industries · Banking & Fintech
Run open models on EU infrastructure, on-premise if you need it, with zero logs. DORA, GDPR and the AI Act stop being a configuration project and get solved in the architecture itself.
compliance
Every regulatory demand mapped to a platform capability that ships built in, with nothing to configure.
DORA
Regulation (EU) 2022/2554 · applicable since 17 Jan 2025
RequiresDigital operational resilience and ICT third-party risk management: the AI provider is part of your operational risk, with audit and exit-strategy obligations.
HelmcodeAn auditable EU provider, dedicated or on-premise deployment, and a standard OpenAI-compatible API: your exit strategy is changing a URL, not rewriting the integration.
GDPR
Regulation (EU) 2016/679
RequiresMinimization, confidentiality and control over transfers of personal data, with the added scrutiny that financial data carries.
HelmcodeZero logs by architecture: prompts and responses are never stored and never train models; inference runs only on EU infrastructure, outside the reach of the Cloud Act.
AI Act
Regulation (EU) 2024/1689 · in force, applying in stages
RequiresTraceability, data governance and human oversight for high-risk systems, a category that includes creditworthiness assessment of natural persons (Annex III).
HelmcodeOpen models with published model cards and licenses: you document exactly which system you run, with which weights and where.
This page is an informational overview, not legal advice. For your obligations and the risk classification of each system, consult qualified legal counsel. AI Act Guide →
what the sector says
Santander AI Lab and Fundación General CSIC published a strategic surveillance report in July 2026 on where B2B AI is heading in Europe. Its conclusions are the argument this page makes, written by people who run a bank. We had no part in it and we are not a partner: it is public, and the link is below so you can check us.
01
The token hides the cost
Price per million is an incomplete way to decide an architecture: it compares vendors but says nothing about holding a process in production with millions of inferences, controls and monitoring. On a per-token API every increase in usage becomes variable OpEx tied to somebody else’s tariffs.
02
Sovereign RAG
Keep the language engine separate from the bank’s document memory, so policies, procedures and critical documentation stay under the institution’s direct control instead of ending up embedded in somebody else’s model. Open weights plus your own index is what makes that separation real.
03
Zero egress
Private repositories are not enough. Sensitive data has to stay inside authorised perimeters for the whole inference cycle, not only at rest. That is a property of where the model runs, which is why it cannot be bolted onto an API you do not control.
04
Compliance as the moat
The report reads the AI Act, DORA, GDPR and NIS2 as competitiveness factors rather than paperwork: whoever can show traceability, human oversight and auditable models is better placed than whoever cannot. Which is the same reason an opaque API is a liability here.
Santander AI Lab · Fundación General CSIC "La nueva frontera europea de la IA B2B", strategic surveillance report, July 2026. Built on an analysis of more than 10.3 million documents and 300,000 specialised publications. read the report →
use cases
The cases with the most traction in the sector, each with its own page in detail.
A starting point per task type. The full guide maps 80 cases to the open model for each one.
We are distilling and quantizing these open models into small, tightly specialised versions, trained for one task rather than for all of them. A model like that runs on less hardware, answers faster and fits where the big one does not, your own datacenter included. If you have a process with volume and stable criteria, that is the conversation we want to have with you.
// faq
What the sector's technical, compliance and business teams ask.
Yes, if the weights run on controlled infrastructure. The regulatory risk is sending data to a foreign API, not the origin of the model: with open weights run in the EU or on-premise, no data leaves your perimeter and the model is auditable end to end.
DORA (Regulation (EU) 2022/2554, applicable since 17 January 2025) treats ICT providers as part of the entity’s operational risk: it requires third-party risk management, audit capability and exit strategies. An open-model stack on EU infrastructure, with an on-premise option and a standard API, simplifies all three versus a closed foreign API.
Prompts and responses are not stored (zero logs by architecture) and are never used to train models. Inference runs only on EU infrastructure, with no transfers to third countries. For strict requirements, the same stack runs on a dedicated GPU or on-premise inside your own datacenter.
Creditworthiness assessment of natural persons is among the high-risk uses in the AI Act (Regulation (EU) 2024/1689, Annex III), with obligations of traceability, data governance and human oversight. The start date is the part in motion: the co-legislators agreed in May 2026 to move the Annex III obligations from August 2026 to December 2027, and as of July 2026 that is pending formal adoption. An open, auditable stack with published model cards and licenses makes documenting the system easier; classifying each specific case is a matter for your legal team.
Yes. On-premise deployment offers the same API and the same models with data never leaving your network, the usual option for core banking, fraud prevention and specially protected data.
The API is OpenAI-compatible: change the base URL and the key, and the SDKs, LangChain, LlamaIndex or your own pipeline keep working unchanged.
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