Company, product, and market entities are named inconsistently
Enterprise / Delivered project
Competitor Analysis AI System
An evidence-led research system for monitoring approved public sources, resolving companies and products, and surfacing meaningful changes.
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StatusDelivered project
IndustryEnterprise
SystemData → Intelligence → Action
01 / Problem statementDelivered project · Enterprise
The operating problem
Strategy teams repeatedly collect fragmented competitor information and struggle to separate important changes from duplicated or low-quality signals.
Conditions the system must survive03 operating constraints
Public sources duplicate, contradict, and revise information
Automated summaries can overstate weak or stale evidence
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
Define competitors, themes, sources, and collection policy
Evidence produced
Competitor, theme, source, and collection policy
02Evaluate
Resolve entities across inconsistent public information
Evidence produced
Entity-resolution benchmark across public source variation
03Engineer
Detect changes and organize supporting evidence
Evidence produced
Change-detection and evidence-quality review
04Operationalize
Generate analyst briefs with citations and confidence
Evidence produced
Analyst brief workflow with citations and disposition feedback
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 research pipeline with scheduled source monitoring, entity graph, change detection, and analyst review.
02
IntegrationApproved public sources, internal notes, CRM context, and knowledge stores contribute permission-scoped evidence.
03
OperationMaterial changes generate evidence bundles; analysts confirm significance and source freshness drives recollection.
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
Entity-resolution precision
How it is measured
Correct company, product, and executive matches across sources.
What it decides
Controls contamination of competitor records.
02Evaluation gate
Change precision
How it is measured
Analyst-confirmed meaningful changes among surfaced events.
What it decides
Sets monitoring thresholds and source priority.
03Evaluation gate
Citation coverage
How it is measured
Brief claims linked to current supporting evidence.
What it decides
Determines whether a brief is reviewable.
04Evaluation gate
Analyst review time
How it is measured
Time from surfaced change to approved intelligence brief.
What it decides
Measures reduction in repetitive research work.
05 / Customer perspective
Value has to appear in the customer’s operating day.
What matters in practice
A brief is useful when each change is linked to current evidence, entity resolution, confidence, and the collection policy.
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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