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Nexora Labs
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Enterprise Software & AI SectorClient Archetype: Global B2B SaaS Enterprise Software ProviderVerified Case Study

SupportAI: Autonomous Customer Support Agent & Enterprise RAG

Automating 68% of Enterprise Support Tickets with Grounded RAG and Deterministic Guardrails

68%
First-Contact Autonomous Ticket Resolution Rate
12 Sec
Average First-Response Time (Down from 9 Hours)
94%
Customer Satisfaction (CSAT) Score
0
Hallucination Escapes on Verified Technical Docs
Visual System Blueprint

Production Architecture & Telemetry

Target SLA: 99.99% • SOC 2 Aligned
SYSTEM-BLUEPRINT//48,500 q/s
Autonomous Private RAG & Multi-Agent Pipeline
Document Ingestion & Chunking
High-Dim Vector Database Cube
Security Guardrail Shield
Neural Reasoning & Synthesis
ARCH STATUS: VERIFIEDP99 LATENCY: 4.8 ms
OWASP LLM-01 Pass

Document Ingestion & Chunking

OCR & Tokenization
Benchmark:22.4 GB/min

Unstructured PDF, DOCX, Markdown extraction with layout-aware semantic chunking.

Click numbered hotspots on the blueprint to inspect other nodesDeterministic Standard
Initial Challenge

The Client Problem

A B2B SaaS company experienced a 120% surge in customer support ticket volume following global expansion. Human support engineers spent over 60% of their day answering repetitive questions regarding API configurations, billing cycles, and feature toggles spread across 3,000 fragmented Confluence pages, PDF user manuals, and Jira tickets. First-response times exceeded 9 hours, leading to customer churn.

Architectural Constraints

Key Requirements Scoped

  • •Autonomous conversational AI agent capable of answering technical product questions in natural language
  • •Enterprise Retrieval-Augmented Generation (RAG) connecting internal documentation securely
  • •Strict factual grounding ensuring the agent never hallucinates features or invents incorrect API code
  • •Direct integration with Zendesk to automatically resolve tickets or escalate complex edge cases with full context
  • •Deterministic privacy guardrails preventing disclosure of internal confidential roadmaps
Architectural Delivery

The Delivered Engineering Solution

Nexora Labs architected SupportAI, an enterprise autonomous support agent. We built a hybrid retrieval RAG pipeline using LangChain, Qdrant vector database, and Claude/OpenAI foundational models. We established multi-stage guardrails (NeMo Guardrails) to enforce strict factual grounding, citations, and negative knowledge boundaries, alongside bidirectional Zendesk ticketing integration.

Confirmed Implemented Features

Hybrid retrieval RAG pipeline combining dense vector embeddings with sparse BM25 keyword matching
Factual verification guardrail layer validating that every generated claim is supported by retrieved docs
Autonomous tool-calling capabilities enabling the agent to query customer subscription status and reset API keys
Intelligent ticket escalation routing complex technical edge cases to specialized tier-3 engineers with summary dossiers
Continuous hallucination evaluation telemetry scoring faithfulness, context recall, and user satisfaction
Systems Topology

Exact Technology Architecture

AI & RAG
  • • LangChain
  • • OpenAI GPT-4o
  • • Anthropic Claude 3.5 Sonnet
  • • Qdrant Vector DB
Guardrails & Safety
  • • NeMo Guardrails
  • • Guardrails AI
  • • Ragas Evaluation Framework
Backend Services
  • • Python
  • • FastAPI
  • • PostgreSQL
  • • Redis
Ticketing & CRM
  • • Zendesk API
  • • Salesforce API
  • • Slack Webhooks
Infrastructure
  • • AWS ECS Fargate
  • • Terraform
  • • Docker
  • • Datadog
Architecture Blueprint

Implementation Details & Architectural Notes

We developed a specialized document chunker that preserves Markdown code blocks and table hierarchies intact. During inference, the agent performs reciprocal rank fusion (RRF) across Qdrant vector searches and PostgreSQL full-text search. Before outputting an answer, a secondary verification prompt compares the drafted response against source chunks to verify 100% factual faithfulness.

Validation Protocol

Quality Assurance & Testing Execution

  • Automated evaluation against a golden test suite of 850 historical customer support inquiries
  • Adversarial red-teaming simulating 200 prompt injection and jailbreak attack vectors
  • Latency benchmarking under concurrent load ensuring p95 response time stayed under 1.8 seconds
Validated Commercial Outcome

Project Impact & Results

SupportAI autonomously resolved 68% of incoming customer support inquiries without human involvement. Average first-response time dropped from 9 hours to 12 seconds, while customer satisfaction (CSAT) scores increased from 78% to 94%.

Practice Integration

Services Leveraged in This Engagement

Custom Software DevelopmentGenerative AI DevelopmentAI Agent DevelopmentCloud Engineering & DevOps

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