Stop begging the bureaus.
Force them to comply.

Credit Dawg uses proprietary AI to parse raw Metro 2 data, mathematically proving data furnisher errors and generating dynamic, OCR-beating disputes that the credit bureaus can't legally ignore.

Fix My Credit View B2B API

Why standard credit repair fails.

Standard factual disputes ("not my account") are auto-rejected by the e-OSCAR system. We use "Data Integrity" attacks based on strict CDIA Metro 2 formatting rules.

Algorithmic Audits

Our Python engine parses your raw credit JSON, mathematically proving contradictions (e.g., Status 97 with an Amount Past Due). If the math fails, the tradeline must be deleted.

Anti-OCR Dynamic Letters

We don't use PDF templates. Our LLM dynamically writes every dispute letter from scratch, varying syntax and tone so e-OSCAR's algorithms cannot flag it as a "frivolous" template.

Consumer Education

We decode FCRA regulations and CDIA standards into plain-English. Understand the exact laws we use to protect you, and learn how to maintain flawless credit long-term.

Free educational handbooks

FCRA-grounded guides — no guaranteed outcomes, no legal advice. Download HTML or PDF.

Consumer's Guide to Credit Bureau Deletions

FCRA § 611 disputes, Metro 2 accuracy, and e-OSCAR mechanics for consumers.

Read handbook →

CRO Blueprint: Defeating e-OSCAR OCR

B2B field-targeting matrix, dynamic letters, and § 623 furnisher obligations.

Read handbook →
Enterprise API

Power your Credit Repair Business with our AI.

Are your templates getting flagged as "frivolous" by e-OSCAR? Are you paying humans to manually parse reports? Integrate the Credit Dawg Metro 2 REST API. You send us raw JSON, we return mathematically proven, Anti-OCR dynamic dispute letters.

View API Documentation
// POST /api/v1/metro2/audit
{
"bureau": "Equifax",
"status_code": "97",
"amount_past_due": 500
}
// RESPONSE: 200 OK
"fatal_violation": true,
"generated_letter": "I am disputing..."

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