deeplinq deploys on your infrastructure, connects your legacy systems, and leaves your data, your models, and your audit trail inside your control.
deeplinq is in private preview with selected financial institutions across EMEA. Onboarding additional banking design partners through Q3 2026.
A single boundary around the regulated organization. Data, orchestration, models, and governance live inside it. Sources feed in from the left; external providers connect inward from the right through scoped residency channels. Nothing crosses the boundary without being logged, evaluated, and policy-checked.
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A single, customer-controlled boundary. Data, models, orchestration, and governance live inside.
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Scoped, region-locked egress to external providers. Prompts and completions are not retained.
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Orchestration enforces policy, retrieval, and inference across every model and source.
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Every request produces an immutable, exportable record for compliance and audit requirements.
One layered system, one perimeter. Connectors ingest, RAG engine indexes, LLM router orchestrates, agents execute — all governed end-to-end inside your perimeter.
Regulated institutions cannot ship data to a vendor's cloud. FINMA, CCAF, and nLPD enforce Swiss residency and data protection. CSSF, Bank Al-Maghrib (Loi 09-08), and PDPL frame customer-data processing across Luxembourg, Morocco, and the UAE. HIPAA and GDPR health provisions define clinical-system egress. GxP traces pharma decisions. Solvency II, MAR, and MiFID II dictate what an auditor reconstructs.
Not edge cases. The operating envelope.
Most AI platforms were built for a different customer — one accepting hyperscaler tenancy and data paths the regulator never signed off on. deeplinq was built for the customer who cannot.
Your orchestration, connectors, agents, model routing, and audit trail sit inside the environment you already defend. The platform fits the envelope — not the reverse.
Each surface has its own regulator, its own audit cadence, its own use cases. One platform adapts to all.
Client files and KYC reviews generate more documentation than any team can read — and secret bancaire leaves no room for external processing.
Platform infrastructure where your team builds client-file and KYC-review agents. They run inside the bank's perimeter and leave a compliance-traceable record.
KYC, credit ops, customer engagement, and transaction monitoring are measured against central banks, BAM, PDPL, MAR, and MiFID II scope.
Platform layer across onboarding, credit files, customer correspondence, and market surveillance — where your team deploys AI with national hosting and audit-grade traceability.
GxP compliance, clinical data handling, and regulatory submissions demand audit traceability generic AI platforms cannot produce after the fact.
Pharma & Life Sciences workflows detailed today — research, quality, regulatory affairs, pharmacovigilance, supplier archive intelligence — with model-version pinning, prompt archival, decision traces for GxP scrutiny. Healthcare-large coverage (HIPAA, EHR, PHI, hospitals, medical devices) being expanded.
National sovereignty, procurement-compliance frameworks, and citizen-data protection make hyperscaler-backed AI non-starters at the procurement gate.
A sovereign AI layer fitting national data-protection regimes, with local hosting and the audit surface oversight bodies expect.
Subscriber data, your national data-protection regime, traffic-and-location confidentiality rules, and national retention directives frame every AI decision a converged operator makes. Customer-relationship and network-operations data cannot move to a vendor cloud — not by policy, by architecture.
Platform infrastructure for customer-care, network-operations, regulatory-DPO, and fraud-pattern workflows — deployed inside the operator's perimeter with model-version pinning, RBAC enforcement, and an evidence trail the regulator and DPO can review. Architected against ISO 27001 and ISO 27017 posture. Lawful-interception is out of scope and stays with the operator's dedicated LI system.
Operations, MRO, customer service, and crew workflows split across systems no off-the-shelf AI was built to fit.
Connectors across ARMS, AMOS, Sabre or Amadeus, Lufthansa Systems Lido, EFB, and FRMS. Agents surface ops, MRO, customer, and crew context inside the operator's perimeter — aligned with EASA, FAA, GCAA, and national CAA expectations. Defense and classified contexts out of scope.
The capability stack doesn't change for regulated institutions. The deployment envelope does.
Read documents, transactions, and system records at source. Extraction, embedding, and indexing stay inside the institution's environment. No training on customer data by default.
Reach into core banking systems, ERPs, CRMs, and operational databases through connectors that run where the data lives. No external data plane.
Route each query to the right model, knowledge source, and system of record. Every routing decision, retrieval, and model call is logged and auditable.
Deploy agents that propose, not decide, where the institution wants a human gate. Every action carries prompt, retrieval context, model version, and outcome — exportable for audit.
Enforce access controls at document, record, and field level. Retention, redaction, and residency through the platform — decisions versioned and reviewable.
deeplinq runs entirely inside the institution's data centre. Models, vector stores, orchestration, and logs live on infrastructure the CISO already controls.
Fit signal: private banking, healthcare with strict residency, public-sector.
deeplinq deploys inside a sovereign cloud region or private-tenant environment chosen by the institution. Residency and data paths defined by the institution's contract, not deeplinq.
Fit signal: commercial banking under central-bank obligations, sovereign-cloud healthcare.
No external network path. Inference, retrieval, and orchestration entirely on local infrastructure. Model updates and audit exports via controlled physical transfer.
Fit signal: classified public-sector workloads, high-security research.
Sensitive data and regulated workloads stay on-premise or on national cloud; non-sensitive workloads extend where the institution chooses. Sovereignty boundary enforced at the platform layer.
Fit signal: universal banks with mixed workloads, multinational healthcare, pharma combining internal and external research.
12 weeks, 4 phases, co-constructed. From customer-side preparation to in-production operations, every phase is mapped — including the prerequisites on your side.
A regulated institution that depends on a single model provider inherits that provider's roadmap, data posture, and regulatory exposure. "The vendor's model produced it" is not an answer a regulator accepts.
deeplinq treats model choice as a compliance decision. Institutions run proprietary frontier models, open-weights on local inference, or both — selected by task, sensitivity, and regulator expectation. Model versions are pinned so an auditor reconstructs a decision months later. Models replace without rewriting the platform.
Where a hyperscaler assistant or CRM-native copilot forces tenancy and a single provider, deeplinq keeps the model behind an interface the institution controls.
Currently supported
Cloud APIs (with data residency)
Open-weights (self-hosted)
Customer organization defines what's sensitive. deeplinq routes every workload to the right provider — Cloud APIs with residency controls for non-sensitive workloads, open-weights inside your perimeter for everything else.
9 providers across Cloud APIs and open-weights. Choose what fits your sovereignty posture.
For banking and government customers, the most demanding compliance configuration is also the simplest to explain.
On-premise deployment + locally-deployed open models = zero prompts leave the network edge, zero external LLM dependencies, zero cross-border transfers, zero third-party AI-provider DPAs to sign.
Not a claim. Not a promise. An architectural constraint enforced by your own infrastructure and validated by your CISO.
Customer perimeter
deeplinq platform
Open-weights LLM
Llama · Qwen · Mistral OS
No data egress. Not by policy. By architecture.
What deeplinq does for each framework today. Coverage-in-progress is marked explicitly. Nothing aspirational.
A regulator review is not won on claims. It is won on evidence. deeplinq treats the evidence layer as first-class.
Every prompt, retrieval, model call, and agent action is archived with full decision trace — inputs, retrieved context, model version, parameters, reasoning path where exposed, human approval status where a gate applies.
Access audited at user, role, and document level. Policy changes versioned. Model-version pins prevent silent substitution.
When a regulator asks for an evidence bundle, the institution exports a structured archive. No reconstruction project. No vendor intermediary.
The platform is horizontal, but banking density justifies a dedicated view — KYC, credit ops, market surveillance, customer engagement; integration footprint across Temenos, Finastra, Avaloq, Murex, SAP, Oracle.
If you are arriving from a banking mandate, the dedicated page goes deeper.
deeplinq is built on a four-layer architecture designed for sovereignty, auditability, and integration.
A working session with our team on your regulatory envelope, your deployment constraints, and the use cases that matter to your institution. Pragmatic, technical, short.