What is hybrid AI for financial services?
Hybrid AI uses different models for different tasks. In Argivio, anything involving client, transaction or risk data stays on private, open-source models in your own cloud, with small models answering first and larger models when needed. Tasks with no client data, such as tracking new regulation or summarising market research, can use managed models such as Claude, GPT or Grok.
Why not private AI for everything, or a managed tool for everything?
Private-only means your analysts miss out on the most capable models for public research. Managed-only sends client data to a vendor and bills every screened transaction by the token. Hybrid gives each task the right model: confidentiality where it matters, frontier capability where it helps, and the lower cost for each workload.
Can client data ever reach a managed model?
No. Every request passes a policy check before any model sees it. Anything involving client, account, transaction or risk-model data, or where the check is unsure, stays private. You decide which task types may use managed AI at all, and you can switch it off entirely.
Does Argivio replace our transaction monitoring or fraud engine?
No. Argivio works on top of the systems you already run. Your transaction monitoring, fraud and screening engines keep raising alerts; Argivio helps analysts triage them, assemble the case, draft the narrative and explain the outcome, grounded in your own policies and typologies.
Does Argivio make credit, fraud or AML decisions automatically?
No. Argivio is decision support with human oversight. An analyst, investigator or credit officer reviews every output and makes the decision. Your institution defines the intended use of each workflow.
What is private AI for banks and financial institutions?
In Argivio, private AI is the part that handles client data. The models run inside your own cloud account or data centre, under your control, instead of on a vendor’s servers. Argivio delivers it as an operated platform, so institutions get the benefits without building an AI team.
How is Argivio different from building financial AI ourselves?
Running AI on client data is far more than provisioning a server with a GPU. It needs infrastructure provisioning as code, model serving, retrieval over your own policies, staging environments, release engineering with evaluation gates and rollback, observability, autoscaling, backup and security policy, all kept running every day. Argivio delivers that complete stack, built on XePlatform, inside your own cloud account and operated for you, so a supervised pilot can start within weeks without new hires.
How does Argivio support GDPR, DORA and the EU AI Act?
Argivio is designed to support GDPR, DORA and EU AI Act obligations, as well as US expectations such as GLBA and model risk management guidance. Client data is processed only inside your own account, every request and approval is logged, and we provide documentation for your impact assessments, third-party risk register and model validation. Compliance itself depends on how your institution uses the platform.
Which AI models does Argivio use?
Open-source general and domain models run inside your account for all client-data work, alongside your own risk models. New models are adopted only after they pass evaluation on your own cases. Managed models such as Claude, GPT or Grok are used only for tasks with no client data.
Can Argivio run on-premise or in a private cloud?
Yes. Argivio runs on the major public clouds in the region you choose, in Europe or North America, or in your private cloud or data centre, built on open standards so you can move without rebuilding.
How much does hybrid AI for financial services cost?
A fixed platform subscription plus your own cloud costs, with no per-token fees for client-data work. Managed AI is billed per token only for the tasks you route to it, under a monthly cap. Small models first, capacity that follows your business hours and batch windows, and shared hardware keep the cost of each answer low, which matters when alerts scale with every transaction.
How long does a pilot take?
The environment is set up in hours and your policies loaded within days. A supervised pilot with your analysts typically runs about four weeks, while your compliance, model risk and security teams complete their review in parallel.
