Data, AI and Right Cloud for enterprises

Retail investors drown in noise and act on impulse; AI investment tools answer with demos that were never trusted with real money. The interesting problem was not the model — it was the operating loop: who decides, who executes, and who gets graded.

So Judex was built for the hardest first customer — myself — in production from the first week, with every design decision made next to the problem: a two-minute daily brief, a one-tap approval, a ledger that remembers every verdict. Once the loop held up under real use, it was made multi-tenant and opened to family and friends.

Solution:

• Two decision engines on a Databricks medallion lakehouse: a Control Tower defending holdings via a graded risk ladder, and an Opportunity Finder that qualifies on strength and buys on weakness.
• Strict division of labour: rules decide, AI only explains — the LLM writes readable reasons, never picks the trade; missing data is excluded, never guessed.
• Human-in-command execution: email brief → one-tap approval → broker execution on a dedicated agent account; the main account is record-only.
• Full audit spine: every recommendation carries an id and a rules-version hash; a ledger grades system calls and user verdicts, including the what-if for declined advice.
• Multi-tenant with a 24-check tenant-isolation suite run against the live system after every relevant deployment.
• Operated, not just shipped: versioned rule revisions from live evidence, cost-tuned across three clouds.

Impact: In daily production since mid-2026 on real portfolios; every decision — machine and human — traceable end to end; rule changes shipped from field evidence without rewriting history.

Stack: Databricks (Delta, Unity Catalog) on Azure, Windmill, Next.js, PostgreSQL, OCI, Terraform, eToro API, LLM explanations.

Artifacts: Published method statements, a dated end-to-end case file, decision-trace portal, monthly scorecard. judex.simplificare.ch (pre-launch).

The existing SaaS platform was hosted entirely on a private cloud, requiring the integration of complex stacks — from servers to switches, routers, operating systems, and databases — in collaboration with multiple third-party providers.

The scale was immense: 1,700 databases, 30,000 virtual servers, and 5,000 physical servers. The monolithic Oracle-based Core Banking Suite spanned multiple banking domains, with business logic deeply embedded and tightly coupled to the data layer. Several attempts to re-platform the data stack to cloud DBaaS had failed due to technical dependencies and economic constraints.

Systems of Engagement for business services were layered on top via APIs and UI services, but lacked portability and flexibility to adopt hybrid or multi-cloud strategies.

Solution:

• Design data layer on Oracle Cloud’s Dedicated Region Cloud@Customer (DRCC) to maintain regulatory and performance compliance while introducing cloud-native capabilities.
• Migrated container-based Systems of Engagement to each client’s preferred hyperscaler (Azure, AWS, OCI, or GCP).
• Defined hybrid blueprints to optimize workload placement, service selection, and Infrastructure-as-Code strategies.
• Designed and implemented Low Code Cloud Topology Automation Orchestrator for IaC authoring.
• Established a Unified Data Model to abstract cloud-specific service variations, enabling standardized, modular provisioning.
• End-to-end Azure Data solution ingesting on-prem Oracle Exadata data via ADF and GoldenGate into ADLS, transforming it with Databricks Delta Live Tables, and delivering governed analytics and ML insights through Power BI and Feature Store

Impact: Enabled consistent, automated, and secure cloud provisioning for 25+ banking clients managing over $4T AUM; reduced provisioning time from weeks to hours; improved compliance posture; and standardized hybrid cloud deployment patterns.

Stack: CAF, Python, Cloud SDKs (Azure, AWS, OCI, GCP), Low-code Windmill Platform, Terraform, Azure Data Factory, Datalake Gen2, Databricks, Power BI, REST, GraphQL, Oracle Exadata, Oracle GoldenGate, Kubernetes.

Artifacts: Hybrid Cloud Blueprint, Infrastructure-as-Code libraries, Cloud SDK provisioning framework, Data ingestion pipelines, Workload migration plan, Operational runbooks.

The existing settlement functions were fragmented across multiple homegrown legacy applications and COTS applications, each catering to asset classes like Forex, Precious Metal, and Interest Rate Derivatives, with overlapping functionality and high maintenance costs.

Regulatory initiatives such as PRIIPs and MiFID were adding further complexity by requiring changes across multiple settlement engines. The platform lacked consistent DevOps practices, which slowed delivery and complicated testing and deployment.

Solution: Designed and oversaw the implementation of the OTC Factory program — a central, standardized streaming-based Settlement Engine across multiple derivatives asset classes. Led integration and service interface management across ERP, CRM, Finance, Booking, Payments, SWIFT, and ECM domains. Built the core of a strategic confirmation platform with long-running production stability.

• Data modelling with the central architecture for Settlement domain, wrapping the Cash Record and Stock Record.
• Streaming adoption (Kafka), Spring Boot integration, and global service interface management.
• Toolchain selection, DevOps adoption, and lifecycle management from adoption to retirement.
• Executed a structured decommissioning program of Settlements, Payment and Recon Engines to reduce cost and complexity.
• Introduced Perforce Delphix Data Virtualization tool, reducing data footprint and refresh time from PROD to UAT to DEV and TEST.

Impact: Lower run costs, faster environment refresh, improved resilience; enabled regulatory compliance at scale.

Stack: Kafka, Spring Boot, Java, Oracle, REST/JSON, CI/CD, DevOps practices.

Artifacts: Architecture patterns, Business Capability Map, proof-of-concept results, decommissioning roadmap, interface catalog.

Asset Management and Wealth Management operated multiple fragmented confirmation and reporting processes, with data scattered across trade capture systems, mainframe-based ERP/CRM, and downstream reporting tools.

Regulatory mandates such as EMIR and FINREG required timely, accurate, and auditable confirmations and trade reporting. The absence of a centralized confirmation data platform led to duplicated workflows, inconsistent data quality, and higher operational costs.

Solution: Built a distributed, regulatory-compliant Confirmation Data Platform with an end-to-end trade capture pipeline (Wealth & Asset Management → master data from ERP/CRM → ECM, client, and regulatory reporting). Delivered EMIR/FINREG compliance, sustainable platform design, and acted as the single global contact for confirmation management. Integrated Confirmation function to BlackRock Aladdin ($22B AUM)

• Centralized OTC and derivative confirmation and reporting into a unified platform for trade reporting, unauthorized trading detection, and MIS.
• Designed high-availability architecture with J2EE, EJB, Oracle Cluster, and IBM MQ for reliable, scalable processing.
• Implemented modern analytics and reporting pipelines using Apache Spark, Apache Iceberg, ClickHouse, and Tableau.
• Introduced a reusable development framework and standardized service patterns for integration and reporting services.

Impact: Reduced operational complexity, improved regulatory compliance, and delivered a single source of truth for confirmations and reporting across asset classes.

Stack: J2EE, EJB, Oracle Cluster, Web Services, IBM MQ, Quartz Timer, SWIFT MT, ECM, Apache Spark, Apache Iceberg, ClickHouse, Tableau.

Artifacts: High- and low-level designs, conceptual and physical data models, solution architecture documentation, development framework.

Stakeholders: Group Operations Business, Legal Business Operations, IT application owners, Change Control Board, Infrastructure Platforms, Agile teams, implementation partners.

The Swiss payment engine processed over 3 million payments per day using a monolithic DB2 setup. With the introduction of ISO 20022 SWIFT message regulations, a complete redesign was required to ensure compatibility, improve maintainability, and support higher throughput.

As part of the modernization, proprietary message formats needed to be mapped to ISO 20022 standards such as PAIN.001 and PAIN.003 for both inbound and outbound payments, with strict adherence to regulatory requirements.

Additionally, a distributed payments engine was developed to improve performance and resiliency, supported by an operational data store designed entirely on ISO 20022 structures.

Solution:

• Led engineering for the Payments Stream of the MyShop Project, focusing on ISO 20022 payment message formats (PACS, PAIN, CAMT).
• Developed transformation components to map ISO 20022 messages to Credit Suisse’s proprietary formats and vice versa.
• Served as IT application owner for the first distributed payments engine integrating ISO 20022 messages into operational workflows.
• Designed and implemented an operational data store based on ISO 20022 message schemas for real-time processing and reporting.
• Coordinated with implementation partners for code reviews, testing, and quality assurance.

Impact: Delivered the first distributed ISO 20022-compliant payments engine at Credit Suisse, enabling regulatory compliance, reducing operational bottlenecks, and improving throughput for millions of daily payments.

Stack: J2EE, EJB, Web Services, Oracle Cluster, IBM MQ, Service Gateway.

Artifacts: High-Level Design (HLD), Low-Level Design (LLD), requirements documentation, framework code for database, web service, and MQ integration.

Enterprises struggle with fragmented tooling, siloed dashboards, and inconsistent policy enforcement across multiple cloud providers. Applying unified security, compliance, and cost controls across clouds often results in duplicated effort, higher operational costs, and visibility gaps.

syntropAI’s AI Cloud Hub was envisioned to provide a unified, language-driven control plane — replacing scripts, static templates, and ad-hoc automation with a natural-language interface backed by policy-as-code, real-time FinOps intelligence, and auditable governance.

Challenge: Centralize AI governance, FinOps insight, and SecOps enforcement across heterogeneous multi-cloud environments without vendor lock-in or operational silos.

Solution: Policy-as-code orchestration with agentic execution (OpenAI SDK, LangGraph, CrewAI, MCP); NIST/OSCAL control mappings embedded in pipelines; FinOps-aware actions with live tagging and cost checks; multi-lingual operations and approvals captured as an auditable graph.

• Governed provisioning: one-sentence requests deploy across clouds with automatic policy checks.
• Autonomous troubleshoot: no playbooks — instruct, fix, and record the trail.
• Approvals & exceptions: built-in dialogue to escalate, approve, and proceed with traceability.
• Cost intelligence: real-time cloud cost insights before the invoice arrives.
• SecOps reporting: auto-generate NIST-aligned reports from live state across clouds.

Impact: Scalable, auditable AI adoption with reduced duplication, fewer misconfigurations, faster governed delivery, and cost-aware deployments.

Stack: OpenAI, Azure AI Foundry, Azure, AWS, OCI, GCP, Graph DB, Terraform, OSCAL, MCP (servers/clients), Python, TypeScript.

Artifacts: AI Cloud Hub reference architecture; policy-as-code libraries (OSCAL mappings); FinOps dashboards & tagging schema; demo notebooks & runbooks; recorded live demos (provisioning, governance, cost, reports).

Product Demo:

• Slide Deck

Live Demos:

• One sentence, all clouds
• Autonomous troubleshoot
• Governance embedded
• Real-time FinOps
• NIST/OSCAL reporting

Github:

• Link

The co-host datacenter housing infrastructure for 25 banking SaaS clients (including Edmond de Rothschild, HSBC, Deutsche Bank, and multiple Cantonal banks) was scheduled for closure, with advanced notice issued to vacate the site. The initial strategy to migrate workloads directly to hyperscaler cloud environments proved unfeasible due to the complexity of the multi-tenant setup and coordination challenges among stakeholders.

A new plan was formed to migrate all infrastructure and workloads to a newly identified co-host datacenter in Zürich, with a disaster recovery setup in Ticino, while ensuring uninterrupted banking operations.

Solution:

• Owned overall migration design and strategy for all clients, covering AIX and x86 workloads.
• Led end-to-end stakeholder coordination — clients, network providers, procurement, CISO, Data Officers, and program management teams.
• Established agile migration teams with architects and SMEs for compute, network, database, and observability.
• Directed sprint planning, work package creation, progress tracking, and risk reporting.
• Secured dedicated, high-availability migration and operations network lines between Zürich and Ticino.
• Produced solution documentation, runbooks, and project plans for 150+ migration packages.
• Oversaw hiring, procurement, approvals, and execution of migration tasks.

Impact: Successfully migrated 1,480 servers (AIX & x86) for 25 banks, executed 233,881 test cases, and vacated the original datacenter with zero downtime or regulatory breach.

Stack / Domains: AIX, x86 virtualization, private cloud, high-availability networking, DR replication, observability tooling.

Artifacts: Solution architecture documents, detailed runbooks, project migration plan, sprint boards, risk registers, compliance approval records.