Customer support teams don’t lose time only because issues are complex—they lose time because knowledge is scattered. Answers live across PDFs, internal wikis, CRM notes, product manuals, engineering tickets, release notes, and tribal memory. Agents spend minutes searching, switching tabs, and asking peers before they can confidently respond. The result is longer handle time, inconsistent answers, and repeat contacts that erode customer trust.
A growing business serving thousands of customers across multiple products was seeing support volumes rise rapidly. The team had a CRM/helpdesk platform, knowledge base articles, and standard macros—but resolution time remained high and quality varied by agent experience.
Key challenges included:
Knowledge fragmentation
Critical information was spread across multiple systems: KB articles, PDFs, internal docs, past tickets, engineering notes, and customer-specific configurations. Finding the “right” answer was slow.Inconsistent responses and high rework
Two agents could provide different guidance for the same issue, leading to customer confusion, escalations, and repeat tickets.Long time to competency for new agents
New hires took weeks to become effective because understanding product behavior and troubleshooting steps relied on tribal knowledge.Escalations consuming engineering time
Many escalations were avoidable but occurred because agents lacked the right troubleshooting steps or confidence in answers.Limited visibility into root causes
Ticket categorization lacked precision, making it difficult to identify common problem patterns and preventive fixes.
The goal was to reduce resolution time while improving answer consistency, agent confidence, and escalation quality.
Maayan Technologies implemented a Support Intelligence Platform combining AI Assist with a Knowledge Graph, integrated directly into the customer support workflow.
1) Knowledge Unification and Governance
We consolidated and cleaned knowledge sources including:
Helpdesk tickets and resolutions
Knowledge base articles and internal wikis
Product documentation, SOPs, and troubleshooting guides
Release notes and known-issues logs
Engineering bug links and incident summaries
Content was normalized, deduplicated, and version-controlled. Ownership was defined for each knowledge domain so articles stayed current.
2) Knowledge Graph: Connecting “What, Who, and How”
Instead of treating knowledge as flat documents, we built a Knowledge Graph that linked:
Products → modules → features → error codes
Symptoms → likely causes → recommended fixes
Customer accounts → configurations → entitlements → known history
Incidents → root causes → patches → workarounds
This made knowledge “navigable” and enabled AI to retrieve context-aware, precise guidance instead of generic search results.
3) AI Assist for Agents (Context-Aware Copilot)
An AI Assist layer was embedded into the support console to:
Summarize the issue from the customer message and ticket history
Suggest clarifying questions to reduce back-and-forth
Recommend next-best troubleshooting steps based on symptom patterns
Generate draft responses with citations to approved sources
Propose resolution notes and ticket categorization automatically
Suggest related past cases and known fixes
Guardrails ensured responses were grounded in approved knowledge sources, avoiding hallucinations and ensuring consistency.
4) Workflow Integration and Escalation Quality
The system integrated with the helpdesk and engineering workflows:
Automatic creation of structured escalation packages (logs requested, steps tried, environment details, error screenshots)
Routing recommendations based on product/module ownership
Capture of resolution outcomes and their linkage back to knowledge entities
This reduced unnecessary escalations and improved the quality of the escalations that remained.
5) Continuous Learning Loop
We implemented an operational loop to keep the system improving:
Agent feedback buttons (“helpful/not helpful,” “outdated,” “missing article”)
Detection of knowledge gaps from repeated queries
Weekly knowledge curation sprints for top issues
Analytics dashboards for deflection, accuracy, and time savings
This ensured the AI system remained aligned with product changes and customer reality.
The transformation delivered measurable improvements in speed and quality:
~40% faster average resolution time as agents spent less time searching and more time executing proven steps.
Improved first-contact resolution through better next-step suggestions and smarter clarifying questions.
More consistent and compliant responses with citations to approved knowledge and standardized troubleshooting flows.
Faster ramp-up for new agents by embedding guided support intelligence into daily workflow.
Reduced avoidable escalations and higher-quality escalation packets for engineering.
Better insight into recurring issues via improved categorization and graph-driven trend analysis.
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