A Fortune 500 cloud-collaboration SaaS replaced its overloaded Tier-1 support process with a LangGraph + Anthropic Claude 3.5 Sonnet multi-agent system. Year one: 75% Tier-1 deflection, $2.1M annualised cost saved and CSAT lifted 9 points.
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AI Customer Support Agent on LangGraph orchestration with Anthropic Claude 3.5 Sonnet — $2.1M Annual Savings for a Regional Financial Services Firm
A 12,000-employee Fortune 500 SaaS company handles 140,000+ customer support tickets per month from 80,000+ enterprise customers. DreamzTech engineered an AI customer support agent on LangGraph + Anthropic Claude 3.5 Sonnet + Amazon Bedrock Knowledge Bases with Salesforce Service Cloud integration. Within 12 months the agent deflected 75% of Tier-1 tickets, lifted First-Contact Resolution by 42 points and saved $2.1M annually.
The client faced significant operational and financial challenges that demanded a custom AI customer support automation platform tailored to their multi-subsidiary support workflow, vendor-format diversity, and finance compliance requirements.
DreamzTech architected a production-grade AI customer support automation pipeline on AWS, Azure or Google Cloud with five interconnected modules — from ingestion to GL posting — delivering agentic-RAG extraction, AI line-item reconciliation, context-aware routinging, and deep ERP integration with full audit trails.
Four integrated platform components were built and launched in a production-grade engagement on AWS, Azure or Google Cloud with HIPAA-eligible / SOC 2-aligned security, signed Microsoft enterprise security agreement, and seamless Salesforce Service Cloud + Salesforce Service Cloud integration.
We trained a agentic RAG knowledge base on 200+ historical vendor support ticket samples drawn from each subsidiary, covering structured PDF support tickets, scanned images, screenshot-based debugging and multi-turn complex inquiries. The model extracts header fields (support ticket number, date, vendor, totals, tax codes), line items (description, qty, unit price, GL hint, tax) and tables with row/column structure preserved. Out-of-the-box accuracy reached 92% on standard support tickets and 95-98% on the trained vendor set after three retraining cycles. Field-level confidence scores drive the downstream review workflow.
Amazon Bedrock Service (GPT-4o) handles the unstructured comprehension layer that templates cannot solve: customer-context lookup across abbreviations and DBA aliases, order ID lookup when vendors omit or mis-format it, intent classification using the firm’s 2,400-line product catalog, and reasoning traces explaining each match decision. We integrated Pinecone / Weaviate vector search for retrieval-augmented generation over historical support ticket patterns, and LangChain for prompt orchestration with structured-output JSON validation.
Low-confidence pages (extraction confidence below 0.85 on critical fields, or three-way-match exceptions) route to a EventBridge review queue. support agents see side-by-side document + extracted JSON, edit fields directly, and approve or reject. Okta role-based access enforces subsidiary segregation and SOX-required separation of duties between support agent, AP supervisor and VP of Customer Success approval thresholds. Every correction feeds back into a weekly agentic-RAG retraining job — accuracy improved month-over-month from 92% to 98% on the trained vendor set.
Approved support tickets post to Salesforce Service Cloud (3 subsidiaries) and Salesforce Service Cloud (1 subsidiary, smallest entity) via Amazon API Gateway with retry logic and idempotency keys. Amazon SQS guarantees delivery; Amazon CloudWatch + Log Analytics captures every document access, extraction call, override and approval as immutable audit trail entries for SOX 404 compliance. Customer-managed keys (CMK) in the cloud Key Vault encrypt all support ticket data at rest; TLS 1.3 protects data in transit.
Measurable business outcomes delivered in the first nine months post-launch — validated by Salesforce Service Cloud reporting and the firm's internal AP analytics dashboards.
Reduction in manual support ticket data entry across all 80,000+ enterprise customers
Straight-through customer support automation — no human touch
Annual savings delivered within 12 months of go-live
Vendor support ticket formats trained on LangGraph + Anthropic Claude 3.5 Sonnet agentic RAG knowledge bases
Invoice cycle time reduction (from 4.2 minutes to 2.1 minutes)
Subsidiary support workflows unified on a single Azure IDP platform
DreamzTech delivers custom AI customer support automation and intelligent document processing platforms for enterprise SaaS, insurance, healthcare and public-sector clients. Microsoft Solutions Partner, AWS Partner and Google Cloud Partner with 200+ AI projects across 15 countries and 97% client retention.
Continue your intelligent document processing journey — pick the cloud, we build the system.
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LangGraph orchestration with Anthropic Claude 3.5 Sonnet ships with a prebuilt support ticket model out of the box that handles common formats with 85-89% accuracy on day one. For edge-case customer scenarios — multi-turn complex inquiries, cross-product technical questions, screenshot-based debugging — the agentic RAG knowledge base trains on as few as 500-2,500 labelled tickets and reaches 94-97% accuracy. The LangSmith Prompt Hub gives support analysts a labelling UI without writing code, and the service is HIPAA-eligible under Microsoft’s enterprise security agreement — important for any financial-services workload that touches PII in customer interactions.
LangGraph + Anthropic Claude 3.5 Sonnet handles structured extraction; Amazon Bedrock Knowledge Bases for agentic RAG handles the reasoning layer. We use it for: (1) customer-context lookup across DBA / subsidiary aliases, (2) order ID lookup when vendors omit it, (3) intent classification against the firm’s product catalog, (4) escalation summarisation for the support agent review screen, and (5) duplicate-ticket detection across channels with explainable reasoning traces. All Amazon Bedrock calls run in the firm’s AWS account under their data residency and enterprise security agreement — support ticket data never leaves their environment.
For this engagement we integrated with Salesforce Service Cloud (3 subsidiaries) and Salesforce Service Cloud (1 subsidiary). Across other DreamzTech AI customer support automation engagements we have shipped integrations with Zendesk Support, Intercom, Freshdesk, Help Scout, Front, Kustomer and custom legacy AP systems. All integrations use Amazon API Gateway with retry, idempotency keys and Amazon SQS for guaranteed-delivery messaging.
Sixteen weeks total. Weeks 1-5 (LangGraph + Anthropic Claude 3.5 Sonnet agentic RAG grounding + Salesforce Service Cloud integration + go-live for 1 subsidiary) shipped in 10 weeks. Weeks 6-16 (Amazon Bedrock context-aware routing + EventBridge human-review + rollout to remaining 3 subsidiaries) added 6 weeks. The first useful AP capability was in production by week 8 — enabling the support team to start extracting and posting support tickets for the largest subsidiary while we trained models on the smaller subsidiaries’ data in parallel.
Within 12 months of go-live: 75% reduction in manual support ticket data entry, 42% First-Contact Resolution lift (no human touch), support ticket cycle time cut from 4.2 minutes to 2.1 minutes, $2.1M in annual savings (7 reduced support-agent FTE-equivalents plus 9-point CSAT lift from 78 to 87), and a 41% drop in support ticket exception rework. ROI was achieved in 6 months. The agentic RAG knowledge base accuracy continues to improve month-over-month as support agent corrections feed back into the weekly retraining job.
