AI in Fintech: Startup House Projects, Lessons, and Outcomes

Key facts at a glance

• Fintech track record: partnership with Siemens Financial Services Poland since 2016; a cyber risk platform for finance-sector and private equity clients since 2020; 4+ years of team augmentation for climate fintech CHOOOSE
• Automation outcome: leasing applications in about 2 minutes, automated credit decisions in about 7 minutes, 24/7
• Growth outcome: 150% revenue growth and 225% more B2B clients within a year of a platform launch
• Compliance basis: ISO 27001 compliant, development mapped to clients' DORA requirements, sensitive data on dedicated GCP clusters
• AI approach: RAG-grounded systems answering only from verified client data, with traceable sources and no training on client data

Fintech is where "move fast" meets "prove it"

Financial software has a specific tension. Customers expect consumer-grade speed. Regulators expect bank-grade control. Most fintech failures happen when a team optimizes for one side and treats the other as paperwork.

Our position, built over a decade of fintech projects: compliance is an engineering discipline, not a legal review at the end. Systems are designed "secure by design", with code quality reviews and cloud monitoring integrated so issues are resolved before they reach users. Here is what that looked like in three projects, and what we learned.

Project 1: Siemens Financial Services, automation as the product

Since 2016, we have built three cloud products for Siemens Financial Services Poland, integrated with Siemens's corporate systems and external providers. The flagship, SimplyLease Online, turned leasing into a self-service flow: an application that takes about 2 minutes and an automated credit decision delivered in about 7 minutes, available 24/7. SimplyLease won the Polish E-commerce Award 2017.

The lesson: the decision engine is the product. Digitizing the application form changes nothing if a human still reviews every case during office hours. Automating the decision, within Siemens Group security standards, is what removed the bottleneck. This is the pattern behind most successful AI and automation work in finance: find the step where the process waits for a person, and engineer that step.

Read the case study

Project 2: a cyber risk platform for Fortune 500 and private equity

Since July 2020, we have partnered with a US company (under NDA) whose platform supports cyber risk decisions for C-level executives, Fortune 500 companies, and private equity firms. Risk quantification sits close to finance: the platform translates complex mathematical models into decisions boards can act on, aligned with NIST and CIS frameworks.

We started with a 2-month Product Discovery, then took the product from prototype through MVP to scaled SaaS. Sensitive data runs on a dedicated GCP cluster in a location chosen by the client. The application responds in milliseconds.

The outcomes: 150% revenue growth within a year of implementation, 225% growth in B2B clients including Fortune 500 companies, and expansion into a newly identified private equity segment.

In 2025, the same platform got an embedded AI agent built by our team, which cut client onboarding by 95%, from 45 minutes to under 2 minutes. The agent runs on LangGraph with Anthropic's Claude models, with tenant isolation designed so the model cannot access data outside its boundary, and business unit names pseudonymized before any message reaches the model.

The lesson: in regulated, data-heavy categories, the AI challenge is not intelligence. It is boundaries. An agent for financial data must be architecturally unable to leak across tenants, not just instructed not to.

Read the case study

Project 3: CHOOOSE, scaling a climate fintech team in 2 weeks

CHOOOSE, an Oslo-based climate tech and climate fintech leader, needed to scale fast after its seed round. We built the team in 2 weeks: design, QA, frontend, and backend specialists, matched for culture and competence. The collaboration ran for more than 4 years as a flexible staff augmentation partnership, supporting a carbon offset platform used in climate programs of leading travel and transportation brands, and delivering significant operational cost savings for the client.

The lesson: fintech velocity is often a talent problem. A financing product with a strong model and no engineers to ship it loses to a competitor with average ideas and a full team. Speed of team assembly is a fintech capability in its own right.

Read the case study

What AI in fintech looks like when it has to hold up

Across these projects and our AI product work, five rules keep repeating:

1. Ground every answer in verified data. Our AI products use RAG architecture: the system answers only from the client's approved data, and every answer is traceable to a source. In finance, an untraceable answer is a liability, not a feature.

2. Keep client data out of model training. Client and customer data is never used to train public or shared models. Processing happens within the client's secure environment, under existing access policies and SSO.

3. Design tenant isolation into the architecture. Access boundaries enforced at tool creation time cannot be bypassed by prompt injection. Downstream filters can.

4. Meet the regulators where they are. We are ISO 27001 compliant and map development to our clients' DORA requirements. For sensitive financial data, we set up dedicated GCP clusters in client-preferred locations and orchestrate processes with minimal data storage to reduce risk.

5. Integrate, don't replace. Our API-first architecture connects to existing IT landscapes, third-party providers, and ecosystems like Google and Microsoft. Most financial institutions do not need a new core. They need an intelligent layer on top of the one that works.

Our AI toolkit for finance

Beyond custom builds, three Startup House products apply directly to financial operations:

• InProduct AI, a compliance-aware copilot that integrates with your codebase, keeps documentation in sync, and resolves user issues in context.
• SmartSearch, hallucination-free financial search that understands business intent and returns proof-of-fact verification for every result.
• KnowHub, a secure knowledge portal that turns scattered procedures, requirements, and internal policies into a single source of truth.

About Startup House

Startup House is a 50-person software development company based in Warsaw, Poland, established in 2016, ISO 27001 compliant, with 100+ products shipped for clients on 5 continents. As co-founder Alexander Stasiak puts it: "We don't just build software; we solve business problems with transparent processes and shared ownership."

Planning AI in a financial product? Book a 30-min call.

FAQ

What fintech projects has Startup House delivered?

Startup House has built cloud financing platforms for Siemens Financial Services Poland (including SimplyLease Online, with 2-minute applications and automated 24/7 credit decisions), scaled a cyber risk platform serving Fortune 500 and private equity clients (150% revenue growth in year one), and supported climate fintech CHOOOSE for over 4 years in a team augmentation model.

How does Startup House handle sensitive financial data?

Sensitive data runs on dedicated GCP clusters in client-preferred locations, with processes orchestrated to minimize data storage. Startup House is ISO 27001 compliant and maps development to clients' DORA requirements.

Can Startup House integrate AI with legacy banking or financial systems?

Yes. An API-first architecture enables controlled integrations with existing IT landscapes, third-party providers, and ecosystems like Google and Microsoft. For legacy systems, we build a conversational AI layer on top, so the core logic stays intact.

How does Startup House prevent AI hallucinations in financial applications?

All AI products use Retrieval-Augmented Generation: answers come only from the client's verified data, with proof-of-fact traceability. Client data is never used to train models, and tenant isolation is enforced architecturally.

How fast can a fintech product reach the market?

Through Product Discovery, features are prioritized to launch an MVP or demo version quickly while maintaining delivery quality. AI products can deploy in as little as 2 weeks once data is prepared; discovery for complex platforms typically runs 4 to 8 weeks.