NiCE Actimize's Phoenix Connector Migration
NiCE Actimize partnered with Xoriant to modernize its connector estate on the Phoenix platform, bringing together a cross-functional team of around 20 Development, QA, DevOps and Data Engineering specialists.
The program re-platformed connectors onto a new architecture that moves data from cloud object storage through a streaming ingestion layer into the connector, the indexing tier and the downstream case management application. Thirteen connectors were migrated with a 50%+ performance improvement, while Anthropic Claude models were applied to alert segmentation and AI-assisted SDLC automation.
About NiCE Actimize
NiCE Actimize is a leading provider of AI-driven financial crime, risk and compliance solutions, monitoring over five billion transactions a day for more than 1,000 clients. Its trade surveillance solutions help buy-side firms such as hedge funds, asset managers and wealth managers detect insider dealing and market manipulation.
Migrating connectors without disrupting surveillance coverage
while improving throughput, reducing production support load and standardising quality across a large connector portfolio. Key requirements included:
Architecture Alignment
Move every connector onto the new Phoenix data flow.
Functional Parity
Preserve all existing connector capabilities end to end.
Performance at Scale
Raise throughput materially over the legacy connectors.
Automated Quality
Establish a repeatable regression suite for every connector.
Support Reduction
Lower production support effort for client deployments.
NiCE Actimize required a repeatable migration pattern that could scale across the remaining connector portfolio.
Why Xoriant & Anthropic Claude?
Cross-Functional Delivery
One Xoriant team of around 20 across Development, QA, DevOps and Data Engineering.
Anthropic Claude Models
Agentic alert segmentation, intelligent classification and AI-assisted SDLC automation.
Automation Engineering
Roughly 900 automation tests across 13 connectors in about 3.5 hours.
Testing & Quality
Automated UI and backend testing, performance testing and security scanning.
Deep functional coverage
Deep functional coverage across attachment processing, language detection, whitelist and blacklist management, user tagging and domain classification.
Migrating Connectors to Phoenix
Xoriant ran the Phoenix Connector Migration program with a cross-functional team of around 20 Development,QA, DevOps and Data Engineering specialists, delivering connectors in successive waves.
Program Setup and Team
Stood up a cross-functional team of about 20 across Development, QA, DevOps and Data Engineering, with a shared migration pattern and delivery cadence.
Re-Architected Connector Flow
Moved each connector onto the new Phoenix flow: cloud object storage, streaming ingestion, connector processing, indexing and downstream case management.
Functional Capability Migration
Carried over attachment processing, language detection, whitelist and blacklist management, policy actions, user tagging, domain classification and reconciliation.
AI-Assisted Engineering with Claude
Applied Anthropic Claude models to agentic alert segmentation and intelligent classification, and to AI-powered automation across the SDLC.
A Scalable Connector Platform
Performance Improvement
The new Phoenix architecture lifted connector throughput by more than 50%.
Fewer Support Tickets
Clients on migrated connectors saw roughly a 30% drop in production support tickets.
Connectors Migrated
Delivery momentum was maintained across 13 connectors despite resource constraints.
Automated Tests in 3.5 Hours
A robust automated QA pipeline now covers all 13 migrated connectors.
Onboarding
Early migrations set the pattern that sped up every later connector.
Compliance Features
Anthropic Claude models drive agentic alert segmentation and SDLC automation.
“Xoriant has worked closely with our teams to identify, design, and implement practical AI solutions across multiple areas. Our collaboration has included AI-powered automation across the Software Development Life Cycle (SDLC), intelligent classification and analysis, and agentic AI solutions leveraging Anthropic Claude models for alert segmentation. These initiatives have helped enhance existing processes, improve operational efficiency, and accelerate the adoption of Generative AI within our products and engineering workflows.”
Swapnil Disawal Senior Director, NiCE Actimize