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Is this your company? Claim itWe design inference accelerator silicon and its full software stack, with hardware and software engineers working together across offices on several continents.
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We ship many credit products in parallel, and our regression coverage cannot keep pace. Releases slow down or carry risk. A good solution must plug into our existing pipeline, cover lending flows like applications, servicing and payments, let QA teams across regions author tests without heavy engineering help, and pass a security review.
Since moving to the cloud, our hosting bill has become something engineering leads review by hand, with no dedicated tool. As usage grows we want it to stay predictable. A good solution gives cost visibility per service, fits a mature monolithic web app, and lets one team trial it without a long rollout. Not interested in tools that assume a microservice estate.
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.
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.
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.
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 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 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.
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