Experience
Data engineering, Power BI, and applied AI.
The founder's background across the Microsoft data stack, and how the same approach applies to agency reporting. Nothing on this page is presented as a ClarusIQ client result.
From the founder's background
Work delivered in previous roles, before ClarusIQ, described in general terms. These are not ClarusIQ client engagements.
Data Engineering
Consolidating hand-built pipelines into one metadata-driven design
Hundreds of pipelines reduced to a few dozen
A reporting environment had grown to hundreds of individual data pipelines, each built by hand, so a single schema change meant hunting down duplicate logic across many files. Rebuilding the architecture around one metadata-driven design reduced that to a few dozen pipelines, each extended through configuration instead of new code, so a new data source plugs into an existing pattern instead of needing its own build.
Power BI Reporting
Automating a large Power BI reporting library
Hundreds of reports, one build process
A Power BI reporting library had grown to hundreds of individual reports, most built and maintained by hand, each drifting slightly from the others over time. Replacing that with a single automated build process meant reports no longer had to be rebuilt or patched one at a time.
Applied AI
Building an AI-connected analytics application
Data-connected AI
An analytics application was built to connect AI directly to an underlying data model rather than treating it as a bolt-on feature, so its output stayed tied to real, defined data instead of operating as a generic, ungrounded chat interface.
For marketing agencies
What this looks like applied to agency reporting.
ClarusIQ hasn't yet delivered a marketing-agency engagement to publish as a named case study. The two scenarios below are demonstration scenarios: a concrete illustration of how the same approach applies to agency reporting, not a completed client engagement.
Marketing: Agency Reporting
Demonstration scenario
One reporting model for every client account
12 client accounts, one report template
A mid-size performance marketing agency ran a dozen client accounts, each with its own hand-built reporting spreadsheet pulling from Google Ads, Meta Ads, and a CRM. Blend spend, leads, and CRM pipeline data into a single Power BI semantic model with one consistent definition of ROAS and cost-per-lead, and every account manager opens the same report structure instead of rebuilding it from scratch each cycle.
Marketing: Applied AI
Demonstration scenario
Drafting the client narrative automatically
Grounded, client-ready summaries
Account managers were spending hours each month writing the 'what happened and why' narrative behind each client's numbers. Ground an AI summary step directly in the validated campaign data model, producing a first-draft performance narrative account managers review and send, instead of a generic chatbot answering from ungrounded data.
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