Walled CorrectHallucination Detection for High-Risk Enterprise Systems
Intercept ->Validate -> Deliver
Validate Every AI-response Before it reaches your end user.
Detect and Correct embarrassing LLM hallucinations before they turn into million dollar liabilities.
Hallucination Rate
Trusted By
AI Hallucinations are an Inbuilt LLM Problem
Hallucinations are an unfortunate side-effect of Generative AI. The fundamental design of LLMs creates the opportunity for hallucinations to creep in.
While newer LLM models hallucinate less, AI hallucinations remain a complex and costly enterprise problem.
of all LLM responses contain hallucinations
Stanford HAI, 2024
documented AI failures with clear hallucination-to-harm causation chains
Potential liability from a single wrong AI output
Industry estimate
Hallucinations Happen across Industries
Financial Services
An AI advisor tells a customer their account earns 2.5% when it's actually 1.8%. They make investment decisions on false data.
Healthcare
Clinical AI suggests a drug interaction that doesn't exist. Treatment plans change based on fabricated medical information.
Legal
AI cites court cases never tried, statutes that don't exist, or regulations repealed years ago.
Validate with
Walled Correct for
Complete Peace of Mind
Validating every AI-generated response for correctness is the only viable strategy ahead.
Walled Correct fact-checks every AI response against your organization's ground truth, approved knowledge bases, and verified sources. Flag AI responses for review, return them with appropriate warning labels, or block them altogether from ever reaching your end users.
Powered at the core by WalledEval, our open-source AI safety evaluation framework. Published and peer-reviewed extensively, it benchmarks hallucination detection across multiple domains and model families.
Correcting AI as Simple as 1,2,3,4
A straightforward validation pipeline that catches hallucinations in real-time, before they reach your users.
Intercept Response
Every LLM response passes through the WalledAI validation layer before reaching the end consumer. No response goes unverified.
Ground Truth Check
Responses are cross-referenced against your enterprise knowledge bases, approved datasets, and verified sources to identify factual claims.
Score & Threshold
Each response gets a confidence score. Thresholds can be configured as per the use case. A customer-facing bot needs higher confidence than an internal brainstorming tool.
Deliver or Escalate
Validated responses pass through. Low-confidence responses are blocked, flagged for human review, or returned with confidence warnings.
On-Premises, Private, and Pre-Configured Validation
Walled Correct is 100% deployable within your enterprise premises. Your ground truths, enterprise policy documents, product databases, and regulatory references never leave your organizational infrastructure.
Confidently deploy hallucination detection with zero data exposure and pre-configured policies for high-risk industries.
Keep your ground truth data, policies, and compliance references within your organization's boundaries while securely validating AI responses
Customizable Confidence Levels for Complete Control
Different use cases need different confidence levels. A customer-facing AI bot needs to enforce stricter hallucination detection policies than an employee-only knowledge base. Walled Correct lets you configure thresholds per department, team, and application depending on your use case.
Customer-Facing AI Assistants
When your AI speaks to customers, every word represents your brand. Walled Correct ensures responses are factually grounded in your product documentation, pricing tables, and approved messaging. A wrong answer to a customer isn't just embarrassing - it can create legal liability.
⚠ Without Walled Correct
A telco's AI assistant incorrectly tells a customer they're eligible for a free upgrade. 50,000 customers later, the company faces millions in fulfillment costs or a PR crisis from walking it back.
No-Stress AI with Near-Zero Latency
Correctness doesn't have to come at the cost of performance. Enjoy seamless hallucination detection with Walled Correct's rapid response time and near-zero latency.
Opt for on-premise validation and reap further performance gains.
Performance comparison across different use cases: Validation Latency (ms), Throughput (req/sec), Accuracy (%)
And Every Hallucination Detection Feature You can Ask for
Gain fine-grained control and visibility into AI hallucinations with Walled Correct's extensive feature set.
Configurable confidence thresholds per use case
Tailor validation strictness to different parts of your organization. Customer-facing applications can enforce 98% confidence while internal tools can operate at 85%.
Ground truth validation against enterprise knowledge bases
Cross-reference every AI response against your authoritative sources, product documentation, policy libraries, and verified data sets.
Automatic flagging with human-in-the-loop escalation
Flag uncertain responses for human review, block hallucinated content automatically, or return responses with confidence warnings.
Complete audit trail of validation decisions
Track every validation check, score, and decision for compliance audits, debugging, and continuous improvement of your thresholds.
Department-specific accuracy requirements
Set different validation standards for finance, legal, healthcare, and other departments based on their risk profiles and regulatory needs.
Citation verification for sourced responses
Automatically verify that AI-cited sources actually exist and contain the claims being attributed to them.
Version-aware document referencing
Validate against specific document versions so responses referencing outdated policies or regulations are caught and flagged immediately.
Real-time response blocking and filtering
Instantly block hallucinated responses before they reach users. Flag, label, or prevent problematic content from being delivered in real-time.
Custom validation rules and workflows
Define custom validation logic tailored to your organization's unique needs, policies, and risk profiles.
Ready to Experience Reliable Hallucination-Free AI?
Deploy Walled Correct to validate every response in real-time.
Hallucination Detection Success Stories
Financial Services Firm Eliminates Hallucination Liability
Healthcare Provider Ensures Accurate Patient Information
Law Firm Validates AI-Generated Documents
Learn More about Hallucination Detection
WalledEval: Safety Evaluation Toolkit for LLMs
The open-source evaluation framework powering Walled Correct's hallucination detection - peer-reviewed and benchmarked across domains.
Read more about WalledEval: Safety Evaluation Toolkit for LLMsRead moreFerret: Review, Hallucinate, Refer
Our research on building review and reference systems that catch hallucinations in multi-turn conversations.
Read more about Ferret: Review, Hallucinate, ReferRead moreThe $10M Hallucination: Case Studies in AI Fabrication
Real-world examples of hallucination-driven decisions that cost companies millions - and how to prevent them.
Read more about The $10M Hallucination: Case Studies in AI FabricationRead moreFrequently Asked Questions
How does Walled Correct detect hallucinations?
Walled Correct validates AI outputs against your organization's ground truth documents, knowledge bases, and approved data sources. It flags fabricated facts, incorrect citations, and statistically impossible claims - reducing hallucination rates from a typical 12% to just 0.3%.
Can Walled Correct work with any LLM?
Yes. Walled Correct is model-agnostic and works with GPT-4, Claude, Gemini, Llama, Mistral, and any other LLM. It validates outputs regardless of which model generated them.