Walled Correct
Hallucination Detection &Validation.
Validate every AI-generated response against ground truth before it reaches the end user. Set confidence thresholds and automatically flag or block hallucinated content. In regulated industries, a wrong answer isn't just embarrassing - it's a liability.
Backed & Trusted By Industry Leaders
The Hallucination Problem
of LLM responses contain hallucinations
Stanford HAI, 2024
potential liability from a single wrong AI output
Industry estimate
LLMs are confidently wrong. They generate responses that sound authoritative, cite non-existent studies, invent statistics, and fabricate legal precedents - all with perfect grammar and unwavering confidence. Without a validation layer, your employees, customers, and stakeholders may act on AI-generated fiction.
Financial Services
An AI assistant tells a customer their account has a 2.5% interest rate when it's actually 1.8%. The customer makes financial decisions based on wrong data.
Healthcare
A clinical AI suggests a drug interaction that doesn't exist, causing unnecessary alarm and changing treatment plans based on fabricated information.
Legal
An AI cites a court case that was never tried, a statute that doesn't exist, or a regulation that was repealed three years ago.
The Validation Pipeline
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. Configurable thresholds per 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.
Where Walled Correct Makes the Difference
Different use cases need different confidence levels. Walled Correct lets you configure thresholds per department, team, and application.
Customer-Facing AI Assistants
Threshold: High (95%+)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.
Internal Knowledge Management
Threshold: Medium (85%+)Employees querying internal wikis, policy documents, and process guides need reliable answers. Walled Correct validates responses against your internal knowledge base and flags when the AI is generating from its training data rather than your approved sources.
Without Walled Correct:
An HR AI assistant cites an outdated leave policy from 2022 instead of the current 2026 version. Employees take leave under terms that no longer exist, creating payroll complications.
Compliance & Regulatory Queries
Threshold: Very High (98%+)When employees ask AI about regulatory requirements, the answer must be correct. Walled Correct validates against current regulatory databases and flags any response that can't be traced to an authoritative source.
Without Walled Correct:
A compliance officer asks about PDPA requirements and the AI cites a provision that was amended 18 months ago. Decisions made on outdated regulatory guidance create audit exposure.
Research & Analysis
Threshold: ConfigurableResearch teams use AI for literature review, data analysis, and hypothesis generation. Here, creativity is valued, but fabricated citations and invented statistics must be caught. Walled Correct distinguishes between creative synthesis and factual fabrication.
Without Walled Correct:
A research analyst presents a market sizing report to the board with AI-generated statistics. Three of the five cited market reports don't exist. The board makes a $10M investment decision on fabricated data.
Stop AI hallucinations before they cause damage
Walled Correct validates every AI response against authoritative sources - in real time, before it reaches your users.
On-Premise Validation Layer
Having an on-premise infrastructure layer provides a critical additional validation step before sending answers back to end consumers. This isn't just about catching hallucinations - it's about maintaining trust in every AI-generated response across your organization.
Your ground truth data - product databases, policy documents, regulatory references - never leaves your infrastructure. Validation happens locally, against your authoritative sources, with zero external data exposure.
- Configurable confidence thresholds per use case
- Ground truth validation against enterprise knowledge bases
- Automatic flagging with human-in-the-loop escalation
- Complete audit trail of validation decisions
- Department-specific accuracy requirements
- Citation verification for sourced responses
- Version-aware document referencing
Detection Accuracy
Industry-leading hallucination detection powered by PhD-led research in AI safety and evaluation frameworks.
"WalledEval" - our open-source evaluation framework - is the research backbone behind Walled Correct's detection capabilities. Published and peer-reviewed, it benchmarks hallucination detection across multiple domains and model families.
Customer Story
Online Advisory Platform Validates 1M+ Daily Conversations with Zero Hallucination Leakage
Challenge
A leading online advisory company handles over 1 million AI-powered conversations per day across financial planning, insurance guidance, and retirement advisory. Their AI assistants were generating confident but fabricated product details, incorrect policy terms, and non-existent regulatory references - with 8% of responses containing material inaccuracies. At their scale, even a fraction of a percent meant thousands of customers receiving wrong advice daily.
Solution
Deployed Walled Correct with tiered confidence thresholds: 98% for product-specific advice, 95% for general guidance, and 99% for anything referencing regulatory requirements. Every AI response is validated in real-time against the company's product database, approved rate tables, and regulatory reference library before reaching the customer. Low-confidence responses are automatically escalated to human advisors with context.
Results
Hallucination rate dropped from 8% to 0.1% across all conversation types. At 1M+ daily conversations, this means fewer than 1,000 responses per day need human review - down from 80,000. Customer trust scores increased 22%. Regulatory complaints related to AI-generated advice dropped to zero. The human advisor team was redeployed from error-correction to high-value complex cases.
Daily Conversations
Hallucination Rate
Trust Score Increase
Regulatory Complaints
Related Resources
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 moreStop hallucinations before they reach users
See how Walled Correct validates AI responses against your ground truth in real-time.
Frequently Asked Questions
Authoritative references
Primary sources behind the standards, regulations and research referenced on this page.
- Survey of hallucination in natural language generation (ACM Computing Surveys)Peer-reviewed survey of why language models fabricate and how to mitigate it.
- NIST AI 600-1 Generative AI ProfileGenerative-AI-specific risks and suggested actions from NIST.
- OWASP Top 10 for LLM ApplicationsRecognised list of generative AI risks including prompt injection.
- NIST AI Risk Management FrameworkUS framework for governing, mapping, measuring and managing AI risk.