Agency Intelligence Β· Experimental

Who does the agency
actually buy media for?

Agency β†’ clients β†’ their media β†’ the agency's scope. Evidence with citations, the historical AI Digital billings baseline, and an honest "insufficient data" when the numbers are not there.

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01
Agency
Name or domain is enough. Everything else narrows the search and lowers the cost.
Known values are stored next to the result for validation. They never feed the prediction.
Limits in this mode
Actual Apify usage can differ from the cap; SEMrush and the LLM are billed separately.
Lab
Research a US agency
Experimental research; amounts may reflect revenue or contract values rather than advertising spend.
More identifiers (optional): LinkedIn, legal entity, Clutch, city, HQ country, company type
Checks the agency is US-based, then gathers public evidence from every source. Usually 2–4 minutes; results appear as each step finishes.
Identity details
Agency
Research progress

Summary

Financial evidence

Agency scale

Media buying capacity

Documents

Managed media spend

Bottom line

What's new from the last run
Sources β€” status of each source, re-run one
Strategist override (audited) β€” US eligibility, company type
All facts β€” source link, identity status, review
Source runs β€” actor, run id, build, input hash
02
Pipeline
Each stage reports its own state; a failed source makes the run partial, not empty.
Stages

03
Executive summary
Three targets, three units. A null is a finding, not a failure.
Agency

Benchmark comparison

Discovered clients

Historical and unverified relationships are shown but never attributed. Change a status with a reason; the original evidence stays.

Media capabilities

How the estimate was built β€” methods, inputs, assumptions, what would improve it
Sources & run diagnostics β€” every actor run, cache hits, costs, evidence
Copyable summary

        
04
Recent investigations
Stored on this server; open one to review or correct it.
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