Research evidence changes quickly and comes from inconsistent sources
Media & Entertainment / Delivered project
AI for Creative Intelligence
A governed multi-agent workspace for gathering evidence, comparing concepts, and organizing audience and content signals.
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StatusDelivered project
IndustryMedia & Entertainment
SystemData → Intelligence → Action
01 / Problem statementDelivered project · Media & Entertainment
The operating problem
Creative strategy teams assemble market, audience, and performance evidence manually across disconnected sources and inconsistent formats.
Conditions the system must survive03 operating constraints
Generated interpretation must remain separate from observed data
Creative teams need breadth without losing provenance or editorial judgment
02 / How we approached it
From operating uncertainty to testable evidence.
The work was decomposed into four engineering decisions. Each one produced an artifact the customer could inspect, test, and carry into deployment.
01Frame
Connect approved research and performance sources
Evidence produced
Research theme, source, and evidence-quality policy
02Evaluate
Assign narrow research tasks to observable agents
Evidence produced
Agent task benchmark for retrieval, comparison, and synthesis
03Engineer
Normalize evidence into comparable structures
Evidence produced
Citation and interpretation-boundary review with strategists
04Operationalize
Require source citations and strategist review
Evidence produced
Reusable workspace for briefs, evidence, and editorial approval
03 / Deployment record
Deployed around the workflow—not beside it.
The system boundary includes where inference runs, how evidence reaches existing tools, and how people handle uncertainty after launch.
Implementation statusDelivered project
01
TopologyCloud agent workspace with source connectors, isolated task execution, and a structured evidence store.
02
IntegrationApproved research, audience, performance, and content systems contribute permission-aware evidence.
03
OperationAgents perform narrow observable tasks; strategists approve synthesis, and source freshness and citation quality are monitored.
04 / Evaluation metrics
What must be measured before the system earns trust.
Evaluation covers model behavior, workflow burden, and production performance. The metric defines the gate; the customer baseline and acceptance threshold define the target.
01Evaluation gate
Evidence precision
How it is measured
Relevant, decision-useful sources among retrieved evidence.
What it decides
Controls research noise and source selection.
02Evaluation gate
Citation correctness
How it is measured
Claims accurately supported by the linked source passage.
What it decides
Determines whether generated briefs are reviewable.
03Evaluation gate
Research cycle time
How it is measured
Time to produce an evidence-complete first brief.
What it decides
Measures reduction in manual collection and formatting work.
04Evaluation gate
Strategist acceptance
How it is measured
Accepted, edited, or rejected findings with reasons.
What it decides
Shows where the system supports rather than distracts creative judgment.
05 / Customer perspective
Value has to appear in the customer’s operating day.
What matters in practice
The system should widen the strategist's view, preserve citations, and make disagreement or weak evidence easy to inspect.
06 / Technology context
Tools follow the system—not the other way around.
Final architecture depends on data quality, operating conditions, integrations, risk, and evaluation criteria established during discovery.
07 / Continue exploring
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