In a market flooded with AI claims, it’s easy to get swept up in impressive-sounding accuracy percentages. But when you dig deeper, many of these results are hypothetical, cherry-picked, or based on synthetic or limited datasets.
At Insurants, we believe accuracy isn’t a pitch—it’s a performance metric. That’s why we don’t just talk about outcomes. We measure them, every month, across all lines of business and every market we serve.
Real AI Needs Real Measurement
We support clients across a wide range of commercial insurance products—Property, Casualty, Marine, Energy, Cyber, Reinsurance, and more—each with its own data challenges and document structures. It’s not enough to say “it works.”
So, we built a scalable, systematic approach to proving it works. Consistently. At volume. In production.
Monthly Sampling: Our Accuracy Assurance Framework
Every month, Insurants conducts a controlled sampling and review process across client engagements. Here’s how it works:
Randomized Sampling
From the thousands of documents processed monthly, we take a statistically valid sample across different classes, markets, and document types.
Human Review of AI Outputs
Subject matter experts compare AI-extracted data against source documents using predefined field-level criteria to validate correctness and completeness.
Accuracy Scoring
Each field is scored individually, and aggregate accuracy rates are reported by Line of business, Geography, Document type (e.g., slip, endorsement, binder) and AI model component (GenAI, rules-based, etc.)
Continuous Tuning
Insights from each month’s sample are fed back into model retraining, business rule refinement, and system tuning—ensuring continuous improvement with clear ROI.
Not Just Accuracy—Client Outcomes
Measuring accuracy isn’t just about validating the technology. It’s about embedding confidence and creating action for clients:
- Enabling straight-through processing with minimal manual intervention
- Validating compliance with internal and external audit standards
- Quantifying operational savings tied to real improvements, not assumptions
- Supporting deployment decisions across new regions, lines, or workflows
“We’re not making claims about what’s possible. We’re showing what’s already working—on your data, in your process, at enterprise scale.”
– Laurence Trigwell, CRO Insurants AI
Why This Matters More Than Ever
As commercial insurers increasingly adopt GenAI and AI-driven automation, scrutiny from stakeholders is growing. Regulators, boards, and auditors are asking:
- How do you know your AI works?
- Can you prove its accuracy?
- Can you explain its decisions?
Insurants clients can answer “yes” to all of the above—because we measure and report on it, every month.
Hypotheticals Are Easy. Proof Is Hard. We Choose Proof.
While others market potential, we deliver proof. At Insurants, we’ve built our business not on theoretical capabilities, but on verifiable performance at real volume, across the complex, high-stakes world of commercial insurance.
If you’re investing in AI, demand more than a demo. Demand measurement. Demand improvement. Demand results.
Want to see how our approach works with your documents, lines, or markets?
