Our integration methodology for London enterprises addresses the regulatory, multi-jurisdictional, and legacy complexity that defines the UK market. Discovery and architecture (weeks 1-3): comprehensive mapping of the current system landscape — every system, every data flow (automated and manual), every integration point. We identify: manual processes ripe for automation (the spreadsheet bridges, the email-based workflows, the copy-paste operations), data quality issues (inconsistencies between systems that share the same data), regulatory data flow requirements (which data must flow where, when, and in what format — per FCA, PRA, and Bank of England requirements), and latency requirements (real-time, near-real-time, or batch — driven by business and regulatory needs). Integration patterns: API-first connectivity (REST/GraphQL APIs for modern systems, with proper versioning, rate limiting, and documentation), event-driven architecture (Apache Kafka or AWS EventBridge for real-time, decoupled integration — critical for trading and payments systems), message-based integration (IBM MQ, RabbitMQ for systems requiring guaranteed delivery and transaction-level reliability), file-based integration (SFTP, AS2 for legacy systems and external partner connectivity — still prevalent in Lloyd's Market and correspondent banking), and database integration (Change Data Capture using Debezium for real-time data extraction without modifying source systems). Regulatory compliance: every integration includes audit trail logging (who sent what data, when, to which system, with what result), data lineage tracking (tracing each data element back to its source — essential for FCA data quality requirements), retention management (per FCA SYSC 9 and MiFID II Article 16 record-keeping requirements), and access controls (RBAC with multi-factor authentication for sensitive system access). Technology stack: TypeScript/Node.js for API-layer integration, Python for data pipeline processing, Apache Kafka for event streaming, and containerised deployment (Docker/Kubernetes) for scalability and reliability. Testing: integration testing (verifying data flows end-to-end across systems), reconciliation testing (ensuring data consistency post-integration), performance testing (verifying latency and throughput under production load), and regulatory scenario testing (verifying correct data flow for regulatory reporting edge cases).