Real Estate Technology
Built for Our Proptech Company
Our proptech company needed to move faster. The investment managers were building DCF models and running scenario analyses manually in Excel, pulling data from scattered market reports and government statistics. The work was expert-grade but painfully slow, and every new deal meant starting from scratch. There were no AI agents. We built them from the ground up, with real domain knowledge baked in.
The Gap
Expert analysis trapped in spreadsheets that couldn't scale
Investment managers were spending days building individual DCF models in Excel, manually pulling data from Destatis, CBRE, and other market reports, then stitching it all together by hand. Every new deal required rebuilding from scratch. The domain expertise existed in people's heads, but it wasn't encoded anywhere a system could use it. As deal volume grew, the bottleneck wasn't intelligence. It was throughput.
What We Built
A Value Layer encoding real estate investment expertise into AI agents built from scratch. Long-running agents that work on the same analysis across multiple sessions, pulling live data from trusted sources and refining outputs as new information becomes available.
Expert knowledge as workflow logic
How experienced investment managers actually build DCF models: which assumptions to stress-test, how to weight different data sources, what scenarios matter for a given asset type. Domain expertise that no general-purpose AI would ever surface on its own.
Long-running multi-session agents
Agents that work on the same file across multiple sessions, progressively refining the analysis as more information comes in. Not a one-shot prompt, but a persistent workflow that mirrors how analysts actually work: iteratively, over days, shaping the model as the picture gets clearer.
Live external data integration
Direct connections to Destatis for government statistics, CBRE for market reports, and other trusted data sources. The agents pull grounded, live datasets instead of relying on stale training data or manual copy-paste from PDFs.
Scenario generation at scale
What used to be a single scenario painstakingly built in Excel becomes dozens of scenarios generated, compared, and stress-tested in minutes. Same rigor, dramatically higher throughput.
Layer 3
Applications
AI agents for DCF modeling, scenario generation, market analysis
Layer 2
The Value Layer
Context
Real estate valuation expertise, deal structuring logic, market report interpretation
Prompting
DCF modeling frameworks, scenario generation logic, data synthesis rules
Orchestration
Long-running multi-session agents, external data integration (Destatis, CBRE)
Layer 1
Systems of Record
Excel models, Destatis, CBRE market reports, deal pipeline data
The Result
| Metric | Before | With Value Layer |
|---|---|---|
| DCF model creation | Days per model in Excel | Minutes per model, AI-assisted |
| Data sourcing | Manual pulls from Destatis, CBRE, PDFs | Live integration, auto-updated |
| Scenario analysis | 1-2 scenarios per deal (time-constrained) | Dozens of scenarios, stress-tested automatically |
| Knowledge persistence | Starts from scratch every deal | Agents carry forward learnings across sessions |
| Scaling with deal volume | Linear: more deals = more analyst hours | Parallel: agents handle volume without proportional time cost |
What This Proves
- Expert knowledge can be encoded into AI workflows, not just prompts. The result is agents that think like experienced analysts, not generic chatbots.
- Long-running, multi-session agents mirror how real work happens: iteratively, over time, refining as new data comes in.
- Live data integration turns AI from a reasoning engine into a grounded analysis tool. The difference between interesting and trustworthy.
See what a Value Layer looks like for your business.
Book a Free Value Layer Audit