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Is this your company? Claim itWe apply machine learning and generative models to clinical development tasks such as data cleaning, patient review and statistical programming for drug sponsors.
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We keep every new laptop and account within policy, but proving it at audit time still eats weeks. Our small IT group gathers proof by hand across Linux and Mac machines. A good solution must treat Linux as a first-class citizen, tie device inventory to audit evidence automatically, and plug into our ticketing and code hosting.
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.
Large enterprise buyers expect a current security attestation, and refreshing it every year still eats far too much of our small team's time during rapid migration demand. A good solution must collect evidence automatically across more than one public cloud, stay affordable at our size, and avoid pulling engineers off product work. Trial first, please.
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 hold highly sensitive vulnerability findings for large clients, and retention and handling controls have to be airtight. Today we rely on contracts and internal oversight, which feels thin. A good solution must segregate data strictly per client, enforce retention and deletion rules, keep an audit trail, and let us keep full control of where findings live.
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 collect device telemetry from a very large consumer base, and new privacy rules now require us to prove consent and enforce retention. Today legal and security staff handle this by hand, without proper tooling. A good solution must track consent per device, automate deletion schedules, produce audit evidence, and come with a local implementation partner.
Larger customers keep asking whether we hold a formal security certification, and today we answer with hosting commitments and local-market assurances instead of a broad attestation. We need a way to prepare for an audit without drowning a small engineering team. A good solution must keep evidence collection mostly automatic and keep all data in European data centres.
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?
Customers hand us real conversation logs so we can test their chatbots, and those logs hold personal data. We need a lightweight way to control where it lives, who can open it, and when it is purged for good. It must host inside the European Union, set up fast without an admin, and fit a small team's budget.
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