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Is this your company? Claim itWe make customer service inbox and knowledge base software for small and mid-sized companies, now shipping retrieval-based AI answers.
We ship AI replies that pull from each customer's own knowledge base, and right now our engineers judge draft quality by hand. It doesn't scale. We need a lightweight way to score retrieval and answer quality on the customer's own content before replies reach end users. No dedicated ML staff to run it, and pricing must be predictable.
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Our agents guide employees on benefits and file claims, so a wrong answer has real consequences. Every model change needs a repeatable check that accuracy held. We need evaluation sets we own and control, step-level tracing of what the agent did, and clear regression reports before release. Must pass a security review and never train on our customer 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 run a platform where brokers design and manage their own insurance products, so routine requests and policy changes keep piling up. We want AI helpers that handle the repetitive ones without adding headcount. A good solution must understand insurance regulation, plug into our policy system, keep humans in control of changes, and show clear pricing.
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?
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