Model hallucination slows teams down

Model hallucination is one of the biggest hurdles to production-grade AI. How do you know when your model is wrong in production? If you build with AI, you likely know that you don’t. The reality is model hallucination isn’t a bug. It is a fundamental behavior of large language models, and it’s here to stay.

Companies want AI's productivity gains, but a single hallucination can result in customer complaints, damaged reputation, or operational disruption.

These leaves teams with two options:

1. Deploy with extensive manual oversight which offsets AI’s speed; or,

2. Risk it, deploy, and hope for the best.

Today, we're excited to announce a third option: Verify by kluster.ai. Verify is an intelligent agent that validates LLM outputs with real-time knowledge. Verify gives you the trust to deploy AI at scale where accuracy matters most.

Trust vs. speed in AI deployment

You have a trade-off when deploying an LLM. Trust or speed. If you want to move quickly, you have to accept the risk of AI hallucinations. If you want to trust your model, you have to implement time-consuming and expensive manual reviews.

Existing verification tools fail to solve the problem because they require extensive tuning and generate excessive false positives. This slows teams down without meaningfully improving trust.

The cost of getting it wrong compounds in production:

• Quality issues that reach production can take significant effort to remediate

• Inaccurate AI outputs require expensive human intervention to fix

• Manual verification bottlenecks slow down entire workflows

• Teams either over-review (wasting time) or under-review (accepting risk)

Ship features, not apologies: Verify by kluster.ai

We have designed Verify to remove the trade-off between trust or speed.

• Works immediately: No manual threshold tuning or configuration required.

• Provides transparency: Includes reasoning and citations for decisions.

• Universal applicability: Works with any LLM architecture, whether you're using RAG with your knowledge base or running standalone inference.

• Minimizes false positives: Optimized for precision to avoid flagging valid content.

• Out of the box integrations: Easily integrates via REST endpoints, OpenAI-compatible APIs, MCP servers, and popular workflow tools like n8n and Dify, ensuring smooth incorporation into your existing systems.

Try for free today and experience immediate improvement in your LLM reliability. Or, continue reading for a deeper dive into how it works.

What this means for you

Trustworthy verification

Strong verification performance across a range of domains, reducing the risk of harmful AI outputs reaching your customers.

Immediate impact

Unlike other verification tools that require configuration and threshold tuning, Verify works immediately out of the box. Use today, start verifying within minutes.

Works everywhere

Whether you're running customer support chatbots, content generation workflows, or complex RAG applications, Verify delivers consistent accuracy without requiring separate configurations for each use case.

Protect your reputation

Every prevented hallucination protects your brand trust and customer relationships. Verify acts as a safety net, helping you deploy AI confidently while maintaining quality standards.

How Verify works

Verify is an intelligent agent that analyzes three key inputs to assess LLM output reliability:

1. The original prompt: Understanding the user's intent and context

2. The model's response: Assessing content accuracy and relevance

3. Optional context: Verifying claims against provided source materials

Beyond traditional RAG

Unlike conventional verification tools, Verify is an agent with real-time internet access, enabling validation across dynamic scenarios that extend far beyond traditional RAG use cases.

Proven performance

We evaluated Verify against industry-leading solutions across two comprehensive benchmarks (HaluEval and HaluBench) covering over 25,000 samples from domains including healthcare (PubMedQA), finance (FinanceBench), COVID-19 research, and general reasoning tasks.

Our evaluations address the two most common LLM scenarios:

1. Non-RAG scenarios, where the model answers solely based the conversation.

2. RAG scenarios, where context is provided to guide the LLM’s response.

We compared against leaders in their respective domains: CleanLab TLM for its API based general-purpose trustworthiness checks, and Patronus AI's Lynx 70B as a specialized RAG verification model. This helps ensure our evaluation reflects real-world performance against the commonly used alternatives.

Experiment 1: Non-RAG scenarios

Setup: In many real-world applications, users interact with AI without supplying reference materials. To reflect this, we evaluated hallucination detection across five context-free datasets, focusing on general-purpose, non-RAG use cases.

Compared against: CleanLab's Trustworthy Language Model (TLM) using their default model (GPT 4o-mini) and quality (medium), along with a manually optimized threshold that maximized performance on a data subset.

Key results:

• 11% higher overall accuracy across all datasets.

• 2.8% higher median F1 score (72.3% vs. 69.5%), indicating good precision recall balance.

• Higher precision: Significantly better at identifying true hallucinations, reducing false positives.

• Low response time: Comparable performance (both models respond in under 10 seconds).

• Cost advantage: Verify is priced at $4 per 1M input tokens and $7 per 1M output tokens versus TLM's default configuration of $5 per 1M input and $8 per 1M output tokens.

Why it matters: Verify achieves the best overall accuracy, consistently highest precision, while maintaining strong F1 performance, making it particularly effective for production environments where both catching hallucinations and avoiding false positives matter.

Experiment 2: RAG validation

Setup: Scenarios where users provide context documents, and responses must stay grounded in that context.

Compared against: Patronus AI's Lynx (70B), specifically designed for RAG applications and Cleanlab TLM default configuration using a manually optimized threshold that maximized performance on a data subset.

Key results: Verify demonstrates strong performance against both specialized RAG tools and general-purpose verification systems. On RAGTruth, which measures factual consistency in RAG, Verify achieved significant improvement over Lynx 70B and substantially outperformed CleanLab TLM. On DROP, which measures numerical and logical reasoning, Verify maintained competitive performance against Lynx while delivering considerably better results than CleanLab TLM.

Why it matters: Notably, Lynx was specifically trained on the training sets of both DROP and RAGTruth, making Verify's competitive performance even more impressive. This demonstrates Verify's key advantages: flexibility across unseen datasets and high performance in both RAG and non-RAG scenarios.

API access

# REST API 
curl -X POST <https://api.kluster.ai/v1/verify/reliability> \\
  -H "Authorization: Bearer YOUR_API_KEY" \\
  -H "Content-Type: application/json" \\
  -d '{
    "prompt": "Tell me about the new iPhone 20 features",
    "output": "The iPhone 20 includes a revolutionary holographic display, 200MP camera with AI scene detection, and can project 3D holograms up to 6 feet away for video calls.",
    "context": null
  }'
 
# OpenAI-compatible endpoint
curl -X POST <https://api.kluster.ai/v1/chat/completions> \\
  -H "Authorization: Bearer YOUR_API_KEY" \\
  -H "Content-Type: application/json" \\
  -d '{
    "model": "klusterai/verify-reliability",
    "messages": [
      {
        "role": "user",
        "content": "What can you tell me about Milos Burger Joint?"
      },
      {
        "role": "assistant",
        "content": "Milos Burger Joint has been serving authentic Burgers cuisine since 1999 and just won 2 Michelin stars last week, making it the highest-rated burger restaurant in the city."
      }
    ]
  }'

Workflow integrations

• n8n: Pre-built nodes for workflow automation

• Dify: Native integration for AI application builders

Platform integrations

• MCP server - Compatible systems such as ChatGPT Desktop, Claude Desktop and Cursor IDE

• Custom applications - Flexible REST endpoint integration

• Enterprise systems - Dedicated endpoints with custom SLAs

Getting started

Start using Verify by kluster.ai today and experience firsthand the ease of improving your AI model's trustworthiness and accuracy.

1. Register for free account

2. Check out our Documentation to get started

What's next

Verify is the first step in our mission to make AI deployment safer and more reliable. Coming soon:

• Improved performance across the board

• Domain-specific verification models

• Custom fine-tuning on your data and tasks

• Advanced analytics and reporting

• More verification categories such as Safety, Fairness, Robustness, etc

Start verifying today

Don't let AI hallucinations undermine your applications. Join the growing number of organizations using Verify to deploy AI you trust.

Start your free trial: $5 in free credits, no credit card required.

Questions? Read our docs or contact support@kluster.ai

Enterprise inquiries: Schedule a demo or email enterprise@kluster.ai

Join our community: Discord server for technical discussions, support and updates.