Everything support teams need to know about adopting AI — from ticket triage and draft replies to quality scoring and sentiment tracking. Explore eighteen AI capabilities, adoption frameworks, and industry research.
// Capabilities
Each capability addresses a specific challenge in support operations. Explore how AI can transform your team's workflow.
Classify, prioritise, and route tickets automatically using LLM-powered analysis. Assign priority, category, and team in milliseconds.
ClassificationGenerate context-aware draft responses grounded in your knowledge base, conversation history, and customer context.
GenerationScore conversations against configurable rubrics. Track agent performance with automated quality reviews.
QualityReal-time sentiment analysis with escalation risk detection. Track customer health across every interaction.
AnalysisLive support dashboard with volume forecasting, anomaly detection, and SLA tracking.
MetricsSemantic search across your knowledge base. Find answers by meaning, not keywords.
KnowledgeJinja2-powered response templates with smart variable interpolation from ticket context.
TemplatesConversation summarisation with handoff briefs and entity extraction. Long threads to key points.
SummarisationLLM-powered translation, language detection, and cultural localisation for multilingual support.
TranslationAI copilot with contextual suggestions, next-action recommendations, and rich context panels.
CopilotAudit knowledge bases for duplicates, contradictions, and coverage gaps. Data quality for AI.
Data QualityTheme discovery and trend analysis across support cases. Understand what customers really ask about.
IntelligenceScore case complexity and automation potential. Build a data-driven automation roadmap.
AI ReadinessPII detection and data sanitisation. Make support data safe for AI consumption.
SecurityAuto-tagging and taxonomy normalisation for consistent, clean support data.
Data QualityPerformance baselines, before/after comparison, and automation ROI tracking with real data.
ROIDry-run automation rules against real data. Test before deploying to production.
TestingCase type playbook builder. Resolution steps, required data, and automation feasibility.
Playbooks// Foundation
AI for support isn't a single tool — it's a coordinated set of capabilities. Triage classifies and routes tickets. Reply drafts responses. QA scores conversations. Sentiment flags escalation risk. Each capability is modular, but they compound when combined.
# A typical AI-powered ticket lifecycle
1. Triage → Classify, prioritise, route
2. Reply → Draft a context-aware response
3. Sentiment → Monitor customer emotion
4. QA → Score the conversation
5. Pulse → Track metrics and SLAs
# Each step is independent but compounds
# when combined into a full workflow.// Philosophy
Each service is independently deployable. Use one or compose them all. No monolith, no coupling.
REST APIs everywhere. Integrate with your helpdesk, build a custom UI, or automate with scripts.
Choose your AI provider — swap between OpenAI, Anthropic, Azure, or any provider without re-architecting.
// Research
Data-backed analysis on customer support trends, AI benchmarks, and operational metrics. Cited sources from Zendesk, Gartner, Salesforce, McKinsey, and more.
// Landscape
Frameworks, platforms, and standards shaping AI-powered customer support in 2026.
Open-source frameworks for building custom AI agents and multi-step workflows
10 min read · 2026-04-02Turnkey AI agent products built specifically for customer support teams
9 min read · 2026-04-02MCP and A2A — the two protocols shaping how AI agents connect to tools and to each other
8 min read · 2026-04-02Multi-agent systems, voice agents, human-in-the-loop, and observability — the trends shaping 2026
9 min read · 2026-04-02// For your team
Whether you handle tickets, analyse trends, manage a team, or set strategy — simpli has tools and AI skills tailored to your workflow.
Dive into our guides, explore AI capabilities, and start your team's AI adoption journey.