Data Orchestration for AI Agents
Published on
Overview
Data Orchestration in AI-powered PropTech applications is the process of combining multiple Smart Indexes data sources into a coherent, queryable, and explainable information flow. This enables AI agents to seamlessly blend property identifiers, pricing analytics, and geo-risk layers into actionable insights for investors, developers, and brokers.
Consultations are available upon request for all users interested in API integration (Google Meet / Zoom).
Core Data Sources
- Makani Search API — resolves precise property location and attributes.
- Property Price Indexes API — provides yield metrics such as Cap Rates and median prices (deals: rent, sales).
- Developers API — delivers environmental and infrastructure risk layers (Flood Risk, Average Crime Rate, Traffic Accident Rate).
- Brokers API — supplies registered broker and agency data for market context and outreach.
Orchestration Workflow
Identify the scope — AI agent determines geographic and asset type constraints from the user query.
Resolve location — use Makani Search API to get coordinates and baseline property attributes.
Enrich with pricing — fetch relevant Cap Rates and median sale/rent prices via Property Price Indexes API.
Add context — overlay risk layers from Developers API to quantify environmental and safety exposure.
Integrate market actors — optionally call Brokers API to include verified agent/agency data.
Normalize and score — convert all metrics to common scales, apply weights, compute composite score.
Return explainable results — provide ranked shortlist with factor-by-factor contribution breakdown.
Why Orchestration Matters
- Holistic view — combines asset-level, market-level, and environmental data.
- Operational efficiency — automates multi-source retrieval and joins in seconds.
- Data consistency — Makani ensures all sources align on the exact same property reference.
- AI readiness — structured output ready for ingestion by conversational agents, dashboards, or ML models.
Example Orchestration for a Query
User query: «Find residential buildings in Dubai Marina with Cap Rate above 8%, low flood risk, and at least 2 nearby metro stations».
- Makani Search API → building coordinates & profile.
- Property Price Indexes API → filter by Cap Rate ≥ 8%.
- Developers API → filter by Flood Risk threshold, count transport points.
- Return ranked shortlist with explanations.
Implementation Tips
- Use asynchronous calls to speed up API aggregation.
- Cache stable attributes (Makani details) and refresh dynamic metrics (Cap Rates, associated risks) periodically.
- Log query parameters and weights to support explainability and reproducibility.