AI Integration Services
Wiring AI into the product you already run – models, LLMs, APIs and agents integrated into your stack, with the monitoring and guardrails to operate them inside a regulated environment.
Results from work we have shipped
WislaSearch is an AI-powered search assistant that centralises fragmented data, indexes and retrieves the right information instantly, and keeps everything inside your own secure infrastructure.
AI we integrate
Connecting a model, a vendor API or an in-house service to your product, with the adapters, retries and error handling to run it in production.
Wiring a language model and a retrieval layer into your app so it answers over your own documents and data, not the open web.
Dropping an agent into an existing process, where it gathers inputs, calls your systems, applies your rules and escalates the edge cases to a person.
KYC, fraud, document and vision vendors integrated behind one interface, with fallback and vendor-swap designed in from the start.
The plumbing that feeds a model live data and carries its output back into the systems that act on it.
Rate limits, cost controls, logging, refusals and human-in-the-loop checkpoints, so an integrated model is safe to leave running.
Connecting AI to the older systems a bank actually runs, where a clean API rarely exists yet.
How we deliver AI integration
The same delivery discipline on every engagement - from the first map to a handover your team runs.
We map the systems, the data flow and the failure modes, then write a phased scope with the boundary decided up front.
We design the interfaces between your stack and the model or vendor, so either side can change without a rebuild.
We build the integration, add retries, guardrails and observability, and test it against the edge cases that break naive wiring.
Deployed in your cloud, monitored on latency, cost and quality, with a runbook handed to your team.
What shapes the work
Most AI does not fail on the model. It fails because the model never reaches the data or the workflow it was meant to change. An assistant that cannot reach the right records is a demo; a fraud model that cannot reach the payment stream is a research paper.
So the hard part of adding AI is rarely the AI. It is the integration: the contracts between systems, the live data, the error paths and the guardrails. That is the work this page is about, and it sits next to our API integration services and the applied modelling on AI development.
The data that makes an integrated model useful – your records, transactions and policies – is usually the data you cannot send to a public provider. We integrate for that constraint: self-hosted or private-endpoint models where capability allows, retrieval that keeps your data on your infrastructure, and contractual limits on any third-party endpoint. The full boundary approach is on the Data Science &
AI vendors change fast, and a model that is best today may be second-best or withdrawn next year. So we put a clean interface between your product and any model or vendor behind it, with fallback and a swap path built in. You get the capability without the lock-in, and swapping a provider becomes a configuration change rather than a rebuild.
AI integration rarely stands alone. If the goal is autonomous or assisted workflows, see AI agents development; if it is language and generation, see generative AI; if you need the model built as well as wired in, that is AI development. For the wider platform this connects to, see financial software development.
We prove the risky part first. A proof of concept on your own systems and anonymised data is fixed-price and time-boxed, answering one question: can the model reach the data and the workflow cleanly, inside your boundary. If it can, production follows as time and material or an outcome-based arrangement, with monitoring and guardrails built in from the first sprint.
What does AI integration include?
Connecting models, LLMs, APIs, agents and third-party AI services to your product and data, with the pipelines, monitoring and guardrails to run them in production.
Can you add AI to our existing product rather than rebuild it?
Yes. That is most of this work: wiring a model, API or agent into an app you already run, with the adapters, error handling and observability to operate it.
Do you integrate third-party AI vendors or only your own models?
Both. We integrate KYC, fraud, document and vision vendors behind one interface with fallback, and we integrate models we or your team have built. The interface is vendor-neutral so you can swap later.
How do you keep an integrated model compliant?
The boundary is decided at design time: self-hosted or private-endpoint models where capability allows, your data kept on your infrastructure, contractual limits on any third-party endpoint, and audit logging where the use case needs it.
How do you avoid AI vendor lock-in?
A clean interface sits between your product and any model or vendor, with fallback and a swap path, so changing provider is a configuration change, not a rebuild.
Who owns the integration afterwards?
You do - source, adapters, pipelines and documentation, running in your cloud from the start.
A private, on-premise WislaSearch deployment that turns a logistics operator's scattered operational knowledge into instant, cited answers - without anything leaving their network.
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An AI-driven solution that optimises medical sales representative routes and improves field efficiency, using deep learning over visit patterns to predict the best routes.
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WislaCode helped Verysell Group Applied AI Lab explore the current state and future of AI testing, turning uncertainty into clear, actionable insights.
View caseThis was a very task-heavy project, mostly exploration and R&D-driven. However, by the end of WislaCode, we were left with a detailed roadmap consisting of clear milestones - able to be converted into tangible KPIs - and some neat ideas of what actionable are next. Integrating...
We collaborated with WislaCode on a product strategy development project and gave the highest marks for this contractor. The WislaCode team delivered on time and with outstanding quality.
We collaborated with WislaCode on a route-to-market optimisation project. Working with WislaCode was effective, transparent and predictable, which is especially critical for AI and ML projects. We provided them with six months of anonymised data, and within just three weeks...
Have a model that needs wiring in?
Bring the capability you want to add and the systems it has to reach. We will scope an integration that proves the connection works, inside your boundary, fast.