Trust center
A billion-dollar buyer product is built on proof, not AI theatre.
Twellie separates deterministic valuation logic from AI explanation, publishes confidence ranges, discloses data provenance, and refuses headline accuracy claims until closed-sale backtests are large enough to defend.
Core logic
Python first
Price verdicts, offer ladders, repair credits, and appraisal-gap risk are deterministic. AI explains the math.
Accuracy policy
No fake percentage
We do not publish a headline accuracy score until a recent closed-sale benchmark clears the sample-size gate.
Report proof
Every number has a source
Live, hybrid, demo, public record, photo-AI, and inferred values are labeled in the report and methodology.
User promise
Buyer-safe decisions
The product gives opening offer, fair offer, walk-away cap, repair credit, and appraisal contingency guidance.
Billion-dollar execution roadmap
The operating plan
01
Data moat
Unify live listings, closed comps, public records, tax, permits, hazards, photos, insurance signals, and buyer context into one normalized property graph.
02
Calibration engine
Backtest predicted value, fair offer, and walk-away price against recent closed sales by market, home type, price band, and data quality.
03
Decision intelligence
Move beyond AVM output into buyer actions: what to offer, what credit to ask for, when to walk, and what contingency protects the buyer.
04
Proof trail
Every report stores logic version, model IDs, data-source status, comp weights, adjustment reasons, and generated-output timestamps.
05
Workflow capture
Attach inspection response, offer letter, seller net sheet, wire-fraud check, closing checklist, and document binder to the same report.
06
Distribution
Win trust through public benchmarks, sample reports, SEO buyer education, partner lenders, inspectors, attorneys, and agent-free buyer communities.
Accuracy transparency
What we will publish before claiming accuracy
Benchmark sample
Recent closed sales only; no stale deed prices or active listings.
Coverage split
City, ZIP, property type, price band, comp density, and data-source mode.
Metrics
Median absolute percentage error, 80th percentile error, bias, and confidence-band capture.
Failure cases
Rural homes, unusual architecture, thin comps, distressed sales, major unseen renovation.
Trust gates
What must be true before scale
500+ recent closed-sale rows in each launch market before local accuracy copy goes live.
Every report exposes live, hybrid, or demo data status in the header.
Every comp adjustment is visible, weighted, and explainable.
Every buyer-safe offer includes repair, uncertainty, budget, and appraisal-gap buffers.
Every AI-written explanation is grounded in stored Python outputs, not free-form model opinion.
Plain-English promise
The report should make a buyer harder to mislead.
That means fewer unsupported list prices, clearer appraisal-gap risk, visible comp evidence, realistic repair credits, and a walk-away number the buyer can defend.