashboards: drag-and-drop self-serve analysis for business users, all grounded in the governed model.
• Looker Embedded: white-labelled analytics inside your SaaS product, with signed SSO, row-level security, and per-tenant theming.
• Native warehouse connections to BigQuery, Snowflake, Redshift, Databricks, Postgres, MySQL, SQL Server, and 60+ others. Queries push down, data stays in the warehouse.
• Scheduled deliveries and alerts: dashboards to email, Slack, Google Drive, SFTP, or webhook on a cadence or when a metric crosses a threshold.
• Action Hub and Data Actions: trigger writes back to Salesforce, HubSpot, Slack, Zendesk, or any webhook directly from a dashboard row.
• Looker API and SDK: programmatic access to every model, query, and dashboard for custom apps, automations, and CI/CD pipelines.

Gemini in Looker

Gemini is now embedded across Looker. Conversational Analytics lets users ask plain-English questions and get answers grounded in LookML, not hallucinated SQL. Dashboard Agents summarise visualisations in context and let users follow up without leaving the dashboard. A LookML AI Agent in the VS Code extension translates business intent into production-ready model code. The semantic layer is what makes this safe at scale, the AI reasons over governed definitions, so a question about "net revenue retention" returns the company's actual NRR formula, not whatever an LLM guesses.

Automations We Build with Looker

Most teams treat Looker as a place dashboards live. We treat it as the metric engine other systems pull from. Once the semantic layer is clean, every downstream automation gets sharper, because everyone is measuring the same thing the same way.

• Customer health score back to CRM: nightly Looker job calculates account health from product usage, support tickets, and billing, then writes scores back to Salesforce or HubSpot via Action Hub for CS to action.
• Threshold-triggered Slack alerts: pipeline coverage drops below 3x, MRR churn spikes, a paying account stops logging in. The relevant channel gets a message with a dashboard link and the AE or CS rep tagged.
• Embedded customer-facing analytics: we build the secure SSO, row-level filtering, and theming so your customers see only their own data inside your product, branded as yours.
• Conversational analytics in Slack: a Gemini-powered Looker bot that answers metric questions from the C-suite without anyone needing to open the BI tool.
• Board pack and investor update auto-generation: scheduled Looker deliveries feed templated Google Slides or Notion docs, so the monthly investor update is 80% done before anyone touches it.
• LookML CI/CD pipeline: Spectacles or Looker's built-in CI validates SQL and content tests on every PR, so a junior analyst can ship model changes without breaking the executive dashboard.

Why Teams Choose Looker

• One definition of every metric. LookML enforces a single source of truth, so finance, product, and CS stop fighting over whose ARR number is right.
• Warehouse-native performance. Queries push down to BigQuery, Snowflake, or Redshift, so dashboards scale with your data, not a separate Looker cube.
• Embedded analytics that fit a product. Signed SSO, row-level security, and full theming make it the default choice for SaaS companies shipping customer-facing dashboards.
• Gemini grounded in your model. Conversational AI that quotes your actual metric definitions instead of hallucinating SQL, because the semantic layer is the prompt context.
• Code-based development. LookML lives in Git, supports PRs, code review, and CI/CD. Analytics ships like software.

Looker connects natively to BigQuery, Snowflake, Redshift, Databricks, Postgres, and 60+ other sources, with deep two-way integrations into Salesforce, HubSpot, Slack, Google Workspace, and Marketo via the Action Hub. Pricing is custom and quote-based: platform fees typically start around $35k/year for the Standard tier and scale into six figures for Enterprise and Embed, with per-user licences for Developer, Standard, and Viewer roles on top. If you have a modern data stack and need to operationalise it across the company, that's the build we do.