AI Hallucinations is suitable for individuals and teams to quickly verify and implement.

AIHallucinations

Core parameters and statistics

Project	Specifications
Product Name	AI Hallucinations
Category	AI Quality Assurance
Delivery form	Web / SaaS + API
Support Platform	Web
Supported languages	Chinese, English
Target users	AI application developers, quality assurance teams, technical decision-makers
User scale	Undisclosed
Pricing Model	Freemium / Subscription

Platform coverage and user scale data are based on the official real-time page and third-party statistics.

User and market recognition

Gradually build user awareness in the field, and product capabilities are used by content creators and teams to improve work efficiency. Some industry users have incorporated it into their daily workflow. It is recommended to refer to the latest official disclosures for specific user scale and industry adoption rate data.

Cost advantage

Cost Dimension	Description
Free version	50 detections per day + basic detection dimensions, used on the Web
Subscription version	1000 detections per day + multi-model comparison + API access, $49/month
Enterprise Edition	Unlimited detection volume + Private deployment + Customized detection model + Exclusive SLA

Compared with traditional manual inspection (coverage rate 10%-20%), embedded automated inspection can increase the coverage rate to 100%, and compress a single inspection from 15-30 minutes to less than 30 seconds.

Main functions

• Sentence-by-sentence hallucination detection: Sentence-level analysis, identifying three types of hallucinations: factual errors, logical inconsistencies, and common sense deviations, with confidence scores and reasoning processes. Applicable tasks: locating specific error points in AI-generated text; usage value: upgrading from general “quality perception” to specific problems that can be located.
• Multi-model comparative evaluation: Run multiple large models on the same input to compare illusion rates and output quality. Applicable tasks: model selection evaluation; usage value: quantifying the reliability differences of different models in business scenarios.
• Prompt Optimization Suggestions: Analyze factors in the prompt words that may cause hallucinations and give optimization suggestions. Applicable tasks: extending from "post-discovery" to "pre-prevention"; use value: reducing the probability of model hallucinations.
• Evaluation Report and Dashboard: Generate a reliability evaluation report based on the time dimension, display the hallucination rate trend and distribution ratio, and support PDF/CSV export. Applicable tasks: quality tracking and auditing; value in use: providing quantitative decision-making basis for management.
• API Integration: RESTful API supports both synchronous (real-time detection) and asynchronous (batch processing) modes. Applicable tasks: Embedding CI/CD pipeline; Usage value: Automated quality access control.

Model and version evolution

Version	Date	Key Changes
Latest version v1.0	2026-07	Four-dimensional comprehensive detection framework, multi-model comparison, Prompt optimization, Chinese support, API open
Previous version v0.9	—	Rules + lightweight classifier, covering fact checking and logical reasoning, English only, accuracy 65%-70%

The version record shall be subject to the official release notes.

Technical advantages

• Multi-dimensional comprehensive detection framework: Simultaneously runs four pipelines: fact checking, logical reasoning checking, common sense consistency and internal self-consistency. The weights can be configured to adapt to different scenarios.
• Quantitative evaluation index system: Standardized evaluation indicators (hallucination rate, severity distribution, type distribution), making it possible to compare the quality of different models/versions/prompts.
• External knowledge base integration: Integrate dynamic retrieval with knowledge sources such as Wikipedia and Wikidata, and internal self-consistency analysis to supplement and verify non-public information.
• Security and Compliance: Supports privatized deployment to meet data localization compliance requirements, and test results can be saved as audit evidence.

How to use

Entrance	How to use
Web side	Browser access → Select detection mode → Paste/upload text → Run detection → View results → Optimization
API	Embed detection interface in CI/CD process or production environment

Typical usage process: Register an account → Select detection mode → Upload AI-generated text → Run detection → View sentence-by-sentence analysis → Adjust according to Prompt optimization suggestions → Generate evaluation report.

Product Pricing

Package	Price	Contents
Free version	$0	50 tests per day + basic dimensions
Professional version	$49/month	1000 times per day + multi-model comparison + API
Enterprise Edition	Business Pricing	Unlimited + Privatization + Custom Models + SLA

Pricing. There are different pricing in different regions.

Application scenarios

• AI application production quality access control: Detection is embedded in the CI/CD process, and the coverage rate is increased from 10%-20% to 100%. Verification method: Manually inspect the output marked as "high hallucination risk" by the system.
• Model selection and version evaluation: Systematically compare the hallucination rates of candidate models based on your own business data. Verification method: Each model tests 200-500 business samples to ensure statistical significance.
• Quality review of batch content generation: Only review parts marked as high risk, reducing labor investment by 70%-85%. Verification method: Compare the consistency of system marks and manual review results.

Applicable people

• Individual User: AI application developers verify model output quality during the development stage
• SME Team: QA team uses quantitative metrics to establish a quality baseline
• Large Enterprises: Technical decision-makers obtain objective data on model reliability to assist in selection.
• Unfit Boundary: high-frequency low-latency scenarios (requires confirmation of API response time <500ms), highly subjective judgment content, model sycophancy and training data bias and other deep-seated issues

Comparison of competing products

Comparative Dimensions	AI Hallucinations	Manual Sampling	General Benchmark
Core differences	Four-dimensional comprehensive detection + configurable weight	Depth but low coverage	Universal but lacks specificity
Price	Freemium	High labor cost	Free
Covered scenes	Text hallucination detection + multi-model comparison + Prompt optimization	Full scene	Standardized evaluation
User evaluation	Rich detection dimensions	Accurate but time-consuming	Unable to cover business scenarios
Technical threshold	Low	High	Low

Summary and Outlook

AI Hallucinations focuses on the key quality assurance link of large language model hallucination detection, providing a systematic tool chain from sentence-by-sentence detection to multi-model comparison to prompt optimization. The four-dimensional detection framework and configurable quantitative evaluation indicators provide a structured solution for quality assurance of AI applications. It is recommended to pay attention to its function iteration speed, pricing strategy changes and ecological expansion direction. It is suitable to use the free version to experience it first and confirm the matching before making an investment decision.

Risk Disclosure: Detection accuracy ranges from 70%-90% (depending on task type and language) and cannot completely replace human review. Test results should be regarded as "quality warnings" rather than "final judgments" - high-risk scenarios need to enter the manual review process. Multi-modal output detection is not supported yet. The detection accuracy of Chinese is slightly lower than that of English. The depth of integration of industry-specific knowledge bases needs further verification. Before purchasing the enterprise version, you need to confirm that the privatized deployment meets the data localization compliance requirements.

Related tools: crewai, langchain

Version Info

• Public beta version ：It is currently a publicly accessible version, and specific functions will be updated at a specific pace. （2026-07-14）
• earlier version ：An early trial version, the core direction is consistent with the current version. （2026-07-14）