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.
DevTools · Developer Tooling · 11–50 · Europe #b014_24
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We make a desktop productivity launcher with paid AI features that route to several model providers. Our team is fully remote, works asynchronously and ships every week.
- Industry
- DevTools & Software · Developer Tooling
- Size
- 11–50 employees
- Region
- Europe
- Tools budget
- under $1K per month
- Needs
- LLM evaluation & testing. We switch between model providers often and lack a consistent way to score answer quality and cost before rollout.Today: small AI team ships fast model and billing changes; checks appear manual · Urgency: nowInternal AI agents & automation. We want to automate repetitive support and release chores with agents, but without adding meetings or heavy process.Today: staff build internal extensions for weekly updates and releases with shared chat tools · Urgency: this year
- Who decides
- cto · team trial
- Expects from suppliers
- Side-by-side model comparisons on our own prompts; Lightweight setup for an async team
- Not a fit
- Tools locked to one model vendor; Enterprise-only contracts
- Stack
- LLM APIs in production, CI/CD pipeline in place, ticketing / issue tracker
- Buying triggers
- AI adoption push, cost pressure
Needs
No threads yet.
Cooperation
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Every model update has to be proven again in very different care settings before it ships. Today our scientists check detection quality site by site, and comparing versions stays slow and hard to repeat. A good solution must handle edge-device sensing models, keep results traceable for audits, run on EU hosting, and suit a small team with a modest budget.
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.
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 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?