Insly launches Nora: a modular AI layer for insurance operations
Insly has introduced Nora, a modular artificial intelligence proposition built to sit on top of existing insurance software. It automates repetitive, administrative work across submissions, underwriting, and claims. The goal is simple: boost efficiency, reduce workloads, and give your teams time for higher-value decisions.
"Insly's experience working with MGAs and insurers means we understand the administrative burden that holds back growth and innovation," said Risto Rossar, founder and chief executive at Insly. "From incomplete broker submissions, to inconsistent document formats and managing back and forths over email, the solution has always been to increase headcount and reduce margins. Now, Nora offers an alternative solution."
How the modular approach works
Nora is built as a set of specialised skills that can be configured to each customer's workflows. Instead of a wholesale overhaul, teams can target one bottleneck at a time and expand from there. It sits alongside your current systems, so you keep what already works.
- Pick the skills you need now, add more as you go.
- Plug into current submission, underwriting, and claims processes.
- Focus on measurable wins before scaling across lines and regions.
Where it helps first
- Submissions: standardises incoming formats, flags missing data, and organises packs for review. Using common standards (for example, ACORD) becomes far easier when documents aren't all over the place.
- Underwriting: extracts key fields, groups correspondence, and reduces copy-paste across systems.
- Claims: indexes documents and emails, routes tasks, and compiles concise summaries for adjusters.
Built on Insly's AI progress
Nora is the next step in Insly's AI journey following the launch of FormFlow in 2024. FormFlow's extraction capabilities are now part of Nora's skill set, creating a broader ecosystem. As Rossar put it: "AI isn't something you implement wholesale, but a tool to remove one insurance bottleneck at a time."
That philosophy shows up in the product shape: start narrow, clear the friction, then expand to the next task. The result is a practical AI layer that adapts to the needs of each insurer or MGA.
Why this matters for insurers and MGAs
- Reduce manual admin without adding headcount.
- Shorten cycle times from submission to quote to bind.
- Cut rework from incomplete or inconsistent data.
- Free underwriters and claims handlers for judgment calls and broker relationships.
How to get started
- Pick one high-friction workflow (e.g., new business submissions in a single line).
- Define success metrics (turnaround time, touch count, error rate).
- Map the exact handoffs and documents, then enable the relevant Nora skills.
- Pilot with a small team, measure, and roll out step by step.
If you're exploring skills, workflows, and training that align with insurance use cases, see AI for Insurance for practical guidance and examples.
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