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Is this your company? Claim itWe are a public surgical robotics company with installed systems in hospitals, plus cloud applications that collect and analyse procedure data.
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People move between our product teams, and each product keeps its own logins and permissions. Nobody reviews access the same way twice, so leavers and role changes slip through. A good solution must work across many separate products, give one central view for our risk team, support regular access reviews, and satisfy public-sector procurement rules.
Every core conversion leaves us asking one question: are balances and postings provably right before go-live? Today we lean on careful engineering and manual review, which is slow and leaves doubt. We need automated checks that compare source and target ledgers, flag mismatches early, and give auditable proof for the bank. Bank references required.
Our product usage data lands in scattered stores, so analysts and agents can't query one trustworthy dataset. We need a unified layer with clear lineage and freshness checks. It must be light enough for a small platform group to own, with no big ops team, plus support hours that match our region. We'd start with a team trial.
Findings from outside testers and our bounty programme arrive faster than we can sort them. Each one needs a clear owner, a severity call and a ticket in our tracker, and today that routing is mostly manual. A good solution deduplicates reports, assigns owners automatically, tracks fix deadlines, and shows evidence we can reuse in audits.
Serving speech and language models at national scale makes accelerator spend our biggest line item, and we can't tell which model or team drives it. A good solution must show cost per model, keep data on-site in region, fit our private cloud Kubernetes setup, and be trialable by a small team on a modest budget.
We ship investing features fast, and our backend engineers write and run tests themselves. As we grow, we worry a rushed release could break money movement. We need automated tests around transfers and account flows that plug into our existing pipeline, take little setup time, and fit a tiny budget. Heavy enterprise tooling is out.
We run heavy performance and correctness testing on our engine and its connectors, and much of it is still slow and partly manual. We need automation that handles large JVM codebases, runs well for remote engineers, and includes benchmark tooling we can repeat. Budget is modest, so pricing must stay small-team friendly. Tools built only for small codebases won't work for us.
We run claims operations reporting through analyst-built spreadsheets and dashboards, and by the time managers see them the picture is already stale. We need reporting that connects natively to our cloud data warehouse, lets operations staff drill down to individual claims, and uses clear per-member pricing. It must work alongside our existing claims vendor.
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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