LLMs generate output that sounds authoritative but is dead wrong. Catch it before users see it. Validate every AI-generated output against your real data.
You shipped an AI feature. It works 94% of the time. The other 6%, users see confident lies. Your customer support chatbot claims you're out of stock when you're fully stocked. Your sales assistant quotes wrong pricing. Your content generator invents statistics that sound credible. One hallucination erodes trust permanently.
GPT, Claude, Llama, Mistral. All LLMs hallucinate. It's not a bug. It's architectural. You can't fix it by prompting harder or fine-tuning longer. You can only catch it by validating every output before it reaches users.
This is quality assurance for language models. You define what "correct" means in your domain: inventory accuracy, pricing consistency, data alignment, temporal validity, knowledge cutoffs. AI Output QA compares every LLM response against your ground truth and blocks outputs that don't match.
It runs at inference time, after the LLM generates but before the user sees. No retraining. No prompt engineering. Just validation. Millisecond latency.
Checks every LLM output the moment it's generated. Hallucinations never reach users. You show a fallback response instead: "I'm not sure" beats confident lies.
Define validation rules in plain English: "If a product ID is mentioned, verify it exists in inventory." "If a date is mentioned, it must be in the future." Rules run against your live data, APIs, and databases.
Integrated between your LLM API and your product. OpenAI, Claude, Llama, Cohere, self-hosted. One line of middleware. No vendor lock-in.
Across 12 production deployments, AI Output QA caught 4,200+ hallucinations that would have cost customers time, money, and trust. Average time to regain customer confidence: 34 days. With validation: zero damage.
An online retailer used Claude to auto-generate product descriptions. Claude occasionally invented prices ("This retro desk lamp is $15" when it was actually $85). Each hallucination cost $200-800 in refunds, customer service overhead, and reputation damage. Over 6 months: $47,000 in losses.
Integrated AI Output QA Layer between Claude and the product catalog API. One validation rule: "If price mentioned in output, check against inventory database; block if difference exceeds 5%."
In 6 weeks, QA caught 47 price hallucinations. Zero reached production. Customer complaints about pricing dropped from 6/week to 0. Monthly hallucination cost: $0. Confidence in AI descriptions: 98%.
Industry sweet spot: SaaS founders (Series A and beyond), agencies, customer support platforms, content generation tools, financial software, legal tech, healthcare tech.
1. Define. Write validation rules in plain English. "If a date is mentioned, it must be in the future." "If a product code is mentioned, it must exist in our database." "If a person's name is used, verify with our team directory." Rules live in a YAML config file.
2. Integrate. Drop our middleware between your LLM API and your product. One import. Works with OpenAI, Claude, Anthropic, Cohere, self-hosted models. No code changes to your prompt or model selection.
3. Validate & Monitor. Every inference hits the validation layer. Correct output passes through. Failed output gets the fallback response you defined. Dashboard shows hallucination rate by rule, by model, by feature. Trending data helps you understand where the LLM struggles.
100k validations/month. Best for testing and small pilots. Includes dashboard and API access.
1M validations/month. Most SaaS founders start here. Priority support, advanced rules.
On-premise option, dedicated support, custom rule development, SLA guarantees.
All plans include 30-day free trial. No credit card required.
Bigger models hallucinate less. Prompt engineering helps a little. Fine-tuning helps more. But no model has solved hallucination. It's a fundamental property of how neural networks work: they're designed to predict the next token that "feels" right, not to know what's actually true.
The only way to ship confident AI features is to validate output. Every team we talk to is doing this validation work manually: writing scripts, building internal dashboards, running manual review queues. We took that operational burden and turned it into a product so you can focus on building instead of babysitting.
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