SMBs need support automation but can't afford 12-month implementations or dedicated AI teams. Multikor deploys production-grade ticket routing, response generation, and escalation at 20% of traditional time and 10% of traditional cost. The Delta Intelligence Engine classifies by intent and sentiment — self-healing pipelines keep it running.
Foundation models are widely available. But deploying them securely, cost-effectively, and reliably inside real support workflows remains complex and expensive. Large enterprises assemble this infrastructure over 12-18 months with specialized teams. Most SMBs cannot.
Multikor abstracts that complexity. Our agentic orchestration layer governs how models, constraints, infrastructure automation, and your support data interact in production — so you get trusted, autonomous support workflows without hiring an AI department.
Measurable impact across cost, quality, and customer satisfaction
Reduce support team costs by automating repetitive inquiries while maintaining quality. Free up agents for complex issues.
Provide round-the-clock support without additional staffing costs. Handle inquiries across time zones automatically.
Respond to customer inquiries in seconds instead of hours. Intelligent routing ensures critical issues reach the right team.
Faster, more consistent responses lead to higher customer satisfaction scores. Track sentiment in real-time.
Five-step intelligent automation workflow
From incoming ticket to resolution in five confidence-scored steps
Agent reads email/chat message, extracts intent (billing question, password reset, bug report), checks sentiment (frustrated, neutral, happy), and assigns priority (P1 = angry + account at risk; P2 = billing; P3 = FAQ).
Routes to specialist queue: Technical Support (API errors, integrations), Billing (invoices, refunds), Account Management (expansion, cancellation risk).
Agent searches your Confluence/Zendesk articles, past ticket resolutions, and product docs using semantic search. Retrieves top 3 candidate answers with confidence scores.
Agent drafts reply using approved templates + retrieved KB content, maintaining your tone (formal, friendly, technical). For high-risk topics (cancellations, data requests, security incidents), auto-routes to senior agent for approval before sending.
Agent flags: "I'm 65% confident this solves it. Here's the draft reply and the 3 KB articles I used. Approve/edit/reject?" Your support agent reviews in under 30 seconds, clicks approve or edits. Feedback trains the model.
When a human edits a draft or marks an answer incorrect, the agent logs the correction and updates its response library. Monthly: review top unresolved ticket types and add new KB articles. Coverage improves from 80% to 92% over 6 months.
Your support data flows through three production-grade layers — from ingestion to autonomous resolution.
Ingests emails, chats, voice transcripts, and web forms. Dynamic schema inference maps ticket data to canonical support models automatically.
Constraint-bound reasoning for intent extraction, sentiment analysis, KB search, and response drafting. Deterministic guardrails prevent hallucination.
Replies sent, tickets closed, KB updated. Self-healing pipelines auto-recover from integration failures. 95% auto-remediation.
Each implementation increases domain intelligence. What starts at 80% auto-resolution reaches 95% as the system learns your patterns.
Each function (routing, KB search, response, escalation) has its own agent with domain-specific rules.
Routes work between agents and humans, enforces approval workflows, tracks SLAs.
Encodes your rules — escalation triggers, tone guidelines, high-risk topic routing — so agents stay within bounds.
Agents search your docs, past resolutions, and product articles. Learn from every human edit and correction.
Every action recorded — routing decisions, confidence scores, human overrides — for quality assurance.
Common customer support workflows that benefit from automation
Automatically categorize incoming tickets (billing, technical, account) and route to the appropriate team or queue based on complexity and expertise required.
Generate contextual responses for common inquiries using your knowledge base. Handle FAQs, account questions, and product information automatically.
Search documentation, FAQs, and historical tickets to find relevant information. Surface answers faster than manual search.
Detect customer frustration or urgency in messages. Automatically escalate unhappy customers to senior agents or managers.
Track response and resolution times automatically. Alert teams when tickets are approaching SLA deadlines.
Unified automation across email, chat, and voice channels. Consistent responses regardless of contact method.
Customer Support automation works best for these organizations
Organizations with 10-50 support agents handling high volumes of repetitive inquiries
Software companies with predictable support patterns and extensive product documentation
Online retailers handling order status, shipping, returns, and product questions
Banks, insurance, and fintech with compliance requirements and high support volumes
Deploy at 20% of traditional time and 10% of traditional cost
Integrates with existing systems. No infrastructure replacement. No AI team required.
Knows When to Act. Knows When to Ask.
Production-grade security for regulated support operations
Immutable tenant tagging and per-tenant encryption. Customer data encrypted at rest and in transit with complete isolation.
SOC 2, HIPAA, GDPR, and PCI-DSS enforced programmatically. Automated audit trails for every routing decision and response.
Each customer's support workflows execute in isolated namespaces. No cross-tenant data leakage.
95% of deployment errors auto-remediated. When integrations fail, agents detect, diagnose, and repair without engineering tickets.
Multikor is the production-first alternative to assembling disparate AI tools for support. Faster to deploy. Lower cost to operate. Guardrailed by design. Self-healing in production.
Knows When to Act. Knows When to Ask.
Built for SMBs who need enterprise-grade support automation without an internal AI department.