What counts as the signal?
Examples include a buyer publicly requesting a service, a competitor changing a price, a new grant matching a sector, or a monitored page entering a meaningful state.
HIRO RADAR METHODOLOGY
How a repeated search becomes a controlled monitoring system.
Hiro Radar is not defined by a single crawler, model or keyword list. The service begins with a decision rule: what signal matters, where it may appear, what must be rejected, how fresh it must be and what evidence should accompany it. The implementation can then use deterministic rules, search, monitoring automation and AI-assisted classification where each is useful.
METHODEVIDENCE FIRST
01 · SCOPE
Start with acceptance and rejection criteria.
A useful monitoring system needs a boundary before it needs automation. A Radar brief records the target signal, source families, output type, cadence, delivery channel and important filters. Exclusions are first-class inputs, not an afterthought.
What should never pass?
Stale results, duplicates, job listings when the user wants client work, weak keyword coincidences and sources outside the approved scope can be rejected before delivery.
How current must it be?
Freshness is part of the decision rule. An old result can be factually correct and still be useless as an opportunity signal.
What evidence is required?
A result should retain enough source context for a person or downstream workflow to understand why it was surfaced and verify the underlying claim.
02 · SOURCE ACCESS
Use appropriate access paths and treat source constraints as part of the product.
Standard Radar scopes focus on approved public sources. Paid or authenticated sources, custom integrations, uncertain platform terms, sensitive domains or unusually high volume are not silently treated as ordinary monitoring.
Normal automated pricing assumes public-source monitoring that does not require bypassing access controls.
Hiro Radar does not treat technical circumvention as a monitoring feature.
Source feasibility includes technical access, rate limits, platform restrictions and whether the requested monitoring method is appropriate.
Non-standard source access is reviewed instead of being hidden inside an automatic scope.
03 · SIGNAL PIPELINE
Separate detection from qualification.
Finding a candidate is not the same as deciding that it is useful. The Radar loop deliberately separates raw detection from the filters that make a result operationally valuable.
01CollectPotential matches
02NormalizeComparable structure
03DeduplicateRepeated signals
04QualifyRules & context
05EvidenceSource support
06DeliverActionable output
Use exact rules where exact rules are better.
Dates, explicit exclusions, source identity, required fields, duplicate keys, thresholds and commercial limits should not be delegated to a language model when deterministic logic can express them clearly.
Use models where language and context matter.
AI can assist with relevance classification, semantic matching, summarization, evidence organization and qualification when the signal cannot be reduced to a single literal keyword. Model output remains bounded by the service scope and does not create new commercial permissions.
FALSE POSITIVES
A good Radar is measured by useful matches, not by how many items it can collect.
False-positive control is a product requirement. For example, a filter saying “exclude hiring posts” should not cause the system to classify the entire Radar as an employment-decision system simply because the word “hiring” appears. Scope interpretation must distinguish the requested target from rejection language.
Separate target from exclusions
Risk and intent classification should use the monitored target first, while exclusion text is used primarily to reject matches.
Preserve reason codes
When a request is routed for review or a result is rejected, the system should retain a structured reason rather than a vague model judgment.
Version policies
Pricing and qualification policies are versioned so an old decision can be interpreted against the rules that actually produced it.
Do not hide uncertainty
If source rights, relevance or technical access are uncertain, the correct result can be review or non-delivery rather than fabricated confidence.
DELIVERY
Output should carry enough context to support a next decision.
A useful Radar result is not merely a URL. Depending on scope, delivery can include the source, what changed or matched, why it passed the criteria, relevant extracted facts, confidence or qualification context and the timestamp/freshness needed to act.
For a lead
Evidence should explain the visible need, where it appeared, why it matches the requested service and whether mandatory filters passed.
For a competitor change
Evidence should identify the source surface, the material change and the context needed to distinguish a strategic update from routine page churn.
PUBLIC METHODOLOGY · VERSION 1
Define my RadarMethodology is part of trust.
This page describes the public operating principles of Hiro Radar as of 16 September 2026. Implementation details can evolve, but the core constraints—explicit scope, appropriate sources, false-positive control, evidence and no hidden sensitive actions—remain part of the service definition.