We built and trained our own model rather than licensing a generic rules engine. It reads your report the way a credit analyst would — then shows exactly how it reached each conclusion.
The model parses credit reports across bureaus and formats — PDF exports, scans, or connected pulls — extracting personal information, tradelines, and inquiries with a per-field confidence score.
It's trained on thousands of resolved dispute cases, learning which patterns — obsolete debts, duplicate collections, reporting errors — are actually disputable versus cosmetic.
Every finding comes with what happened, why it matters, the recommended action, and a confidence percentage — so you can decide for yourself, not just trust the model.
The model sequences disputes into rounds, adapting each follow-up to how a bureau actually responded — including Method of Verification requests — instead of a one-shot template blast.
Outcomes from every resolved dispute feed back into the model across both the US and Indian credit systems, sharpening its accuracy over time.
We never guarantee results. The model estimates a likely point range based on industry patterns — actual outcomes depend on your file and how bureaus respond.