Deeplinq for Banking
The AI layer built for banks that can't send their data anywhere.
A platform built around banking realities. Connectors to your core systems. Agents scoped by desk running inside your perimeter. Every prompt, document, and inference stays where your regulators require. Architected for DORA, MiFID II, FINMA, and ACPR. Deployed in days, not quarters.
In private preview with selected Tier-1 and Tier-2 banks across EMEA.
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.
Where banking teams build first
Fifteen banking workflows your team builds on the platform. Your team defines the logic; deeplinq provides connectors, orchestration, and audit trail.
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.
Client onboarding & KYC
Draft onboarding documents, verify KYC against internal databases and external registries, route exceptions to compliance.
Next-best-action
For each client interaction, the agent surfaces products, actions, and risks grounded in actual client data.
Product research
Ask a natural-language question about any product, get answers grounded in your catalog, pricing, regulatory constraints.
Meeting preparation
Five minutes before the call: the agent reads six months of interactions, positions, pending items, market context — and briefs the RM.
Middle-office
Regulatory research
Ask across every circular, internal policy, regulatory archive, external source. Sourced answers with circular and page references.
Real-time compliance checks
Run transactions, documents, or communications against your compliance rules — audit trail on every decision.
AML investigation assistance
Surface patterns across transactions, counterparties, and communications that would take hours manually.
Risk signal detection
Continuous monitoring of portfolios, exposures, external data — anomalies flagged to risk teams in real time.
Audit response
Regulators ask, the agent assembles: every decision, log, interaction — with full source chain.
Back-office
Reconciliation
Cross-system agents that identify breaks, draft explanations, route exceptions.
Report drafting
Regulatory reports, internal reports, board packs — drafted from source data, reviewed by your team.
Approval routing
Exceptions, overrides, non-standard decisions routed automatically with the right context for each approver.
Document processing
Contracts, custody statements, trade confirmations — extracted, validated, routed, archived.
Operational Q&A
The internal help desk that never sleeps — policy questions, procedure lookups, system status.
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
- No customer data leaves your perimeter. Right-to-be-forgotten respected across the AI layer.
- EU AI Act
- Risk-classification-ready architecture. Transparency logs, human oversight controls, model governance.
- FINMA
- Architected for Swiss data residency. Partner office in Geneva.
- ACPR / CNIL
- French banking and data protection alignment. Architecture reviewed against ACPR outsourcing guidance.
- Additional regional
- BAM (Morocco), Gulf financial authorities — on request.
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.
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."