Fintech · Wealth & Investing · 50–200 · North America #b007_12

We wish for evaluation tooling built for regulated advice. It would score our model's explanations of portfolio decisions against clear criteria, flag unsupported claims, and route doubtful outputs to a human reviewer with an audit trail. We would judge it by how fast a small team can trial it, how well it catches errors staff review finds, and whether it never trains on our data.

Before every model change reaches partners, we must prove our symptom assessment still performs against clinical cases. Today we lean on synthetic cases and automated metrics, which feels thin. A good solution gives versioned, reproducible evaluation sets, clinician-reviewed scoring, regression comparison between model versions, and fits our release pipeline and quality agreements.

Every new customer means our analysts read long plan documents and re-key benefit details into structured data by hand. It slows onboarding and invites mistakes. A good solution must pull values from insurance plan summaries, flag low-confidence fields for human review, and validate against known plan rules. Plain OCR does not help us.

We wish for an evaluation workbench that reruns every model version against our full set of clinical findings and gives identical results each time, with locked data, code and settings. We would judge it by reproducibility across reruns, clear per-finding regression reports, audit trails fit for medical device reviewers, and how easily it plugs into our existing pipelines.

We publish claims about how coding agents behave on messy, real-world code, and today we rerun our benchmarks by hand each time a new model ships. It eats research time and results drift. We need repeatable runs we can trigger per model release, with comparable scores over time, light enough for a small team and modest budget.

Our claims and fraud handlers keep repeating the same manual lookups across systems, which eats time better spent on judgement. We want agents that automate those lookups safely, with clear audit trails, human approval on sensitive actions, and tight access controls. Insurance experience matters. Who has handled this well in regulated claims environments?