Preventing AI Hallucinations with Verified Data

Most RFP teams are turning to AI by creating smaller, fine-tuned models within OpenAI to streamline their processes. While this can save time, it often leads to a critical issue: AI hallucinations—when generative models produce inaccurate or fabricated responses—can derail your proposal process.

Why does this happen?
These models are built to conform to instructions, even if it means fabricating answers. The result? Legal and compliance risks, misinformation in proposals, and lost deals. It’s no surprise that many companies are still hesitant to fully embrace AI—it often feels impersonal and unreliable.

The root of the problem lies in how these models work. OpenAI systems typically analyze massive datasets from the open web, looking not at the actual meaning of the text but at patterns in language. This can result in surface-level answers that lack substance or accuracy.

Iris takes a fundamentally different approach.

We don’t rely on open-source data or outdated legacy Q&A banks that return generic, canned responses.
Iris generates answers directly from your internal documentation, knowledge base, and approved content libraries—ensuring relevance and compliance.
As your team uploads new materials, Iris learns and adapts, continuously improving and delivering tailored responses that reflect your voice and priorities.

Each answer Iris provides is grounded in your internal knowledge—not guesswork.

That’s why leading proposal teams choose Iris—to prevent AI hallucinations, protect brand integrity, and scale their RFP automation with trust.

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