Mixed-use developments carry layered, technical information — zoning overlays, live-work unit ratios, density bonuses, ground-floor commercial allocations, transit proximity — that generic web copy rarely conveys in a form language models can parse. When a planner or prospective resident asks an AI assistant about a project's live-work component, the model pulls from whatever structured signals exist online; without them, it guesses or omits your development entirely. AI HALO builds schema.org markup for residential and commercial unit types, drafts an llms.txt briefing that states your subdivision's zoning classification, density figures and amenity programming in plain declarative language, and establishes a knowledge-graph entity linking your development to the municipality, planning file numbers and adjacent infrastructure. It also unblocks AI crawlers that permissive-but-misconfigured robots.txt files often silently exclude, then re-scans at 30 days to confirm the models now answer planner and buyer questions correctly.
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We map your development's actual zoning bylaw language into schema.org LandUse and Place properties, then mirror the same terms in the llms.txt briefing so models cite your municipality's specific classification rather than a generic 'mixed-use' label that could confuse planners.
Yes. The knowledge-graph entity and llms.txt briefing can be published for a project in the entitlement stage, giving AI assistants accurate proposed-use, density and unit-mix data to reference when planners or council researchers ask about it ahead of approval.
The initial engagement is one-time, covering the current phase's data. As new phases release, the structured data and briefing can be refreshed to reflect updated unit counts, commercial allocations and completion timelines.
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