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MLL Intake System - Stakeholder Presentation
Multi-Agent AI Chatbot System
MLL Division
MLL Division
1
2026-01-30T05:08:02Z
2026-01-30T05:08:02Z
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MLL INTAKESYSTEMMulti-Agent AI Chatbot SystemStakeholder Presentation | UI Design & System ArchitectureManufacturing, Logistics & Labs (MLL) DivisionJanuary 2026 | Version 1.0AIPK
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Contents1Executive Summary2Project Overview3UI/UX Design4User Interaction Flow5System Architecture6Multi-Agent Framework7Classification Pipeline8Knowledge Graph9Enterprise Integration10Performance Metrics11Implementation Timeline12Benefits & ROIPK
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Executive Summary The Problem15-30 min navigating Confluence docs~70% manual classification accuracyDuplicate requests waste resourcesLong SLA times, incomplete data The SolutionNatural language interface4-stage semantic classification (>95%)GraphRAG duplicate detection (>90%)Smart questions in <5 minutesKey Performance Targets >95%Classification <30 minProcessing >90%Duplicates >70%Resolution 4.5/5SatisfactionPK
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Project OverviewProject ScopeSDSREQUEST: Production-ready multi-agent AI chatbot processing IT requests through natural language, routing to IRIS (ServiceNow) and Jira.Primary ObjectivesEliminate Confluence navigationReduce SLA timesImprove classification accuracyEnable intelligent team assignmentLinkedIn GraphRAG Benchmarks (SIGIR 2024)+77.6%MRR-28.6%Resolution+0.32BLEU<3msLatencyData Foundation Data Source Records IRIS Solution Intake 3,091 Jira Epics (ABFZ) 7,210 Jira User Stories 6,551 TOTAL 16,852 PK
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UI/UX Design - User JourneyConversational-first approach with intuitive chat interfaceKey UI ComponentsChat InterfaceStreamlitFile UploadFileUploaderOption ButtonsCustomProgress TrackerProgress BarDecision PanelExpandableStatus DashboardDataFramesUser Experience Flow1WelcomeSSO greeting, quick actions2ArticulationNatural language, feedback3CollectionSmart questions, progress4SubmissionReview, one-click submitPK
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User Interaction Flow 1GreetingSSO auth 2TypeSelection 3DescribeNL input 4Classify4-stage AI 5QuestionsSmart Q's 6ValidateReview 7SubmitConfirm 8RITMServiceNow 9EpicJira auto 10NetworkIf needed 11TrackStatusPK
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System Architecture4-Layer Architecture for Scalability & IntelligenceLayer 1User InterfaceStreamlit ChatMobile AppAPI GatewayDashboardLayer 2Agent OrchestrationSupervisorClassifierIntakeNetworkLayer 3Semantic IntelligenceOntologySWRL RulesNeo4j KGVector IndexLayer 4Enterprise IntegrationIRIS APIJira APIActive DirectoryCMDBPK
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Multi-Agent FrameworkSupervisor AgentOrchestratorClassifierIntakeNetworkIncidentAccessStatusKnowledgeIntake vs Incident Intake Incident New demand / never worked Unplanned interruption / broke Enhancement or capability Reduction in service quality PK
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Semantic Classification Pipeline1Stage 1: NERExtract entities: Site, Component, TypeSITE: JUAREZ | COMPONENT: Network2Stage 2: OntologyLink to classes, apply SWRL rulesEntity Linking → Class Assignment3Stage 3: Embedding384-dim vectors, HNSW similarityScore >0.7 similar | >0.9 duplicate4Stage 4: Graph ContextTeam patterns, KB articlesService Owner, Est. Resolution TimePK
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Knowledge GraphNode TypesJiraEpickey, summaryUserStorykey, pointsRITMnumber, stateSiteid, nameUserid, emailComponentnameRelationshipsAUTOMATES_TO: RITM → EpicCONTAINS: Epic → StoryLOCATED_AT: Epic → SiteASSIGNED_TO: Task → UserSIMILAR_TO: Epic → EpicData Pipeline (LinkedIn GraphRAG)1ParseTree repr2ConnectLinks3Embed384-dim4IndexHNSW5ClusterPatternsPK
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Enterprise Integration FlowIRIS → Jira Automation1Chatbot creates RITM in IRIS2IRIS triggers Jira Epic creation3Gather User Stories task created4User gets RITM + Epic linkNetwork Team (Pete Ward)1. MLL Intake required (prereq)2. Service owner gathers scope3. Network Jira card created4. Katherine reviews on triage5. Pete assigns to teamPriority Definitions P1 Urgent/Emergency Business critical, security P2 High Priority Project work, focus needed P3 Standard Normal processing (default) P4 Deferred Back burner PK
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Performance MetricsRetrieval Performance (LinkedIn) Target KPIsClassification>95%Processing Time<30 minDuplicate Detection>90%Field Completion95%SLA Reduction40%Answer Quality Improvements BLEU0.057 → 0.377+0.32 ROUGE0.183 → 0.546+0.36 METEOR0.279 → 0.613+0.33PK
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Implementation Timeline18-Week Strategy | 4 PhasesPhase 1 (Wk 1-4)Foundation• Ontology• KG Schema• ETL Pipeline• NER ModelPhase 2 (Wk 5-10)Development• 8 Agents• LangGraph• Classification• DuplicatesPhase 3 (Wk 11-14)Integration• IRIS/Jira API• SSO Auth• UAT TestingPhase 4 (Wk 15-18)Deployment• Production• Monitoring• RolloutKey MilestonesWeek 4KG CompleteWeek 10Agents ReadyWeek 14UAT Sign-offWeek 18Go-LivePK
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Benefits & ROIUser Benefits✓ <5 min submission✓ Natural language✓ Real-time tracking✓ 24/7 availabilityIT Team Benefits✓ 95% field completion✓ >90% duplicate detect✓ Auto routing✓ Audit trailBusiness Benefits✓ 40% SLA reduction✓ 85% time savings✓ 28.6% faster resolution✓ 4.5/5 satisfactionExpected ROI Metric Before After Improvement Time per Request 15-30 min <5 min 85% savings Processing Time 4-6 hours <30 min 90% reduction Duplicate Work 30% tickets <10% 20% efficiency Resolution Time 7 hrs median 5 hrs 28.6% faster PK
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Thank YouMLL Intake SystemRevolutionizing IT Service Request ManagementQuestions?AIMLL DivisionJanuary 2026PK
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