Addland: Synthetic Property Surveyor

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Business Context

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A leading proptech organisation operates across an enormous and highly complex property data ecosystem, spanning tens of millions of records across geospatial, polygon, planning and market datasets. Their commercial edge depends on rapidly identifying high-value development and investment opportunities hidden deep within this data.

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The Challenge

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The problem wasn’t data access, it was data synthesis at scale. Analysts were forced to manually interrogate multiple databases, cross-check planning constraints, and validate market assumptions. Even with expert teams, surfacing a single viable opportunity could take hours or days, limiting throughput and slowing decision-making.

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Our Process

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Passion Labs designed and deployed a synthetic property agent using our proprietary infrastructure. At its core, we implemented a highly specialised Model Context Protocol (MCP) to orchestrate tools across disparate datasets while remaining model-agnostic and future-proof. We layered this with a poly-agentic reasoning architecture, enabling the system to decompose complex property questions into structured steps: analysis, reasoning, cross-validation, and evaluation.

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The Solution

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The resulting system can holistically query and reason across multiple property datasets simultaneously, autonomously validating assumptions before generating outputs. Complex queries that once required senior analysts are now handled end-to-end by the synthetic agent.

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Impact & ROI

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• 90% reduction in time to surface viable opportunities

• Analyst workflows accelerated from days to seconds

• 3× increase in opportunities reviewed per week

• Significantly higher confidence in investment and development decisions

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This solution unlocked true needle-in-a-haystack discovery, delivering faster insights, better decisions, and measurable commercial advantage.

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