Fintech · Banking & BaaS · 50–200 · Europe #b006_21

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.

We assemble our supervisory returns by hand, pulling data from several departments right before each statutory deadline. It is slow, error-prone and hard to audit. A good solution must pull from our existing systems automatically, keep figures consistent across reports, track deadlines, keep full lineage, and keep all data hosted inside the European Union.

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.

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 are formalising our compliance programme ahead of a bank charter, and regulatory reporting is the gap. Today it is scattered and manual. A good solution must track regulatory changes, map them to our controls, keep exam-ready evidence in one place, and feed our existing ticketing. Bank partners will scrutinise any vendor, so credibility matters.

We already passed one audit and now want an international certification next, but writing every policy from scratch is eating our small team's time. We need tooling that supplies solid policy templates, maps what we already have to the new standard, and keeps evidence collection light. Setup must suit a first-time IT lead and a modest budget.

Every resolved conversation by our AI agent adds model spend, yet our plans must stay cheap for small shops. We lack a clear view of cost per customer and per conversation. A good solution must track model usage by customer, flag unprofitable accounts, plug into our current stack simply, and avoid seat-based pricing.

We wish for a workspace that assembles government pricing submissions straight from governed source data, with every figure traceable back to the originating record. We would judge it by how many manual spreadsheet steps disappear, whether reviewers can replay any calculation, and whether a rule change updates drafts without rework. Deadlines never move, so reliability matters more than features.

We wish for a cost visibility tool that breaks cloud spend down by service, team and environment across containers, serverless and managed databases. It should flag anomalies early, send plain alerts, and plug into infrastructure as code. We would judge it by setup time under a day, no dedicated admin, startup-friendly pricing, and savings clearly exceeding its own cost.

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.

Our bank clients expect independent assurance on our controls, and every cycle we spend weeks chasing evidence across teams and systems. We need something that gathers proof continuously from our cloud environment and pipelines, maps it to control frameworks, and keeps an audit-ready trail. Light rollout, no on-premise installs, and priced for a small team.