Under-promised.Over-delivered.

Life-sciences real estate does not underwrite like office or multifamily. Lab buildout costs, specialized infrastructure, and tenant credit drive value in ways generic CRE models miss, so the team was rebuilding assumptions by hand on every deal and screening fewer opportunities than the pipeline demanded.

We started inside their own model, reverse-engineering how they price lab and R&D space: buildout costs per square foot, tenant credit, and the specialized infrastructure generic tools ignore. We tuned the engine on real deals until its outputs matched the team's own work line for line, so nothing reached an investment decision until they trusted it.

Configured to life-sciences fundamentals, tenant credit, lab buildout costs, and the specialized infrastructure that generic CRE models miss. Drop in a deal and get back a pro forma built on the right assumptions for the asset class, with every output traceable to a source document.

A family-owned investor underwriting across office, multifamily, hospitality, retail, and mixed-use. Five asset classes meant five sets of return thresholds, comp logic, and pro forma assumptions, all carried in people's heads and rebuilt deal by deal.

We mapped how Westlake underwrites each asset class and taught the engine to switch contexts: return thresholds, comp sets, and pro forma logic adapt per asset class without losing the audit trail. We tuned it against the team's own deals until the numbers matched across every property type.

One engine that underwrites all five asset classes the way Westlake already does. Return thresholds, comp sets, and pro forma logic adapt per asset class, every output traces to source, and the team works inside their own models instead of a generic tool's.

An operator scaling acquisitions across industrial outdoor storage and a national self-storage portfolio. Two asset classes, two sets of deal math, and a pipeline moving faster than the team could screen by hand.

We deployed the CRE AI Analyst engine configured for both asset classes: OM parsing, comp pulls, and IC-ready models tuned to IOS and self-storage fundamentals. One workflow, validated against the operator's real deals before anything reached the investment committee.

The same underwriting engine deployed across the operator's industrial outdoor storage acquisitions and national self-storage portfolio. Drop in an OM, get back IC-ready models with comps pulled and every number source-traced. Two asset classes, one workflow.