Everyone in insurance wants to put AI into production, right up until the moment they have to put their name on the output. That moment keeps coming back, because the model landscape keeps shifting. A new model lands every few months, and every time one shows up, the same question hits: does everything still work?

For the past year we've been asking how teams should answer that question with evidence in hand, on their own data and their own workflows, every time the AI model underneath them changes.

Today we're launching Eval Studio inside FurtherAI, so the answer is no longer a guess.

Model swaps break workflows in ways that aren't obvious

When you swap a model or change a workflow, things break in ways that don't surface immediately. A classification that was accurate on one model drifts on the next, a prompt that handled the common cases starts missing edge cases, and a pipeline running in the background can be wrong on dozens of submissions before anyone notices. By the time the damage shows up downstream, it has usually shown up in production decisions first.

Eval Studio catches that drift before it reaches production.

Eval Studio turns real submissions into a test set

The setup is straightforward. You load a test set built from real submissions out of your own pipeline — typically 50 or 100 — and you define what "good" looks like for your team. Did the workflow capture all the policy limits correctly? Is the extracted coverage inside the thresholds you accept? Did it catch every exclusion listed in the underlying document?

You hit run, and every submission flows through your workflow at once. The results come back as an accuracy score against your definition of good, along with the specific cases where something broke. You can see where the AI is correct and where it's failing, with the specific submissions that exposed the problem surfaced for review.

Compare versions side by side before anything ships

The part that changes how teams operate sits in the comparison layer. You make a change to your workflow (ex: swap the model, update the prompt, adjust a downstream step), re-run the eval, and compare the new version against the previous one side by side. You see exactly what improved and what regressed, with the specific submissions surfaced, before any of it touches production.

For an AI workflow handling underwriting submissions or claims documents, that's the difference between deploying with evidence and deploying on theory. The teams running FurtherAI in production today use this loop on a weekly cadence: change, run, compare, ship.

Bring Eval Studio to your underwriting and claims teams

The AI landscape isn't slowing down. New models keep arriving, and with them come new capabilities and new failure modes. The only way to move confidently inside that pace is to know what each change actually does to your workflows on your data.

Eval Studio is available to FurtherAI customers starting today, and we're rolling it out across underwriting, claims, and program management teams. If you want to see Eval Studio running against your own pipeline, get in touch.

DISCLAIMER 

This article is for general informational purposes only and does not constitute legal, regulatory, compliance, underwriting, or other professional advice. The content reflects information available as of the date of publication, and FurtherAI undertakes no obligation to update it as laws, regulations, or AI technologies evolve. 

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