Most teams don't have a reporting problem. They have a reporting infrastructure problem. Looker Studio can fix it, if it's built right.
We design and build Looker Studio environments the way they should work from day one: a modeled data layer instead of forty inconsistent reports, real governance instead of guesswork, and dashboards your team can actually trust from source to insight.
Anyone on the team can spin up a new report, and now nobody's sure which version is the source of truth.
Native Google Ads and Analytics reporting is great, but blending in other sources gets messy fast.
Dashboards slow to a crawl once a report tries to blend several large data sources directly.
Naming conventions and calculated fields get rebuilt slightly differently in every report.
There's no real governance layer. Anyone with edit access can change a metric definition.
We design and build report environments from the ground up: data source connections, calculated fields, blending logic, and dashboard layouts that hold up at scale.
For teams that need workspace-level controls, we handle Pro configuration, team workspaces, and organizational settings so access and ownership are clear from day one.
We map every source your reporting depends on and choose the right connector approach (native, partner, or warehouse-backed) based on volume, refresh needs, and cost.
For environments backed by Looker (the platform), we build and maintain LookML models so every metric is defined once, centrally, and inherited consistently across every report.
We put structure around your Looker Studio environment: naming conventions, folder hierarchies, access controls, and a documented metric library your team can actually maintain.
Slow dashboards usually mean the data architecture underneath needs work. We diagnose the bottleneck - whether it's an inefficient blend, a missing extract, or a query that should hit a warehouse instead and fix it.
We train your team to build inside the environment we create, so standards hold after we hand over the keys.
Your Data Is Ready. Are Your Dashboards?
Book a free Looker Studio consultationWhat “Modeled” Really Means
Every Looker Studio pitch mentions “the data layer” like it's a checkbox. It isn't. It's the difference between a dashboard that survives a headcount change and one that quietly breaks the day its builder leaves.
A modeled layer means your metrics are defined once, upstream, in a place every report inherits from, not recalculated inside forty different Looker Studio files with forty slightly different assumptions about what counts as a “conversion” or a “qualified lead.” When that layer lives in BigQuery, it also means the heavy lifting, joins, filtering, aggregation - happens in a warehouse built for it, and Looker Studio is left doing what it's actually good at: rendering. That's the real fix for slow dashboards. Not fewer charts. Not lighter formatting. A warehouse doing the work instead of the canvas.
We build the layer most agencies skip.
Most Looker Studio work stops at the dashboard. We start with the data model, because that is where consistency, performance, and trust actually come from.
We work across the full Google stack.
GA4, Google Ads, BigQuery, Campaign Manager, Search Ads 360 - we know how these sources behave, where they conflict, and how to reconcile them without fudging the numbers.
We do not hand you a report and disappear.
Governance documentation, metric libraries, access structures - we make sure the environment functions after the project ends, not just on demo day.
We right-size the solution.
Not every team needs a warehouse. Not every team needs Looker Pro. We recommend what your data volume, team size, and budget actually call for.
Your Data Is Ready. Are Your Dashboards?
Book a free Looker Studio consultationWe review your existing reports, data sources, and access structure to find what is broken, duplicated, or missing.
We design the data layer: source connections, extract strategy, semantic definitions, and folder governance.
We build the modeled layer and the reports on top of it, following documented naming and metric standards.
We reconcile numbers across sources and sign off that definitions match what the business expects.
We document everything and train whoever owns it internally.
We review your existing reports, data sources, and access structure to find what is broken, duplicated, or missing.
We design the data layer: source connections, extract strategy, semantic definitions, and folder governance.
We build the modeled layer and the reports on top of it, following documented naming and metric standards.
We reconcile numbers across sources and sign off that definitions match what the business expects.
We document everything and train whoever owns it internally.
Teams often ask whether they should just hire a BI analyst instead of bringing in outside help. It's a fair question, and the honest answer depends on what you're actually solving for. A full-time hire makes sense when reporting needs are permanent, growing, and central to how the business runs day to day.
An implementation partner makes more sense when the need is architectural - you need the model built correctly once, documented, and handed off rather than an ongoing headcount. Most teams we work with are somewhere in between: they need the foundation built by someone who's done it dozens of times, and then they need their own team trained to maintain it. That's the engagement shape we're built for, and it's also why the handover and training phase isn't an afterthought in our process, it's the point where the investment either sticks or doesn't.
The failure pattern is consistent enough that we can spot it before the first call ends. Someone technical builds the first version of a dashboard fast, under deadline pressure, blending sources directly because it's the quickest path to a working report. It looks fine at ten rows of data and thirty. It starts lagging at scale, so someone adds a filter to mask the slowness.
Then a second analyst joins, doesn't know the first person's naming conventions, and builds a parallel metric with a different definition because rebuilding the original from scratch is faster than reverse-engineering it. Six months later, there are three “monthly active users” numbers floating around the org, and nobody wants to be the one who says out loud that leadership has been looking at inconsistent data in board decks. None of this happens because anyone was careless. It happens because nobody was responsible for the layer underneath the reports and that's precisely the gap this engagement closes.
Those who run paid media, web analytics, or both and need reporting that spans more than one source without breaking.
Inside companies that have standardized on Google Cloud and need Looker Studio to behave like enterprise BI without the enterprise price.
Those who are responsible for data quality but do not have the engineering headcount to build a modeled layer from scratch.
Those managing reporting for multiple clients who need a repeatable, scalable environment that does not require rebuilding from zero for every account.
Whether you're standing up Looker Studio for the first time or cleaning up a sprawl of untrusted reports, the starting point is the same: a free assessment of your data sources, reporting needs, and what a properly modeled environment would involve.
Book a Free Looker Studio AssessmentThe core product is free to use. Looker Studio Pro adds team content management, service-level support, and enterprise access controls for a per-user, per-project fee worth it once multiple teams are managing shared reports.
Yes, beyond native Google Ads, Analytics, and BigQuery connectors, there are hundreds of partner and community connectors for other platforms, plus the ability to blend in data via BigQuery or Sheets.
This usually happens when reports blend large raw sources directly instead of reading from a modeled, pre-aggregated layer. Building that layer in BigQuery is the fix, and it's a core part of our implementation approach.
Yes, through folder structure and sharing permissions and Looker Studio Pro if you need more formal access management across teams or clients.
If you need enterprise governance, complex row-level security, or very large-scale modeling beyond what BigQuery-backed Looker Studio can reasonably handle, a platform like Looker, Tableau, or Power BI may be the better fit, we'll tell you honestly if that's where you are.
Timeline depends on the number of data sources and complexity of the modeling layer required.