AI Systems
AI Customer Interaction & Recommendation Assistant
A multi-agent assistant built on knowledge retrieval, workflow orchestration, CRM integration and explicit handoff logic.
Context
A high volume of customer inquiries with a large share of repeated questions and a need for personalized recommendations.
Problem
Operators spent time on the same answers and on hunting context across systems; answer quality depended on the specific person and their experience.
Constraints
Accuracy over coverage, mandatory handoff to a human on ambiguity, personal-data protection.
Architecture
An orchestrator with retrieval, reasoning and tool agents; handoff to an operator is the terminal handler, not an emergency exit.
AI layer
Hybrid routing: a cheap model for intent classification, a strong one for critical answers and recommendations where the cost of error is high.
Event model
Inquiry → intent detection → context assembly → answer or recommendation; on insufficient confidence the workflow goes to a human with preserved context.
Integrations
CRM, knowledge base and interaction history behind a normalized context layer shared by all agents.
Automation flows
Auto-answer for routine cases, a draft for the operator on hard ones, enrichment of the customer record after the conversation.
Infrastructure
Per-step context budgets, caching of stable request parts, timeouts and fallback strategies on each agent.
Observability
Tracing of which context fragment influenced the answer, groundedness metrics, and the share of inquiries handed to a human.
Results
Repetitive operator workload dropped, answer quality stopped depending on the individual, response time decreased.
Lessons
Value is in orchestration and context, not a single model; an honest handoff on low confidence mattered more than “always answer”.