AI development services

At WislaCode, AI development means putting an AI capability into a financial product and the workflow around it: AI agents, computer vision for document and identity checks, NLP, credit and lending decisioning, anti-fraud and risk scoring, and KYC and identity, plus the integration that makes each one run in a live system. It is the applied layer, so where the real need is custom model training or MLOps we point to ML development, where it is LLMs, RAG or fine-tuning we point to generative AI development, and where it is autonomous agents we point to AI agents development. Many regulated projects use several of these together, and we route you to the right service rather than overclaiming on this page.

At WislaCode the compliance boundary is decided at the design step rather than bolted on later: self-hosted or private-endpoint models where capability allows, data kept on your own infrastructure, and audit logging where the use case requires it. A named lead defines where the AI runs, its boundary and how it is logged, so a bank or fintech gets an AI feature it can put in front of an auditor as readily as in front of a customer. Deciding the boundary first means the AI never becomes a data-governance gap you have to explain after the fact.

Yes. A large share of WislaCode's applied-AI work is exactly that: wiring a model, an API or an agent into an app or core system you already run, with the monitoring and guardrails to operate it in production. That covers modernising a legacy platform, adding an AI capability to a web or mobile banking app, or integrating machine learning into an existing workflow without replacing the stack around it, so the AI ships as part of the product rather than as a side system.

An off-the-shelf tool is often the right call for a general task, but a regulated decision usually needs a model scoped to your data, your rules and the audit trail a supervisor expects, which a packaged product rarely exposes. WislaCode builds to the specific decision you need to change, whether that is underwriting, fraud scoring or a KYC check, and integrates it into your stack rather than bending your process around a fixed tool. The result is owned by you and runs in your own cloud, so you are not tied to a vendor's roadmap.

WislaCode picks model classes and logging patterns that produce explanations by default, sets up challenger models where a regulator expects them, and designs override paths so a person can intervene with a full audit trail. In a regulated operation a decision the model cannot explain is a decision you cannot ship, so explainability is designed in from the first sprint rather than reported at the end. Every decision stays reproducible after the fact, which is what fraud, credit and compliance teams need to act on an alert or defend an outcome.

WislaCode runs a complex, compliance-heavy build in four phases: first we map the use case, usable data and governance constraints into a phased scope, then a named lead fixes where the AI runs and how it is logged and explained. From there we build and integrate with override and challenger paths, then deploy in your cloud with monitoring on its own targets and a runbook for your team, so governance is in place from the first sprint rather than retro-fitted at the end. Scope starts small and phased, so a transformation programme for a bank or fintech shows working software early instead of committing to a large build up front.

Look for a team that treats governance as part of the architecture, integrates into your existing stack rather than handing over a model in isolation, and can show explainability, override paths and an audit trail for every decision. In regulated finance the harder part is rarely the model; it is reaching the data and workflow the model was meant to change while staying inside your compliance boundary. WislaCode builds applied AI for banks and fintechs from offices in Warsaw and Dubai, scoped to a real decision, run in your own cloud and handed to your team with a runbook.

WislaCode starts with a fixed-price, time-boxed proof of concept on your own anonymised data, scoped to one regulated decision such as a fraud, credit or KYC check, that answers a single question: does the model clear the bar your use case needs. As one worked example of that approach, a route-optimisation proof of concept ran three weeks on six months of anonymised data and showed 30% less travel time and 20% more visits per day, before any full build. Production for a bank or fintech then follows as time and material or an outcome-based arrangement, with governance and monitoring built in from the first sprint.

You do. With WislaCode the source, data pipelines, model artefacts and documentation are yours, running in your own cloud from the start. The system is deployed into your infrastructure, monitored against its own targets and handed over with a runbook, so a regulated financial operation keeps full control with no black box it cannot inspect and no lock-in on the models behind its decisions.