“Submissions aren’t just unstructured – they’re unpredictable.”
That’s the reality every specialty underwriter faces when dealing with high-stakes documents filled with financial nuance, evolving product terms, and hundreds of pages of scanned PDFs.
For years, automation promised to save time. But in the world of specialty insurance, most AI systems still fail. Why? Because this isn’t simple extraction, it’s reconstruction. It’s about piecing together fragmented, ambiguous data within the context of each line of business in a way that preserves meaning, complies with regulation, and supports sound underwriting decisions
What makes submission automation so hard?
- No single model can handle it. LLMs, OCRs, and table parsers each excel at different things. Real-world submissions demand hybrid, orchestrated workflows that route tasks to the right model in real time.
- Domain knowledge matters. Generic AI falls short when it encounters ambiguous terms like “limit” or “aggregate deductible” – terms that shift meaning across regions or product lines.
- Agents must work together. Autonomous agents for extraction, validation, quoting, and communication need tight coordination and explainability.
- Accuracy is everything. Even a 2% error rate can undermine trust. Identifying and isolating these errors requires advanced error handling and real-time confidence scoring.
As Vinod K Singh, President & CTO at Concirrus, puts it:
“You’re not just building an AI model, you’re building an ecosystem of trust, transparency, and domain-specific intelligence.”
How Concirrus approaches this:
- Meta-AI orchestration for routing tasks to the right models
- Fine-tuned LLMs and domain ontologies for semantic precision
- Agentic AI workflows that integrate tightly with underwriting pipelines
- Privacy-first infrastructure that protects sensitive submission data
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