The AI layer built for banks that can't send their data anywhere.
Audit-ready agents for banks that answer to regulators, not vendors.
Built for the institutions where data sovereignty defines the build.
Three banking realities that break generic AI.
- 01
Your data cannot leave.
Core banking, client files, transaction histories, regulatory archives — the data your AI needs is what regulators say cannot move to a vendor cloud. Generic AI asks you to bypass that.
- 02
Your systems won't be replaced.
Your core banking platform, Murex, SAP, regulatory archives — not getting modernized in 18 months. AI requiring a clean data lake isn't AI for your bank.
- 03
Your regulators will ask.
DORA mandates resilience logs. MiFID II demands decision traceability. BAM and FINMA expect local residency. Every AI interaction needs to be loggable, sourceable, auditable — before deployment.
One layer. Your infrastructure. Every desk.
01
Ingest
your policies, circulars, procedures, product docs, call archives
02
Connect
your core banking, CRM, middleware, data warehouses, market data
03
Orchestrate
the LLMs you approve, governed by your compliance team
04
Deploy
agents scoped to a desk, a use case, a risk profile — with audit trails on every interaction
No data movement. No re-architecture. No data science team required.
Platform architecture
One layered system, one perimeter. Connectors ingest, RAG engine indexes, LLM router orchestrates, agents execute — all governed end-to-end inside your perimeter.
Banking workflows where deeplinq makes a measurable difference
Front-office
Relationship Manager briefings
One query assembles the 360° client view — holdings, transactions, interactions, documents, flags — from systems that never talked to each other. The relationship manager opens a single conversation; the agent reaches across core banking, CRM, document archives, and middleware, returning a sourced briefing with citations back to the originating record. Every retrieval is logged, every model decision is pinned to a version, and every export carries the access-control trace your compliance and audit functions expect.
Front-office
Next-best-action
For each client interaction, the agent surfaces products, actions, and risks grounded in actual client data — the client's holdings, recent transactions, suitability profile, and the bank's product catalogue read in context. Recommendations are sourced, not generative speculation: every suggestion cites the client record or product line that justifies it. The relationship manager keeps the call. The agent prepares the move.
Middle-office
Regulatory research
Ask across every circular, internal policy, regulatory archive, and external source the legal team already maintains. Sourced answers with citations back to circular, page, and clause — the model version that produced the answer pinned alongside the output, so the same query reconstructs the same reasoning twelve months later. Compliance and legal teams stay in their existing reading flow; the agent indexes what they already track and shortens the time from question to substantiated answer.
Middle-office
Real-time compliance checks
Run transactions, documents, or communications against your compliance rules in the moment they happen — every check returns a decision with the rule citation, the evidence retrieved, and the model version pinned to the output. Real-time means in-flow, not batch: alerts surface inside the workflow tools your operators already use, with the full chain of context an internal auditor or supervisory authority would expect to reconstruct. Your compliance officer stays the decision-maker. The agent does the look-ups.
Back-office
Reporting & reconciliation
Draft and match in one workflow. Agents reach across core banking, the data warehouse, and the document archive to draft regulatory reports, internal reports, and board packs from source data — with citations back to the figures, transactions, and documents that justify each line. The same agents reconcile cross-system breaks: identifying mismatches between subledger and ledger, between custody and trade-confirmation, between counterparty statement and internal record, and drafting the explanations the operations team would have written by hand. Your team reviews and signs. The agent surfaces the breaks, prepares the package, and logs every retrieval — model version pinned, audit trail intact. The work compresses, the accountability does not.
Back-office
Approval routing
Exceptions, overrides, and non-standard decisions routed automatically — each approver sees the case with the policy clauses that govern it, the precedent decisions on similar cases, and the supporting documents already attached. Loan exceptions, derivative trade overrides, capex requests outside the standard envelope: the routing agent assembles the context the approver would otherwise have to gather, applies the institution's existing approval matrix, and logs the decision trace your compliance and audit functions expect. The decision belongs to your approver. The work belongs to the agent.
This shortlist reflects the use cases most active in our current design partner deployments. Additional workflow families available on request.
We connect to the stack your bank actually runs.
deeplinq is built around the reality that banking stacks are heterogeneous, legacy-heavy, and mission-critical. Our connectors don't assume a greenfield environment.
Core banking & trading
- Temenos
- Finastra
- Murex
- Avaloq
ERP & finance
- SAP S/4HANA
- Oracle EBS
- Oracle Financials
CRM
- Salesforce Financial Services Cloud
- Microsoft Dynamics 365
Market data & research
- Bloomberg
- Refinitiv
- FactSet
- Internal research repositories
Productivity & communications
- Microsoft 365
- Google Workspace
- Slack
- Teams
- Outlook
Document management
- SharePoint
- Documentum
- Internal archives and regulatory repositories
Don't see your system?
deeplinq connects to any system exposing an API, database, or file share. Custom connector development is part of every banking deployment.
Built against the frameworks your regulators audit.
deeplinq's architecture is engineered around the regulatory frameworks that govern European and international banking. Not "enterprise-grade". Banking-grade.
- DORA
- Full operational resilience logging. AI system incidents recorded, classified, reportable under DORA timelines.
- MiFID II
- Complete decision traceability. Every AI-assisted recommendation logged with source chain.
- GDPR / equivalent
- No customer data leaves your control zone. Right-to-erasure respected across the AI layer. Same posture for nLPD (Switzerland), PDPL (UAE), Loi 09-08 (Morocco).
- EU AI Act
- Risk-classification-ready architecture. Transparency logs, human oversight controls, model governance.
- FINMA
- Architected for Swiss data residency. Local presence via our partner Brams in Geneva.
- ACPR / CNIL
- French banking and data protection alignment. Architecture reviewed against ACPR outsourcing guidance.
- Bank Al-Maghrib & PDPL
- Moroccan banking supervision (Loi 09-08), UAE data protection (Federal Law No. 45/2021) — addressed by national hosting, deployment locality, regional cloud options.
ISO 27001 and SOC 2 certification paths underway. Full roadmap under NDA.
What our design partners get that generic AI cannot provide.
Banking-first by design, not by marketing.
Banking-specific roadmap. Vocabulary, workflows, and compliance built around banking realities.
Data sovereignty without trade-offs.
On-premise, air-gapped, or your cloud. No exfiltration, telemetry leaks, or model-provider data sharing. Default, not option.
Deployment partnership, not license handoff.
Every engagement includes a dedicated deployment lead, change management support, direct access to our engineering team.
Co-construction of the category.
Design partners shape banking AI middleware that didn't exist 18 months ago. First-mover influence on product, pricing, roadmap.
Banking FAQ
Can deeplinq deploy inside our existing cloud (Azure / AWS / GCP)?
Yes. deeplinq deploys in any cloud where you have administrative control. Your account, VPC, security perimeter.
Do you support air-gapped environments?
Yes. For classified or restricted environments, deeplinq runs with zero outbound dependencies. Model inference on your on-premise infrastructure.
Which LLMs can we use?
Any of them. Cloud APIs with data residency: OpenAI, Anthropic, Mistral, Google. Open-weights for on-premise or air-gapped: Llama, Qwen, Mistral open, Gemma, Falcon. Plus your own fine-tuned models. deeplinq is the orchestration layer — model choice is yours, switchable.
How long does deployment typically take?
First productive use cases live in 4 to 8 weeks, depending on integration and compliance review. Some teams have first agents in two weeks.
Do we need a data science team?
No. deeplinq is built for business-line deployment. Compliance officers, operations managers, and RMs define and refine agents — IT involved only for integrations and permissions.
How do you handle model hallucinations and errors?
Source-linked answers by default. Every response ties back to a document, database record, or system query. Unsourced answers are flagged unverified.
What about Shadow AI — employees using ChatGPT with confidential data?
deeplinq gives your team an internal alternative without the compliance exposure. Enforcement follows a credible internal tool, not lock-down.
How does pricing work?
Enterprise pricing, scoped to deployment size, agents, and mode. We don't publish per-seat pricing — banking deployments are rarely structured that way.
deeplinq is built on a four-layer architecture designed for sovereignty, auditability, and integration.
A 30-minute conversation. No deck. No demo theater.
We start every banking engagement with a structured technical and regulatory discussion — not a pitch. We want to understand your constraints, stack, and risk posture first. The best outcome is an honest "here's whether deeplinq fits, and what we'd need to do together."