AI Workflows for Faster Issue Resolution
How a financial services firm reduced customer inquiry resolution time from 3 days to 4 hours using intelligent workflow automation.
Arlington Financial was handling 5,000+ customer inquiries monthly through a fragmented process spanning three departments and four systems. VictorLabs built an AI-powered workflow system that automated classification, routing, and response drafting — cutting resolution time by 94%.
Resolution time reduced
3 days → 4 hrs
Routing accuracy
97%
Search time reduced
45 percentage points
Monthly inquiries
5,000+
Context
Arlington Financial is a regional financial services firm serving 50,000+ retail and commercial clients. Their customer service operation handled inquiries across banking, investments, and insurance — each with its own department, knowledge base, and resolution process. Inquiries were routed manually based on agent judgment, leading to frequent misroutes and delays.
Problem
Average inquiry resolution took 3 days. 22% of inquiries were routed to the wrong department on first attempt. Support agents spent 60% of their time searching for information across disconnected systems rather than resolving issues. Customer satisfaction scores had declined for four consecutive quarters, and regulatory SLAs for certain inquiry types were at risk of breach.
Constraints
SOC 2 compliance required all data processing to remain within the firm's environment. Three legacy backend systems (banking, investments, insurance) had no modern APIs. Any AI system needed full audit trail for regulatory compliance. Internal knowledge was spread across Confluence, SharePoint, and PDF policy documents.
Approach
We mapped the full inquiry lifecycle across all three departments, identifying 14 distinct inquiry types and 7 common failure points. Rather than attempting full automation, we designed a system that classified and routed inquiries automatically, retrieved relevant knowledge for agents, and drafted responses for review — keeping the human agent in control of final decisions.
Solution
An LLM-based classification engine analyzes incoming inquiries and routes them to the correct department and agent with 97% accuracy. A RAG system indexes all internal knowledge sources and retrieves relevant policies, procedures, and past resolutions in real time. An AI-assisted response drafting tool generates a complete draft response with citations to relevant policies. All actions are logged for audit compliance. A management dashboard tracks resolution times, accuracy, and SLA compliance.
Architecture
The system is deployed entirely within the client's Azure environment. Frontend: React agent workspace with integrated knowledge panel and response editor. Backend: Python FastAPI services for classification, retrieval, and response generation. Knowledge is indexed via a vector database with chunked embeddings. The classification model uses a fine-tuned LLM with department-specific examples. All services run in VPC with no external API calls.
Results
Average resolution time dropped from 3 days to 4 hours. First-contact routing accuracy improved from 78% to 97%. Agent time spent searching for information decreased from 60% to under 15%. Customer satisfaction scores reversed their decline within the first quarter post-deployment. Regulatory SLA compliance was restored for all inquiry categories.
Lessons
Keeping the human agent in the loop was the right architecture decision — it built trust with the compliance team and allowed for gradual adoption. The RAG system's citation feature (linking every retrieved piece of information to its source document) proved unexpectedly valuable: agents used it for their own learning, not just for drafting responses.
“We needed an AI system that could work across three legacy platforms and meet SOC 2 requirements. VictorLabs designed an architecture that integrated cleanly and passed our security review on the first submission.”
— Marcus Webb, CTO, Arlington Financial