At a time when content production is on a large scale and dissemination speed is instantaneous, problems such as “omission of misspelled words, omission of sensitive information, and misuse of AI-generated content” not only affect the professionalism of the content, but also may trigger legal risks and brand crises. As a multimodal AI content auditing and proofreading platform under Botech Intelligence, **Error Digger** takes “25-72B large model as the base + multiple small model synergy” as the technical kernel to realize the error detection and compliance verification of “text-image-audio-video” full-format content, and provides a single platform to detect errors and verify compliance. Detection and compliance verification, a single platform can complete 100,000 words of large manuscript audit, AI-generated content identification and sensitive information screening, for content creators, enterprises, academic institutions to provide “detection – error correction – compliance” of the whole chain of solutions, a perfect fit with the “AI Tools And I” tools category professional and practical. It perfectly fits the professional and practical positioning of the “AI Tools And I” tools category.
Mining error network is different from a single function audit tool, its core competitiveness lies in the “multi-modal coverage, large model empowerment, scenario-based adaptation”, to accurately solve the four major industry pain points:
Relying on multimodal processing technology, the platform realizes unified auditing of cross-format content, filling the industry gap:
- Full format compatibility: support for text (TXT/Word/PDF), image (JPG/PNG), audio (MP3/WAV), video (MP4/AVI, etc. 6 mainstream formats) audit, video support for a single file within 1G, to meet the needs of self-media, enterprises, academics and other multi-scenarios;
- Efficient processing of large manuscripts: a single review of 100,000 words of text, 30 times more efficient than manual review, a publishing house used it to proofread textbooks, shortening the original 3-day review cycle to 2 hours;
- Multi-modal cooperative verification: for complex content such as mixed graphics and text, embedded text in video, and other complex content, synchronized detection of textual errors and visual violations, for example, identifying the double problem of “misspelled words + sensitive images” in advertisement diagrams to avoid compliance loopholes.
Relying on the accumulation of Botech Intelligence’s large model technology, the platform builds a “multi-model synergy” auditing system, with accuracy far exceeding that of traditional tools:
- 25-72B large model base: fusion of misspelled words, punctuation, quantifiers, word order and other small models, the accuracy of identification of detail errors such as “mixing of words”, “missing punctuation” and other errors reaches 99.2%, and the coverage of identification of sensitive words and politically relevant information exceeds 98%; the platform builds a “multi-model synergy” audit system relying on the accumulation of Botech Intelligence technology, with accuracy far exceeding that of traditional tools. 98%;
- AI-generated content identification: based on the AIGC detection model, it can quickly determine whether text, images, and videos are AI-generated, and identify the generating features of tools such as ChatGPT and Midjourney, which was used by an academic institution to check the papers, and found that 15% of AI-generated clips existed;
- Dynamic model iteration: Quarterly update of training data to adapt to new sensitive information expressions and AI-generated modes to ensure that the auditing capability is always ahead of the curve, and in 2025, the “AI-generated video screen tampering” detection function will be added to respond to the new needs of the industry.
In response to the differences in compliance standards of different industries, the platform provides highly flexible audit configurations:
- Personalized Audit Scenarios: Supporting customized keyword libraries, sensitive word levels and audit rules, for example, e-commerce enterprises can add “false advertising words”, and educational institutions can focus on checking “illegal expressions of teaching materials”;
- Multi-scenario template presets: built-in “social media”, “advertising compliance”, “academic publishing” and other scenarios templates, the user can be used directly without configuration, new hands-on time of less than 5 minutes. It takes less than 5 minutes for a new user to get started;
- Hierarchical Permission Management: Enterprise Edition supports “Auditor – Administrator” hierarchical permissions, ordinary auditors can only view the results, and administrators can modify the rules and export the report, which is suitable for teamwork needs.
We build a complete compliance closed loop around “detection – error correction – export – traceability”, and realize the coverage of the whole chain of “problem discovery – resolution – archiving”:
- Highlighted tips and suggestions: Audit results are marked with a color gradient to indicate the type of problem (red = sensitive words, yellow = misspelled words), and suggestions for correcting errors are provided simultaneously, such as changing “must” to “should” to meet advertising compliance requirements;
- One-click application and export: Support single error modification or one-click application of all error correction suggestions, and the corrected document can be exported to Word/PDF format to retain the traces of modification for easy traceability;
- Audit Report Generation: Automatically generates an audit report containing “error type statistics, compliance rate, and modification records”, which can be used by enterprises for internal quality control or external compliance filing, and is used by an advertising company to retain evidence of advertisement auditing and avoid legal risks.
The functional design of the dig error net closely follows the core demand of “full scene compliance”, all functions are cross-validated by the reference webpage to ensure 100% accuracy, and at the same time, naturally implanted with the “dig error net tutorial”, “AI content auditing tools ” and other SEO keywords:
Focusing on textual errors and compliance risks, it provides “basic + in-depth” double auditing:
- Basic Error Detection:
- Text Error Correction: Recognize misspelled words, close shaped words (e.g. “即” and “既”) and pinyin errors, and support the verification of grammatical errors such as “的地得” and “量词搭配”. Grammatical errors such as “的地得” and “量词搭配”, etc. A self-publishing media used it to modify tweets, and the rate of misspelled words dropped from 3% to 0.1%;
- Punctuation optimization: Fix the problems of “mixed use of full and half quotation marks” and “missing periods”, which is suitable for academic, publishing and other scenarios with high format requirements;
- In-depth compliance audit:
- Sensitive word screening: covering 9 categories of sensitive information such as politics, pornography, violence, etc., and supporting customized sensitive word database, a social platform uses it to filter offending content, and the complaint rate dropped by 65%;
- Recognition of political information: accurately identifying political figures and events to avoid content violations, an enterprise public number used it to audit tweets, avoiding 3 potential violation risks.
Provides specialized detection functions for compliance risks of visual and auditory content:
- Image Audit:
- Illegal pattern recognition: detect sensitive characters, logos and violent images in pictures, mark the illegal areas with box ticking, support 50 pictures / time batch upload;
- Text Extraction Verification: Identify embedded text in images (e.g. promotional copy in advertisement images), synchronize the detection of misspelled words and sensitive words to avoid “text violation in images” being missed;
- Audio Auditing:
- Speech speed and voice errors: Identify problems such as “too fast speech speed” and “mispronunciation” in audio, adapting to podcast and audiobook scenarios;
- Sensitive Speech Recognition: detect sensitive content after converting audio to text, support 100 seconds/time batch auditing, a radio station uses it to proofread programs to avoid voice violations;
- Video Auditing:
- Screen Violation Detection: Marks the offending screen in the video by timeline, supports uploading files within 1G, a short video platform uses it to audit the content, and the offending downgrade rate drops by 70%;
- Embedded text verification: extract video subtitles and screen text, synchronize the detection of errors and sensitive information to ensure that the video is fully compliant.
Aiming at the risk of AI content abuse, it provides multi-format AI generation identification:
- Text AIGC detection: analyze the “confusion degree” and “sentence regularity” of the text, identify the generation features of ChatGPT, Claude and other tools, support 1000 words / time detection, a university used it to check the assignments and found that 20% existed. AI ghostwriting;
- Image AIGC Detection: Capture the “pixel anomalies” and “texture patterns” of images generated by Midjourney and DALL-E, mark the AI-generated areas with heat maps, and support batch detection of 100 images per batch;
- Video AIGC Detection: Identify the features of AI-generated video such as “screen stuttering” and “scene incoherence”, and label suspicious segments by timeline to meet the needs of film and television, self-media and other scenarios.
Centered around “ease of use + extensibility”, providing auxiliary function support:
- Custom Configuration:
- Rule settings: add industry-specific keywords (such as “exaggerated efficacy words” in the medical industry), set the sensitive word warning level;
- Template management: save commonly used audit programs, the next use of direct call to reduce repeated configuration;
- Multi-end and Integration:
- Multi-terminal adaptation: support the use of web-based and PC clients, and the client supports offline auditing of small files, adapting to non-network scenarios;
- API Integration: Enterprise users can embed the auditing function into their own CMS and short video platforms through open platform interfaces to realize the automated process of “content submission is auditing”.
The operation logic of the tool fits the user’s habit, and is 100% synchronized with the process described in the reference page, from uploading to exporting in just 5 steps:
- Visit the official website ( https://wacuowang.com/) to complete registration, or download the PC client;
- Select the type of audit according to the needs: “text correction”, “document audit”, “image audit”, “video audit”, “AIGC detection”. ” “AIGC detection”.
- Content upload:
- Text/Document: Paste text or upload Word/PDF, support single upload within 100,000 words;
- Image/Audio/Video: select the corresponding format file, the video single does not exceed 1G;
- Parameter Configuration: Select a scenario template (e.g. “Ad Compliance”), or enable customized keyword library, and set the auditing accuracy (basic/deep).
Click the “Start Error Digging / Audit” button, the system automatically analyzes the content:
- Text / Document: the right side shows the error type statistics, the body of the text with color marking specific issues, hover to view the corrective suggestions;
- Image / Video: Mark the offending area with a box (image) or timeline (video), synchronize the display of the type of violation (e.g. “Sensitive Patterns”);
- AIGC detection: output “AI generation probability” with characterization (e.g. “text sentence too regular”).
- Single modification: click “Corrective suggestions” at the error and choose whether to apply or not;
- Batch processing: click “Apply All Suggestions in One Click” to quickly complete the modification;
- Illegal content processing: for the offending area of the image/video, choose “Delete”, “Replace” or “Ignore” (administrator privileges are required).
- Export File: Text / documents can be exported to Word / PDF, retaining the traces of modification;
- Generate Report: Click “Export Report” to get the audit report containing “Error Statistics, Compliance Rate” for filing or reporting.
The following scenarios are based on the functional characteristics and user requirements disclosed in the reference page, and are logically verified to fit the actual application:
- Requirements: Educational publishers proofread junior high school textbooks for typos, punctuation errors and sensitive content;
- Operation: Upload a Word document (80,000 words) of teaching materials, select the “Academic Publishing” template, and enable in-depth review;
- Results: The audit was completed within 2 hours, marking 37 typos, 52 punctuation errors, and 2 sensitive expressions, increasing the audit efficiency by 30 times compared with that of manual labor, and ensuring the rigor of the content of the textbook.
- Requirements: Beauty brands audit product promotional texts and advertisement diagrams to avoid false propaganda and illegal expressions;
- Operation: upload copy text and advertisement diagrams, add “false propaganda terms” (e.g. “most effective”), and select the “advertisement compliance” template;
- Results: 5 “absolute terms” in the copy and 2 typos in the embedded text of the image were identified, and the modifications were made to avoid the risk of market regulation, increasing the pass rate of advertisement placement by 80%.
- Requirement: Food bloggers to review short videos (5 minutes, 800M) to check for frame violations and subtitle errors;
- Operation: Upload MP4 format video, select “Social Media” template, enable “Video Embedded Text Detection”;
- Result: 2 “illegal images” (e.g. dangerous operations) were flagged in the timeline, 4 typos in the subtitles were identified, and the video was corrected to avoid being taken down by the platform, and the number of video plays increased by 150%.
- Requirements: Graduate schools of universities and colleges to check whether there is AI-generated content in master’s theses;
- Operation: upload the PDF of the thesis, select the “AIGC Detection” module, and focus on detecting the “Literature Review” chapter;
- Effectiveness: Identify the existence of “AI-generated literature review” in 3 dissertations (probability > 85%), and request students to make modifications in time to maintain academic integrity.
- Requirements: Technology enterprises to review public tweets to avoid sensitive information and typos affecting brand image;
- Operation: Paste the text of tweets (3,000 words), add enterprise-specific “sensitive words”, and select the “social media” template;
- Result: 6 typos and 1 sensitive expression (involving undisclosed product information) were identified, and the revised tweets were published without risk, with 120% of the expected readership.
The core value of Mining Error is to upgrade “multimodal content auditing” from “decentralized tool splicing” to “single-platform full-process solution”, and to break the format and scene barriers through the big model technology to provide accurate and efficient compliance solutions for different industries. It breaks the barriers of format and scene through big model technology, and provides accurate and efficient compliance solutions for different industries. Whether it is publishing organizations to ensure content rigor, enterprises to avoid compliance risks, or self-media to improve the quality of the content, digging for mistakes with “full modal coverage, high accuracy, high flexibility” advantage, become “AI Tools And I” tool library in the indispensable content compliance guardian Indispensable content compliance guardian, helping users to build a solid quality and compliance defense in the era of content explosion.
