Fintech Software Development Services

Engineering for systems that move money and watch markets

Financial software fails differently from other software. A dropped message in a consumer app is an annoyance; a dropped message in a payment flow is a reconciliation problem, and in a surveillance pipeline it is a missed alert that someone will eventually have to explain to a regulator. The engineering that follows from that is specific: idempotency instead of retries-and-hope, ordered and replayable event streams, audit trails that survive a migration, and latency budgets that hold under the load spikes that matter rather than the averages that look good in a report.

We build that class of system. Our work spans payment infrastructure, trading and market data, crypto and digital assets, and real-time market surveillance — on the backend, the data pipeline and the integrations, with the operational discipline these systems require after launch.

Where we work in fintech

Payment infrastructure

Transaction processing, ledgers, provider and scheme integrations, reconciliation, payouts and refunds. The hard parts are rarely the happy path: they are idempotent retries, exactly-once semantics where the network gives you at-most-once, partial failures across third parties, and a ledger that still balances after a bad deploy. Our founder is ex-Wise, where he worked as an engineer on the core payment platform team — that background shapes how we approach money movement.

Trading and market data

Ingesting market data at volume, normalising it across venues, and serving it to engines and interfaces without blowing the latency budget. This is where streaming architecture stops being a preference and becomes the only viable design: back-pressure, partitioning that matches the access pattern, and storage that answers time-range queries at speed.

Crypto and digital assets

Exchange and venue integrations, wallet and custody workflows, on-chain data ingestion, and reconciliation between on-chain and off-chain state. Crypto adds two engineering problems most fintech avoids: venues whose APIs differ in ways documentation does not admit, and a settlement layer with its own idea of finality.

Market surveillance and compliance tooling

We have delivered engineering for a platform that monitors regulated crypto markets for manipulative behaviour — the kind of system that consumes venue data continuously, evaluates detection logic against it, aggregates related signals into cases, and presents them to analysts with enough evidence attached to act on. Architecturally that means microservices around an event backbone, a store built for time-series queries, case aggregation on top, and multi-region production deployment. We describe this work without naming the client.

Numbers we can share: 20+ data sources, 20–80 events per second, production in the EU and the US, 99.99 % platform uptime, 2021–2026. Read the case study.

What we actually build

• High-throughput backends — Java, Kotlin and Spring Boot services designed for sustained load rather than benchmark numbers.
• Real-time data pipelines — Kafka and RabbitMQ event streams, with ordering, replay and back-pressure treated as requirements rather than tuning options.
• Time-series and analytical storage — PostgreSQL, TimescaleDB and InfluxDB for data whose primary axis is time, plus Redis where latency dominates.
• Integrations — payment providers, exchanges, market data feeds, KYC/AML vendors, ERP and CRM systems, and the internal services that never have documentation.
• Dashboards and analyst interfaces — the screens where alerts, cases and positions are actually worked, built for people who look at them for eight hours a day.
• AI where it earns its place — anomaly detection and pattern recognition on transaction and market data, as a layer on top of deterministic rules rather than a replacement for them.
• Platform and delivery — Docker, Kubernetes, CI/CD, observability and multi-region deployment, because a financial system that cannot be operated is not finished.

Payment integrations

We connect products to payment providers and keep the money side correct after launch. Providers we have worked with: Stripe, PayPal, Skrill, Wise and EveryPay, plus crypto-to-fiat flows.

• Card checkout and subscriptions, including usage-based billing.
• Payouts to users through PayPal, Skrill and Wise.
• Funds held until a deal completes.
• Webhooks and reconciliation of provider events against your own records.
• Crypto purchase and sale with withdrawal to fiat.

Example: for Modern Voice we built Stripe subscriptions with reconciliation of payment events.

Engineering constraints we design for

Correctness under partial failure

Distributed systems fail in fragments: one provider times out, one consumer lags, one region loses connectivity. In a financial system every one of those states must resolve to a correct, explainable outcome. We design with idempotency keys, transactional outboxes, reconciliation jobs and explicit compensation paths, so a partial failure produces a known state rather than a support ticket.

Auditability as an architectural property

"Why did the system do that?" is a question regulated businesses must answer months later, about a specific record, from evidence rather than memory. That is a design decision made early: immutable event logs, decisions stored with their inputs, and data retention that outlives the service that wrote it. Retrofitting it later is expensive and usually incomplete.

Latency where it is actually needed

Not every path needs to be fast, and treating them all as if they do wastes budget. We separate the hot path — the one a person or an engine waits on — from batch and analytical work, and set explicit budgets for each. Then we measure tails, not averages, because in market systems the tail is the behaviour users notice.

Security and data handling

Authentication and authorisation between services, encryption in transit and at rest, secret management, least-privilege access, and audit logging of privileged actions. For EU clients, we work inside the GDPR framework by default, including data residency where required.

How an engagement usually runs

1. Discovery. A short paid phase to map the domain, constraints, integration surface, data volumes and technical risks. The output is an architecture and a realistic estimate, not a proposal deck.
2. Architecture. Service boundaries, event model, storage choices, integration contracts and the failure modes each component must survive.
3. Build. A dedicated team of three to seven engineers working only on your product, in European hours, with a named technical lead.
4. Hardening. Load testing, failure injection, observability, runbooks and the operational work that decides whether the system survives its first bad day.
5. Run and evolve. Support, incident response and continued delivery — financial systems change with the market and the regulation around them.

Who works on this

• Petro Gordiievych (founder and architect). Ex-Wise, on the core payment platform team; designs the architecture of most of our products.
• Svitlana Spinatiieva (tech lead). Java and Kotlin, Spring, Kafka, ClickHouse and Snowflake.
• Ivan Butakov (senior backend engineer). High-load services in Java, Kotlin and Rust; Kafka, PostgreSQL, ClickHouse.

We are six people. The same engineers work on the surveillance platform, the payment integrations and our other products.

Why teams choose us for this work

• Domain experience that is not theoretical. Payment platform engineering and production market-surveillance work, not a fintech landing page written after one project.
• Small autonomous teams. Three to seven engineers dedicated to your product. No rotating bench, no split attention.
• Inside the EU. Tallinn, Estonia — EU legal and IP framework, European working hours, real daily overlap.
• One team across the stack. Backend, data, integrations, interfaces — and, when the product has a physical side, electronics and firmware from the same people.
• You own the output. Code, documentation and IP are yours by contract, with no proprietary lock-in.

Frequently asked questions

What does fintech software development include?

For most projects: backend services that process transactions or market data, a data pipeline that moves events reliably and in order, storage suited to time-series and audit requirements, integrations with providers and venues, interfaces for the people who operate the system, and the deployment and observability needed to run it in production.

Do you work with payment systems and money movement?

Yes — transaction processing, ledgers, provider integrations, reconciliation and payouts. Our founder is ex-Wise, where he was an engineer on the core payment platform team, and that experience informs how we design money-movement systems.

Can you build market surveillance or compliance tooling?

Yes. We have delivered engineering for a platform monitoring regulated crypto markets for manipulative behaviour, covering event-driven microservices, time-series storage, case aggregation and multi-region production deployment. We describe client work without naming clients unless they have agreed to be named.

Do you work with crypto and digital assets?

Yes: exchange and venue integrations, on-chain data ingestion, wallet and custody workflows, and reconciliation between on-chain and off-chain state.

Which technologies do you use for financial systems?

Predominantly Java, Kotlin and Spring Boot for services; Kafka and RabbitMQ for event streaming; PostgreSQL, TimescaleDB, InfluxDB and Redis for storage; Docker, Kubernetes and CI/CD for delivery. Stack choices follow the problem, not the other way round.

How do you handle compliance and audit requirements?

By treating auditability as an architectural property rather than a reporting feature: immutable event logs, decisions stored with the inputs that produced them, explicit retention policies, and access controls with audit logging. For EU clients we work within GDPR by default, including data residency where it is required.

How long does a fintech project take?

Discovery typically runs two to four weeks. A first production-ready service or pipeline usually lands in three to six months, depending on the integration surface and the regulatory requirements. We give a realistic figure after discovery, not before it.

Can you take over an existing system?

Yes. That usually starts with an audit: current architecture, data flows, failure modes and the risks that need attention first. Taking over a running financial system is a different job from building one, and we scope it as such.

Talk to an engineer about your system

Tell us what you are building, integrating or modernising — a payment flow, a market data pipeline, a surveillance platform or an existing system that has outgrown its architecture. You will get sharp questions, an honest read on the risks, and a realistic plan.