Looker
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Looker Consulting & Implementation
Most Looker instances drift into dashboards nobody trusts. We build the governed semantic layer that keeps every number the same.
Looker shines when your team treats LookML like real code. We design the semantic layer, model the explores, and ship dashboards that stay trustworthy as your business changes — governed, version-controlled, and adopted by the teams that asked for them. Senior engineers, production-grade from the first commit.
Looker · live signal
What we've shipped with Looker
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LookML projects
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explores shipped
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governed by default
Point of view
Why most Looker dashboards drift
Looker's promise is one definition of every metric, modelled once in LookML and reused everywhere. Its failure mode is the opposite: LookML treated like throwaway config, explores added ad-hoc, the same measure defined three different ways across three models. The dashboards still render. They just stop agreeing with each other.
We've seen the same drift across e-commerce and multi-location operators: a Looker instance that worked when one person owned it, then fractured as stakeholders multiplied. "Revenue" means one thing in the finance Look and another in the marketing one. Every meeting reopens which number is right.
The fix isn't a new BI tool. It's LookML modelled like real code — a clean model/view/explore structure, one measure defined once and reused, permissions and folders that survive an org change, and version control so a change is reviewed before it ships. Looker was built to be the source of truth; most instances just never enforced it.
We implement it the way it should have been built the first time: a semantic layer mapped to how your business actually reasons, governed access, embedded analytics with real tenant isolation where you need it, and documentation your team can run with. One definition, every number the same.
Representative result — the same measure redefined across explores until no two dashboards agree.
Metrics that reconcile
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After
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When Looker makes sense
Pick Looker when…
- You want a true semantic layer
- You need governed, version-controlled BI
- You have many stakeholders, one source of truth
- You want embedded and Gemini-powered analytics
What we implement
Beyond the tutorial.
01
LookML architecture
Models, views, explores designed to scale past the tutorial examples.
02
Dashboard suite
Executive, operational, and self-service dashboards adopted by the teams that asked for them.
03
Governance
Permissions, content access, and folder structure that survive org changes.
04
Embedded analytics
Customer-facing Looker with proper tenant isolation.
In production
What this looks like in production
We don't theorise about governed BI — we ship it. Two engagements where a disciplined Looker semantic layer made every team read the same number.
Netthandelsgruppen — e-commerce
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reduction in reporting time after unifying Shopify, WooCommerce & marketing data behind one Looker semantic layer
One source of truth for every metric · dbt + Databricks + Looker
ShotVet — multi-clinic veterinary group
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faster onboarding of new clinics once KPIs were standardised in modelled reporting, not per-manager spreadsheets
Standardised KPIs across every clinic · Looker + Sigma + Snowflake
FAQ
Questions we get a lot.
What does a Looker consulting engagement actually include?
A diagnosis first — we read your LookML for duplicated measures, sprawling explores, and metrics that don't reconcile across dashboards. Then implementation: a clean model/view/explore architecture, one definition per metric, governed permissions and folders, embedded analytics where you need it, and documentation. Every engagement ends with an instance your team can operate without us.
How is this different from hiring a Looker developer or a big agency?
You work with senior engineers directly — the people writing the LookML, not a rotating bench of juniors. We take fewer clients so each one gets real attention. If your problem is a focused cleanup, we'll tell you that instead of selling a multi-month build.
Can you fix our existing Looker instance, not just build a new one?
Yes — most of our Looker work is governing an instance that drifted. We consolidate the duplicated measures into a single definition, restructure explores, fix permissions and folders, and add version control so changes are reviewed before they ship. We do it incrementally so reporting keeps working while we work.
Do you work with teams outside the US?
Yes. We're based in Mumbai and deliver globally — clients across the US, Europe, and the Middle East. Async-friendly, time-zone aware.
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