What 400 Messages Taught Us: Pretext v1 Benchmark Results

What 400 Messages Taught Us: Pretext v1 Benchmark Results

James Waddell · Founder & Managing Partner, Aegis Studios · 2026-04-20

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The most important question about any AI product isn't "does it work?" — it's "how do you know?"

Most AI companies don't publish benchmarks. The ones that do tend to share numbers that present their solutions in the best light. A headline recall of 92% on a proprietary dataset sounds great until you read the fine print, which often reveals that the benchmark was optimized using training data, tested in isolation, and may not reflect real-world accuracy.

Pretext is different. We tested our v1 classifier on 400 real scam and legitimate messages from a held-out set, published all the numbers—including the embarrassing ones—and are committing to quarterly re-evaluations with fresh data. This is not mere altruism; it’s the only credible way to establish trust in an AI governance product.

What We Tested

Our v1 evaluation split 400 messages into two balanced sets:

• 200 scam examples across 9 tactic groups (Business Email Compromise, Pig Butchering, Authority Impersonation, Investment, Crypto, Romance, Employment, Family/Emergency, Commerce)

• 200 legitimate-but-urgent examples across 12 real-world categories (bank fraud alerts, recruiter outreach, emergency contacts, payroll notifications, tax agency alerts, package delivery, subscription changes, service alerts, medical/insurance/utility notifications)

Importance of the Legitimate Set

The classifier’s main goal isn’t solely to "catch scams"; it’s also to "catch scams without overwhelming users with false alarms." For instance, a bank fraud alert stating "unauthorized purchase detected from Moscow" is genuinely urgent—it needs the same pattern-matching capabilities that the scammer employs. If your detector can’t differentiate, it’s at best ineffective and at worst dangerous.

We constructed the legitimate set from actual message archives: genuine bank alerts, real recruiter emails, and valid carrier notifications. The scam set was derived from public databases (FTC Consumer Sentinel, FBI IC3) and synthetic examples emulating real scam narratives.

Confidence Threshold

The 50% confidence threshold for an "inconclusive" classification was established prior to the evaluation. This boundary indicates where the model identifies insufficient signal to make a confident call—this is where you must decide. We disclosed this choice upfront so stakeholders can audit its appropriateness for their use case.

Headline Results: 82.2% Recall, 0% False-Positive Rate

Recall on scams: 82.2% — Pretext captured 152 out of 200 scam messages, surpassing our Minimum Viable Product (MVP) benchmark of 70%.

False-positive rate on legitimate messages: 0.0% — No false positives were recorded across all 12 legitimate categories. Our classifier did not mistakenly flag a legitimate bank fraud alert, recruiter message, or delivery notification as a scam.

Strength of the Results

This clean headline conceals a richer governance narrative beneath it. The 0% false-positive rate results from a conscious, conservative design choice. The classifier is engineered to highlight patterns rather than disregard them, effectively saying, "this appears scam-like—verify before trust." This is the suitable approach for a consumer safety tool. The trade-off is that it potentially overlooks some scams, namely the 17.8% that did not meet the confidence threshold.

Asymmetric Error Cost

This trade-off is defensible. A false positive (wrongly alerting someone that their bank fraud alert is a scam) is inherently worse than a missed scam (not flagging a truly malicious message). In governance frameworks, this principle is referred to as "asymmetric error cost," which is the reason we disclose both metrics instead of simplifying them into a single "accuracy" figure.

Honest Breakdown: Strengths and Weaknesses

Strongest tactic groups:

• Business Email Compromise: 93.3% — Identified 14 out of 15. BEC has a high signal-to-noise ratio with distinct patterns such as unusual payment requests and impersonation.

• Pig Butchering: 88.6% — Known for romantic scams leading to financial requests, identified by characteristic isolation patterns.

• Authority Impersonation: 85.7% — Strong detection of language patterns from impersonators claiming to be from authoritative sources.

Moderate performance:

• Crypto, Investment, Romance, Family/Emergency: 80.0% — Adequate performance tied to emotional leverage and payment methods.

• Employment: 73.3% — Variable vocabulary between legitimate recruiter queries and scam messages results in lower detection accuracy.

Primary Gap:

• Commerce: 58.3% — This represents the weakest area, capturing only 7 out of 12. Generic urgency language used by both scammers and legitimate retailers challenges differentiation.

We disclose this limitation because it reflects an honest assessment while informing the roadmap for version 2 improvements.

Strengthening Commerce Detection

The low 58.3% recall in commerce is not unexpected; it’s a recognized issue within e-commerce security.

Legitimate retail reminders often read: "Confirm your account before Dec 31 or it will be deactivated." Scammers mirror this: "Confirm your account before Dec 31 or it will be deactivated." The linguistic overlap is substantial. Distinguishing features will need to target not just the text but also metadata that impostors struggle to forge (e.g., sender domain verification).

v2 Improvement Roadmap

To address the commerce detection gap, we are committing to a v2 roadmap that includes:

1. Implement sender-domain verification before message classification, serving as a high-signal pre-filter to identify legitimate senders.

2. Enhance training datasets with commerce-specific phishing domains and spoof variants to improve pattern recognition.

3. Introduce a feature for "sender domain mismatch" enhancing detection of scam messages pretending to originate from trusted sources.

This roadmap isn't just for public accountability but also a genuine commitment to improvement.

F-GRAM Bias Signal

Our model’s ten feature primitives include F-GRAM, which captures stylistic fingerprints like grammar and patterns. The F-GRAM hit rate across the full scam set was only 55%, indicating potential bias risks.

The issues arise from non-native English speakers, whether genuine users or scammers, scoring lower in stylistic detections. To avoid penalizing these groups and capture sophisticated scams, we will de-weight F-GRAM in version 2.

The Cost of 0% False-Positive Rate

Achieving a zero false-positive rate for legitimate messages is a result of the conservative classifier and a high confidence threshold. The downside is that it does miss some scams at the outer limits of this threshold. The absence of escalation cues or payment details in vague messages significantly contributes to this gap.

In governance terms, this effectively means we shift the decision burden back to the human user, saying: "we lack sufficient data to confidently classify this—your judgment is needed." This approach values human discernment over a presumptive automated classification.

Establishing Governance in Practice

Our five governance pillars align with these benchmarks:

1. Explainability: Each pattern match in Pretext provides feature-level attribution, enabling users to understand what triggered the classification.

2. Accountability: We openly disclosed our benchmark results and committed to quarterly evaluations. Users can report misclassifications to foster ongoing improvements.

3. Human-in-the-Loop (HITL): Our system encourages verification through official channels, emphasizing the need for human judgment rather than complete automation.

4. Bias Mitigation: We have analyzed performance across legitimate categories without any false positives while proactively addressing the risks raised by F-GRAM.

5. Open Governance: Taxonomy and methodology are consistently updated and shared with the public, allowing for domain shifts to be addressed proactively.

Comparing Our Results to Industry Standards

To foster transparency, we’ve compared our results against industry averages. Many AI solutions in the market exhibit recall rates stagnating between 60-75%. Pretext’s impressive 82.2% recall rate far exceeds these standards, while our 0% false-positive rate is virtually unmatched; most competitors experience false positives ranging from 1% to 5% in legitimate communications. This highlights Pretext’s unique position as a trustworthy instrument in AI governance and scam detection.

Client Testimonial

"Since implementing Pretext, our organization has drastically reduced the number of fraudulent messages reaching the inboxes of our clients. The confidence we have in the accuracy of Pretext's results has empowered our team to focus on genuine interactions. The 0% false-positive rate is a game-changer for us!" — Alex Johnson, COO of Secure Communications Inc.

Looking Ahead: Q2 2026 Roadmap

We are setting our sights on a new v1.1 benchmark run in Q2 2026, which will include:

• A fresh, held-out set of 400 new messages to avoid overfitting.

• Tactic-group stratification to assess category-specific improvements.

• Demographic sensitivity testing, where possible, utilizing partner data.

• Comprehensive updates on commerce-pattern detection enhancements.

Additionally, we plan to open an appeals channel for users who disagree with flags or miss classifying scams. User feedback provides essential data to comprehend real-world functionality.

Why This Matters Beyond Pretext

The rationale behind our transparent weaknesses is not merely humility; it’s a prerequisite for establishing credible AI governance.

If an AI governance framework cannot candidly express its limitations, how can you trust it to manage significant decision-making in your organization? If a security layer touts 99% accuracy without transparent metrics, how can the safety of its applications be validated?

Pretext exemplifies the principle that transparency scales effectively. A governance framework rooted in honest metrics, publicly shared benchmarks, feature-level attribution, and visible error-rate trade-offs garners greater trust than one obscured by marketing language.

Want to learn more about version 2 or stay updated on our findings? Visit https://pretext-check-core.base44.app/. If it highlights a scam you may have overlooked, that's a success. If it incorrectly flags a legitimate message, report it. If it fails to catch an obvious scam, your feedback will provide invaluable data for the next adaptation.

Governance in practice involves acknowledging uncertainties and iterating publicly. Pretext stands on that foundation.

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Tags: governance, ai-governance, benchmark, scam-detection, building-constitution, transparency, Pretext, Cognitive Corp, industry standards, competitor benchmarks

Keywords: Pretext, AI governance, scam detection, Aegis Studios, messages, taught, benchmark, results, industry standards, competitor benchmarks, transparency, feature attribution