Sydney Business Web (SBW) is an engineering-led web development and AI Visibility business based in Thornton, New South Wales, Australia.
We design and engineer websites, structured-data systems, entity architectures, APIs and technical infrastructure that help search and AI systems retrieve business information and identify businesses accurately.
Official website: https://sydneybusinessweb.com.au/
We distinguish the different technical questions involved in AI Visibility:
- Retrieval: Are recognised AI and search systems requesting useful website content, and are those requests succeeding?
- Entity structure: Are the business, website, people, services, locations and identifiers consistently connected?
- Identity assessment: Are important relationships clear, and which claims or connections need stronger evidence or correction?
- Page-level checks: Can a single page expose a useful connected machine-readable identity?
- Downstream outcomes: Is the business actually represented, cited or recommended in AI-generated answers? Those outcomes require separate evidence.
The following systems address distinct parts of this work. They are not interchangeable products.
AI Observatory is a managed, server-side retrieval-monitoring system for qualified AI and search crawler activity.
Its engineering includes network-edge or server-log collection, crawler-identity corroboration, request and response classification, filtering and reporting. The resulting evidence helps establish which recognised machines requested business information and whether qualifying retrievals succeeded.
Measurement boundary: Retrieval evidence does not, by itself, prove AI understanding, citation, recommendation or endorsement.
- Product information
- Public technical documentation
- Zenodo record
- Earlier archived technical release — v1.0.0
Schema Gorilla examines a website's connected, machine-readable entity and structured-data representation across the whole site rather than validating each page in isolation.
Its analysis considers entity identifiers and relationships, graph coherence, competing or fragmented representations, structured-data consistency and the links between business, people, services and other entities.
Engineering boundary: A coherent graph is valuable structural evidence, but valid schema alone cannot guarantee AI retrieval or recommendation.
AI Identity Diagnostic interprets structured evidence to assess how clearly a website identifies its business and related entities. It examines unresolved or competing identities, missing relationships, and where corroborating information or remediation is needed.
It complements Schema Gorilla's whole-site analysis. The diagnostic does not, by itself, prove inclusion in AI answers or recommendations.
AI Baby Gorilla is a free, single-page structured-data connectivity checker. It gives users an accessible way to inspect whether key entities and relationships on a public web page are connected, partially connected or fragmented.
The public tool is deliberately limited to one page; it is not a whole-site assessment. Its access-controlled API can also support authorised, automated agent workflows.
Observe retrieval → analyse whole-site structure → assess business identity → remediate and verify.
AI Baby Gorilla provides a separate, limited one-page entry point. None of the systems claims that technical readiness alone guarantees citation, recommendation or other downstream outcomes from third-party AI services.
Technical architecture and development are led by Keith Rowley, Founder & Lead Engineer, Sydney Business Web.
Public GitHub repositories document selected architecture, measurement boundaries, methodology and technical references. Production source code, proprietary qualification logic, graph-processing algorithms and commercial implementation remain private unless explicitly released.
Zenodo records provide persistent archival references for the technical materials listed above. The presence of a public documentation repository does not imply that the corresponding production system is open source.
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