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Is this your company? Claim itWe provide cloud accounting and business software for small and mid-sized businesses and accountants across several European countries.
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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.
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.
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 completed a type-two attestation, yet our public pages point to several other frameworks and we cannot tell which are truly current. Evidence lives in scattered places. We need one tool that maps each control across frameworks, refreshes evidence automatically from our cloud and pipelines, flags gaps before audits, and shows its price upfront.
We run many client environments at once, and keeping our security certification current means our quality staff chase evidence by hand across every project. It eats time before each audit. A good solution must collect evidence automatically across separate client cloud accounts, fit a services delivery model, and not tie us to one cloud.
We wish for one evidence workspace where a single control artifact is collected once and reused across every assurance report we face each year. We would judge it by fewer repeat requests to our teams, clean mapping between frameworks, an auditor-friendly trail, and support in our time zones. Partner-facing evidence sharing would be a strong plus.
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 wish for evaluation tooling built for regulated advice. It would score our model's explanations of portfolio decisions against clear criteria, flag unsupported claims, and route doubtful outputs to a human reviewer with an audit trail. We would judge it by how fast a small team can trial it, how well it catches errors staff review finds, and whether it never trains on our data.
We serve investment firms whose security questionnaires keep growing, and every audit cycle eats far too much of our team's time. We want evidence collection and control monitoring automated, with clear audit logs, financial services grade security, and responsive support. Our budget is modest, so pricing must fit a small team.
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