Paradigm Bridge · Applied Mathematics for AI

Know which
expensive work
to skip.

Paradigm Bridge builds calibrated probability signals that rank expensive operations before you pay for them — validated against real outcomes, then shipped as products we operate. Live today: Hunter, off-market seller intelligence for real-estate teams in Dallas–Fort Worth.

See the products Contact the lab

One method. Two markets.

Every product here runs the same play: score the probability of an outcome, then commit money or compute only to the work that scores well. Hunter ranks the owners in a farm by how likely each is to sell. Caustic ranks the regions of a corpus a query could live in, and searches only the ones that matter.

The discipline is the same in both: every number is measured against ground truth — realized sales, exhaustive search — not asserted. And where we operate the product ourselves, outcomes come back as labels the next model trains on. The moat isn’t the data. It’s the outcome history.

Products
Live · Dallas–Fort Worth

Hunter

Off-market seller intelligence for real estate teams

Every agent wants the listing before it hits the market. The signals are sitting in public data — ownership churn, distress, entity structure — but the raw record doesn’t tell you who is likely to sell, or who is really behind the LLC. Hunter does both: it scores every owner in a farm for sell probability, names the real person behind LLC-owned property, and delivers a ranked, contactable list.

Built on 5.2M ownership records and 327K transfers across six years of ownership history — propensity signals measured against realized sales, not guessed. Sold by the ZIP — self-serve shared plans, or an exclusive per-ZIP lock; every customer’s outcomes feed the next model.
hunter.living Talk to us
RankEvery owner scored for sell probability on signals validated against six years of realized transfers.
CrackNames the real person behind LLC-owned property — the individual, their role, and a path to reach them.
ReachVerified contact channels, tiered by source and do-not-call-flagged — leads arrive callable.
LearnOutcome tracking is built in — every lead’s result feeds the next model, so the moat compounds per territory.
Design-partner pilots

Caustic

Geometric query routing for RAG

Standard RAG scores every query against the entire corpus, every time — you pay to search documents the query will never use. Caustic learns which region of a corpus a query belongs to and searches only that slice. It sits in front of your existing vector store: no re-index, no new model.

Measured end-to-end on a real corpus: ~75% of vector-search cost removed at 95% recall, ~81% at 90% — ~4 points under the oracle ceiling, ~6 points over a fixed-budget baseline. PRM800K, 12,500 problems, BGE-large embeddings, recall scored against exhaustive search. Measured, not modeled.
See Caustic Become a design partner
DiagnoseReads your corpus geometry and tells you whether it routes at all — before you commit.
RouteEach query is classified to its region and searched there; uncertain queries hit their top few regions, never a full fallback.
AdaptiveConfident queries search less. Every savings figure is paired with the recall it holds — you pick the operating point.
FitsA wrapper in front of your vector store. No re-embedding, no new model, no rewrite.