AI integration services

Connecting models, LLMs, APIs, agents and third-party AI services to a product you already run, together with the data pipelines, monitoring and guardrails needed to operate them in production. The integration work is usually larger than the model work: authentication, error handling, rate and spend limits, fallbacks, and the audit trail a regulated financial operation has to produce afterwards. WislaCode builds that layer for banks and fintechs, so the AI ships inside the product instead of beside it.

Yes, and that is most of this work. We wire a model, an API or an agent into an application or core system you already run, using the interfaces that exist rather than requiring the modern API you do not have. Adapters, error handling and observability come with it, so the new capability is operable from the first release and not after a second project.

Both, behind one vendor-neutral interface with a fallback path. For $lana (Monetech) we integrated two KYC providers with facial and document recognition behind a single interface, so the product did not have to change when the provider did. The same pattern covers fraud, document and vision vendors as well as models your own team has trained.

The boundary is decided at design time: self-hosted or private-endpoint models where capability allows, your data kept on your own infrastructure, contractual limits on any third-party endpoint, and audit logging where the use case requires it. For EU institutions a dated obligation now sits on top of that. Article 50 of the EU AI Act, Regulation (EU) 2024/1689, has applied since 2 August 2026 and requires people to be told when they are interacting with an AI system, a duty the Digital Omnibus on AI, Regulation (EU) 2026/1744, did not defer when it moved the Annex III high-risk obligations to 2 December 2027. Deciding where a model runs and what it discloses before the build is cheaper than retrofitting both.

An off-the-shelf service is often right for a general task such as summarising a document or classifying free text, and we will say so. A regulated decision is different: underwriting, fraud scoring or a KYC check usually needs a model scoped to your data and your rules, with the explanation and the audit trail a supervisor expects, which a packaged product rarely exposes. The practical test is whether you could reconstruct and defend one specific decision six months later, and where you cannot, integration alone will not fix it.

Ask where the model runs, who holds the data and what is logged on each call, before asking about model quality. Then ask to see the integration itself instead of a demo: the adapters, the retry and fallback behaviour, the spend and rate limits, and how a provider's breaking change is contained to one place. A team that can answer those inside your own repositories and your own cloud is doing integration work; a team that can only show you a model is doing something narrower.

A clean interface sits between your product and any model or vendor, with a fallback and a documented swap path, so changing provider is a configuration change instead of a rebuild. In the EU that matters more than it used to. Under DORA, Regulation (EU) 2022/2554, every model API a financial entity calls is an ICT third-party arrangement recorded in the Article 28(3) register of information, and Articles 28 to 30 require an exit strategy that is documented and tested. We build the swap path because you will be asked to evidence it.

You do. The source, adapters, pipelines and documentation are yours, running in your own cloud from the start and handed over with a runbook. No component depends on us to keep running, and no model provider is one you cannot leave.