Banking platforms operate across complex processes, multiple systems and strict operational constraints. The challenge was not simply delivering individual application features, but creating reliable workflows that could interact with automation and external services while remaining maintainable as the platform evolved.
EXPERTISE / CASE STUDIESENTERPRISE SYSTEMS
ENGINEERING IN PRACTICE
Complex systems. Practical outcomes.
Selected examples of enterprise applications, automation, integrations and AI systems built around real operational challenges.
The projects below span banking, business operations and risk management, combining low-code engineering, automation, cloud services, enterprise integrations and applied AI.
5+YEARS
OutSystems engineering experience across enterprise systems.
O11OUTSYSTEMS
Enterprise application development and architecture.
ODCCLOUD-NATIVE
Modern OutSystems cloud development.
AIAPPLIED
Contextual agents, retrieval and enterprise AI integrations.
BANKING / ENTERPRISE
Enterprise Banking Platform
Enterprise banking applications built across OutSystems 11 and ODC, combining business workflows, robotic process automation and cloud integrations in a highly structured environment.
The platform combined enterprise low-code development with RPA and cloud integrations. OutSystems was used to build business-facing functionality and orchestration layers, while automation handled repetitive operational activities and integrations connected the wider banking ecosystem.
The result was a more connected application landscape where business processes, automation and external systems could operate as part of the same digital workflow.
TECHNOLOGY
- OutSystems 11
- ODC
- RPA
- REST APIs
- Cloud Integrations
- Enterprise Workflows
ENGINEERING FOCUS
- Enterprise application architecture
- Workflow orchestration
- Automation integration
- Cloud connectivity
- Maintainability
- Operational reliability
BUSINESS OPERATIONS
Business Case Management Platform
A central enterprise platform for managing requests, business cases, collaboration, documentation, commercial information and decision-making across complex business processes.
Business cases often involve multiple teams, stages, documents, commercial facts and external systems. Information can easily become fragmented across email, files, spreadsheets and CRM platforms, making ownership and progress difficult to understand.
The platform brings the lifecycle into one structured application. Requests, milestones, teams, tasks, comments, key facts, documentation and files are connected around the business case itself, while Salesforce and cloud integrations extend the application into the wider enterprise landscape.
Users gain a consistent view of progress, responsibilities and information while reducing fragmentation between business processes and supporting systems.
TECHNOLOGY
- OutSystems
- Salesforce
- REST APIs
- Cloud Storage
- Enterprise Integrations
- SQL
ENGINEERING FOCUS
- Business case lifecycle
- RFQ and information requests
- Role-based collaboration
- Salesforce integration
- Cloud file management
- Enterprise architecture
APPLIED AI / RISK
AI-Powered Risk Management Platform
A risk management application enhanced by two distinct AI architectures: one capable of researching live external information and another designed to understand private application context without relying on internet access.
Risk analysis requires both external intelligence and deep internal context. Those two information domains have different security, connectivity and trust requirements, making a single generic AI assistant insufficient.
Two specialized AI experiences were introduced. The first agent can search external internet sources to support research and current-information discovery. The second operates around application-specific information, providing contextual assistance based on internal data without requiring internet connectivity.
The architecture separates external research from private contextual assistance, allowing AI capabilities to be matched to the security and information requirements of each task rather than forcing every use case through the same model.
TECHNOLOGY
- OutSystems
- Applied AI
- AI Agents
- Web Search
- Private Context
- Enterprise APIs
ENGINEERING FOCUS
- Internet-enabled research
- Private contextual AI
- Risk intelligence
- Agent separation
- Application context
- Secure AI integration
DUAL AGENT ARCHITECTURETwo information domains. Two specialized AI systems.
External Intelligence
Searches live internet sources to support external research and current risk intelligence.
INTERNET CONNECTED
Private Context
Provides assistance using internal application context without requiring internet access.
PRIVATE / INTERNAL
THE COMMON THREAD
The platform changes. The engineering principles do not.
Each system operates in a different domain, but the underlying approach remains consistent: understand the business problem, design clear boundaries, integrate deliberately and use technology where it creates measurable value.
Low-code, automation, cloud services and AI are treated as parts of a broader architecture, not isolated features.
YOUR CHALLENGE
Building something complex?
Start with the problem. We can work backwards from there.