by doltics
1 (0 reviews)
Doltics Radar
Protects selected WordPress email, comment, and registration flows with a spam protection API.
Compatible with WP 7
v1.0.0
Current Version v1.0.0
Updated 1 month ago
Last Update on 03 Jun, 2026
Refreshed 21 hours ago
Last Refreshed on
Rank
#57,774
—
No change
Active Installs
1+
—
No change
KW Avg Position
N/A
—
No change
Downloads
94
+1 today
Support Resolved
0%
—
No change
Rating
20%
Review 1 out of 5
1
(0 reviews)
Next Milestone 10
0+
10+
56,810
Ranks to Climb
-
Growth Needed
8,000,000
Active Installs
Pro
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Rank Changes
Current
#57,774
Change
Best
#
Downloads Growth
Downloads
Growth
Peak
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1.0
0 reviews
Overall
20%
5
0
(0%)
4
0
(0%)
3
0
(0%)
2
0
(0%)
1
0
(0%)
Tracked Keywords
Showing 0 of 0| Keyword | Position | Change | Type | Updated |
|---|---|---|---|---|
| No keyword data available yet. | ||||
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- Version
- 1.0.0
- Last Updated
- Jun 03, 2026
- Requires WP
- 5.7+
- Tested Up To
- 7
- PHP Version
- 7.4 or higher
- Author
- doltics
Support & Rating
- Rating
- ★ ☆ ☆ ☆ ☆ 1
- Reviews
- 0
- Support Threads
- 0
- Resolved
- 0%
Keywords
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Frequently Asked Questions
Common questions about Doltics Radar
Traditional filters rely heavily on static keyword blocking, which spammers easily bypass. Our engine uses a dynamic weighted scoring system. It extracts text, analyzes standard language patterns, filters anomalies, and fact-checks content against localized linguistic data. Instead of a simple "yes or no" flag, it combines multiple heuristics to give you a highly accurate, nuanced probability score.
The engine evaluates incoming text through multiple independent lenses (heuristics), such as syntax anomalies, sender reputation, and localized language patterns.
Spam looks different in every language and region. Slang, common idioms, and phrasing vary wildly. Our engine doesn't just translate text; it analyzes it against the specific rules and nuances of the local language context. This drastically reduces false positives caused by regional dialects or informal business language.
The engine cross-references extracted text and claims against verified localized datasets to detect known phishing templates, fraudulent business pitches, and common scam setups. If a piece of text closely mimics a known malicious campaign or contains verifiable misinformation (like a fake lottery claim), its spam score increases accordingly.
Not at all. The text extraction and processing happen in real-time within a secure, encrypted pipeline. We isolate the standard linguistic patterns and words required for scoring, and we do not store the PII (Personally Identifiable Information) or the contents of the processed text post-analysis.
Because our engine extracts normal words and filters them against localized language models, it excels at spotting "leetspeak" (e.g., replacing 'E' with '3') or intentional misspellings meant to trick basic filters. The system recognizes the underlying intent and penalizes the text for obfuscation tactics.