AI hallucinations: What happens when AI makes things up?

The risk for insurance agencies, and how BluePond.AI stops it in Policy Checking and Quote Comparisons.

In most industries, if your AI tool makes something up, it's annoying. In insurance, it’s dangerous. Large Language Models (LLMs) are powerful, but they have a flaw: they sometimes hallucinate, confidently providing answers that sound plausible but are factually wrong or contextually off. In workflows like Policy Checking or Quote Comparison, this can mean misread endorsements, missed exclusions, or mismatched limits. These aren’t just technical errors. They are the kind of mistakes that lead to compliance gaps, E&O exposures, and lost business. 

Where it hurts: The risk of believable errors

When AI is used for Policy Checking or Quote Comparisons, the danger isn’t just in what it misses. It’s in what it makes up. Take Policy Checking. AI might say that “Coverage A is present” when in reality, there would be no explicit mention of it in the document. The model gets confused due to the large context of data and complex instructions. Or Quote Comparisons. AI might tell you “Carrier A has broader coverage than Carrier B” but conveniently skip over a newly added exclusion. The result? Your client binds based on incorrect information, and you’re left with the exposure.

These aren’t obvious errors. They’re subtle, believable, and hard to catch.

“There are no changes to coverage limits.”(Except a sub-limit was added under a new endorsement.)

“Carrier A has broader coverage.”(Except it excludes contractors, and the submission clearly didn’t.)

That’s the real risk with AI hallucinations: errors that sound like insights.

How does BluePond.AI solve this? 

At BluePond.AI, we use our Insurance Reference Library to complement Retrieval-Augmented Generation (RAG) to ground every AI response in real, document-based evidence. This helps the AI to identify all relevant pages for a field and then retrieve the pertinent information. It’s also not just answering your query, it’s citing its source. Every comparison, clause, or endorsement is traceable to a line in the original document.

• Step 1: Retrieve, not imagine Before the AI even starts forming a response, BluePond.AI retrieves the most relevant content from your uploaded policy documents or quote submissions, whether PDFs, scans, or system exports. The model is only allowed to work within this context.

• Step 2: Generate with guardrails Our robust document pre-processing and extraction frameworks enable us to correlate the information across pages. For instance, a coverage on Page 12, which gets modified through an endorsement on Page 60, is easily identified. The information is further validated using our reference library to ensure critical coverages, endorsements, and exclusions are not missed. This enables the models to clearly identify that Clause 5.3 in the renewal policy introduces a new sub-limit of $50,000, not present in last year’s Clause 6.2.”

• Step 3: Highlight and hyperlink Each output includes a direct reference or visual cue, so you can check it yourself. Think of it as AI with citations. Every insight comes with proof.

Why it matters

✔️ Reduces E&O exposure AI hallucinations in Policy Checking or coverage summaries can expose agencies to costly errors. BluePond.AI ensures every output is traceable to the source text, protecting your team and your clients.

✔️ Boosts broker confidence When you know your AI assistant isn’t guessing, you can trust it to handle the grunt work. That frees up time for real client advisory.

✔️ Drives operational accuracy Manual checks are slow. Ungrounded AI is risky. BluePond.AI delivers fast, accurate, and document-verified outputs.

A smarter way to scale

AI in insurance shouldn’t be about speed alone. It should be about trusted speed. With BluePond.AI’s Broker CoPilot, insurance agencies get a faster workflow without compromising on diligence or accuracy. No more hallucinations. Just real answers from real data.