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Is this your company? Claim itWe run a therapy practice with employed clinicians who see clients both in offices and over video, matched through our own intake process.
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We bill customers by usage across many different services, and getting invoices and revenue recognition right is harder than we expected. We are hiring in-house, but we want something that ingests metering data through an API, handles mixed pricing models, and gives finance clean, auditable numbers. No rigid annual seat contracts. Team trial first.
We keep opening engineering roles at several levels, and our hiring loop is slow and uneven. Our team already uses AI coding tools daily, yet our interviews barely reflect that. We need a fair, fast way to assess real work skills, ideally in a typed JavaScript stack, with small-team pricing and a trial first. No heavy contracts.
We wish for a scheduling tool built for freelance clinicians, not salaried rosters. It would match session demand to available people by language, specialty and time zone, then book automatically. We would judge it in a small team trial: fewer manual hand-offs, faster booking, and support for many languages.
Our treasury staff still match prefunded partner wallet balances against partner statements by hand in spreadsheets. Every partner formats statements differently and currencies vary widely. We want an automated matcher that ingests many statement formats, connects to our ledger through APIs, and explains unmatched items in plain terms so ops can resolve them fast.
Our usage-metered compute billing has outgrown spreadsheets. As contracts get bigger, invoicing errors and manual revenue schedules are becoming a real risk. A good solution must handle per-second consumption natively, produce clean revenue schedules, onboard fast without a long services project, and not assume seat-based pricing. We want this solved this year.
Our billing runs on metered consumption, and partner royalties flow out of that same usage. Every period we must reconcile each metered record to invoices and royalty payouts, then hand auditors clean evidence. Sampling won't do. A good fit reconciles every record at huge scale, keeps a full audit trail, and supports our controls testing.
We're growing fast and our engineering interview loops are stretched thin. Senior engineers are pulled off product work to run screens, and candidates wait too long between rounds. A good solution must cut interviewer load without lowering the bar, fit our existing applicant system, treat candidate data carefully, and work well for a remote-first team. We'd want to see proof it works.
We wish for a training workspace that replays real, anonymized alert sequences as drills, so new analysts practice triage the way our shifts actually run. We would judge it by how quickly juniors handle live alerts alone, how clean their escalations are, and whether pricing stays fair when staffing rotates around the clock.
We wish a tool existed that watches failed postings and cash application exceptions inside our customers' accounting systems, explains the cause in plain language, and proposes or applies the fix with approval. We would judge it by fewer manual fixes per week, faster time to clear exceptions, and native connectors to mid-market ERPs.
We struggle to hire scientists who also write production code, and the search drags across many countries with uneven results. Interviews are inconsistent, and strong candidates slip away while we wait. A good solution must assess scientific depth and coding skill together, work across regions, give us comparable evaluations, and move fast without lowering our bar.
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