How does EmailAddress.ai achieve 98.2% catch-all accuracy?

We combine a 6-stage verification engine with 40+ behavioral signals trained on 500M+ verifications and a weekly-retrained gradient-boosted ensemble model.

What signals does the verification engine use?

Signals include SMTP latency profiling, banner fingerprinting, MTA classification, greylisting bypass behavior, role account detection, and historical domain patterns.

What does the confidence score mean?

Each verification returns a calibrated 0-100 confidence score with a deliverable / undeliverable / risky classification, derived from the full signal stack.

How is the model trained?

It is trained on more than 500 million verifications with continuous feedback from enterprise send-outcome data (with consent).

Accuracy Methodology

How We Achieve 98.2% Accuracy

A technical deep-dive into our multi-signal verification engine and continuous learning pipeline.

Trusted by High-Volume Senders

40+

Behavioral Signals

500M+

Training Verifications

98.2%

Catch-All Accuracy

Weekly

Model Retraining

Stage 1: Syntax & Format Validation

RFC 5322 compliant syntax checking, unicode normalization, MX record existence, and role account classification. This stage filters ~15% of addresses before SMTP analysis begins.

Stage 2: Domain Classification

We classify domains into: standard SMTP, catch-all/accept-all, greylisted, protected (Office 365, Google Workspace, Mimecast), and disposable. Each class gets a different verification protocol.

Stage 3: SMTP Behavioral Profiling

For catch-all domains, we initiate multi-session SMTP probes to map latency distributions, banner characteristics, error code behaviors, and session limit responses. 40+ signals extracted per probe sequence.

Stage 4: Historical Signal Fusion

Each domain's historical behavioral profile is cross-referenced against our 500M+ verification database. Domains with consistent patterns get confidence scores from historical outcomes, not just current probe results.

Stage 5: ML Ensemble Decision

A gradient-boosted ensemble model combines all signals with domain-specific calibration. Outputs a calibrated confidence score (0–100) with deliverable/undeliverable/risky classification.

Stage 6: Continuous Feedback Loop

Send outcome data from enterprise customers (with consent) feeds back into model retraining. Models are retrained weekly, with domain-level profiles updated in real-time as infrastructure changes are detected.

Data Methodology

Our B2B and HCP data undergoes the same rigorous validation process before any record enters our database.

Source Authentication

Every licensed data contributor is verified for legitimacy, accuracy standards, and compliance posture before partnership is established.

Format Normalization

Records are cleaned, deduplicated, and normalized to our unified schema before enrichment.

Deliverability Verification

Every email address in our database is run through our catch-all resolution engine to confirm deliverability before inclusion.

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