Production AI, engineered end to end, six eval-gated service lines.
The same playbook, tuned to the constraints of the sectors we ship into most.
Proof, not promises, selected case studies and recognition.
A transparent, 3-phase playbook from first audit to embedded team.
The senior team behind the work, and how to reach us.
Property decisions ride on data that is messy, unstructured, and high-stakes. We build valuation models, lease intelligence, and conversational property search, with the accuracy and citations that let buyers, brokers, and underwriters trust an AI answer enough to act on it.
From AVM systems to lease intelligence every engagement is scoped around your data sources, your use case, and the decisions your team needs to make faster.
AVM systems using comparable sales, location features, and market conditions. Explainable valuations with confidence intervals.
RAG over leases, PSAs, and title documents. Extract key dates, clauses, obligations, and risks instantly.
Semantic search and natural language query over property listings. "3-bed near good schools with a south-facing garden" understood.
LLM pipelines that generate market reports, investment summaries, and neighbourhood analyses from structured data.
Propensity models that score buyer and seller leads by likelihood to transact. Route to the right agent at the right time.
Predictive maintenance for commercial portfolios. Work order prioritisation and vendor routing from IoT and inspection data.
AVMs need to justify their outputs to buyers, lenders, and regulators. We build valuation models that show their working comparables, adjustments, confidence bounds.
MLS feeds, county records, satellite imagery, IoT sensors real estate data lives in formats that don't talk to each other. We build normalisation pipelines that make it usable.
Commercial leases run to hundreds of pages with critical terms buried in schedules and addenda. RAG over raw PDFs misses too much we build structured extraction pipelines.
Interest rate moves and inventory shifts change pricing dynamics quickly. Models need retraining triggers and drift monitoring, not quarterly manual reviews.
In liquid markets with good comparable data, well-built AVMs achieve median errors of 3–6% on residential properties. Accuracy drops in thin markets and for unique properties we quantify this uncertainty explicitly rather than hiding it.
Yes. We embed your listings using property-specific feature extraction, then build a semantic search layer that understands natural language queries. Typical build time is 4–6 weeks including evaluation.
We build structured extraction pipelines that combine layout-aware PDF parsing with LLM-based entity extraction. Key dates, rent escalations, break clauses, and obligations are extracted into structured records not just surfaced as raw text.
MLS APIs, CoStar, Zillow, county assessor records, ATTOM, and custom data warehouse exports. We can work with whatever combination of sources your platform uses.
30 minutes, one of our seniors, no slide deck. By the end of the call you'll know whether we're the right team, and if not, who is.