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Is this your company? Claim itWe make software that helps medicinal chemists prioritise and design compounds, offered both as hosted service and customer-installed product.
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We let our clients build AI health apps on our regulated platform, so guardrails cannot be one-size-fits-all. Each client needs its own configurable limits, and every setting must hold up in front of a regulator. A good solution gives per-tenant policy controls, audit evidence mapped to medical device standards, and proof it has worked in life sciences.
We're a small team and we enforce least-privilege access to customer records by hand. Periodic access reviews eat time every cycle and are easy to get wrong. A good solution runs reviews on a schedule, flags stale or excessive access, keeps clean evidence for auditors, and works for contractors without per-seat penalties. Setup in days, tiny budget.
We're growing fast, and so is our codebase and open source footprint. Our security engineers are drowning in raw scanner findings. We need code-level vulnerability scanning that triages by real exploitability, plugs into our CI pipeline through an API, stays quiet on noise, and never slows developer laptops. Budget is modest.
We are shipping assistant features that read payroll records, and we worry a model could leak or misuse customer data. Our threat modeling is still ad hoc, feature by feature. A good solution must enforce clear limits on what a model can see and do, log every access, and come with documented threat models for model-driven features.
We wish for a shared container platform built for mature application servers, not greenfield microservices. It should let many teams adopt it gradually while customer-hosted installs keep running. We would judge it on support for older server stacks, regional data residency, public sector references, and how little each team must rewrite to ship.
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're a small team running clusters, deployments and observability, and it eats time our product engineers should spend building. We want a leaner internal platform without hiring a dedicated platform group. A good solution must be light to operate, give clear audit logs, keep customer workloads isolated, and pass a security review.
Our platform runs across several clouds, and keeping security configuration consistent between them is mostly manual today. One engineer carries cloud hardening for a multi-tenant setup. We need ongoing checks that cover every cloud we use, flag drift early, and fit a small team. Single-cloud-only scanners won't work for us. Remote-friendly setup matters too.
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 run access across many segmented cloud accounts with single sign-on and role-based permissions, but periodic access reviews are still manual and slow. Drift between reviews goes unnoticed too long. A good solution must automate reviews across all accounts, flag drift quickly, and export audit trails for every admin action, because bank partners and examiners ask for proof constantly.
We train and serve our own language models, and compute is one of our biggest cost lines. Today we struggle to see spend per workload, so trimming it is guesswork. A good solution must show cost per workload, run on premises or in sovereign clouds, work without permanent internet access, avoid large minimum commitments, and fit a small monthly budget.
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