About — Fnolwise

Founding Story

Built by someone who spent seven years tracking the problem

Valerie Lundgren spent seven years as a commercial claims supervisor at a Hartford regional carrier. Most of that time, she was doing the work that should have been automated: calling agents to get complete policy numbers, re-entering FNOL details from email threads into ClaimCenter, figuring out which adjuster had capacity for a roof loss on a 40-unit commercial building.

She tracked it. Fourteen hours a week — hers alone, not her team's — lost to FNOL callbacks and incomplete intake records. Not investigation time. Not coverage analysis time. Intake administration time.

In 2022, she ran a structured intake pilot on incoming commercial FNOLs: a standardized web form, mandatory fields by loss type, automated policy cross-reference. Within four months, adjuster callback rates from incomplete intake dropped 40%. The work was still being done manually — just better-structured manual work. But the 40% reduction was enough to validate the hypothesis: the problem wasn't the adjusters, it was the intake layer feeding them.

Fnolwise was founded in 2024 to productize that hypothesis at carrier scale — with ML-based severity triage, coverage verification, and direct ClaimCenter/Duck Creek push replacing the manual coordination that had been absorbing weeks of adjuster time every month.

"Fourteen hours a week. That's what incomplete intake was costing me personally. Multiply that across a team of eight adjusters and you're not talking about inefficiency — you're talking about a structural claims capacity problem that looks like a people problem."

Valerie Lundgren CEO & Co-Founder, Fnolwise

By the numbers

Structured intake pilot duration 4 months

Callback reduction achieved 40%

Company founded 2024

Our Values

What we believe about commercial claims

Four principles that shape every product decision we make.

Adjuster time is claims capacity

Every minute an adjuster spends on intake administration is a minute not spent on investigation, negotiation, or closure. Capacity is the constraint in commercial claims, and intake overhead is the most addressable drain on it. We build every feature against this principle first.

Structured data before AI inference

We do not apply inference where structure is available. Policy numbers, coverage terms, and loss type fields should be captured deterministically — not predicted. Our ML applies where structure genuinely fails: severity scoring, priority classification, and assignment matching. This distinction matters for carrier compliance and audit trail integrity.

Coverage accuracy before speed

Speed is our value proposition, but not at the cost of coverage accuracy. A fast FNOL push that contains a wrong coverage determination creates downstream cost that dwarfs the intake delay we were trying to fix. We run coverage verification synchronously, on every FNOL, before assignment. Speed comes from eliminating the manual steps — not from skipping the checks.

Carrier workflow first

We don't ask carriers to rebuild their claims workflows around Fnolwise. We integrate into the systems, supervisor review processes, and assignment rules already in place. ClaimCenter is ClaimCenter. Duck Creek is Duck Creek. Fnolwise accelerates the intake layer that feeds them — it doesn't replace the operating model carriers have built over decades.

Cut the time between a commercial loss event and an adjuster's first informed action to under 20 minutes