Autonomous AI Support Agents

Context

A fast-growing B2B SaaS team was drowning in support volume. New customers were onboarding quickly, but the support team was stuck answering the same questions, manually triaging issues, and struggling to keep up with SLAs.

They wanted to ship AI support agents that actually felt on-brand, understood product-specific context, and knew when to gracefully hand off to a human.

Challenges

• High ticket volume: Repetitive "how do I?" questions were overwhelming the support queue.
• Fragmented knowledge: Docs, Notion pages, and internal runbooks were scattered across tools.
• Noisy data: Historical tickets and chat logs were inconsistent, messy, and partially labeled.
• Risk of hallucinations: Leadership wanted AI, but not at the cost of wrong answers to critical workflow questions.

Why Sybil / 0xSero

The client chose Sybil Solutions (built and run by 0xSero) because they wanted someone who:

• Actively works with LLM tooling and agentic workflows in production
• Can span both backend architecture and UX details of an AI assistant
• Moves fast without sacrificing the boring-but-important things: logging, safety rails, observability

Solution

We designed and shipped a multi-layer AI support system:

• Retrieval-augmented generation (RAG):
  • Ingested docs, changelogs, onboarding flows, and internal runbooks
  • Normalized content into a single vector store with ownership + freshness metadata
• Conversation engine:
  • Built a Node.js API that orchestrates the LLM, knowledge base, and safety checks
  • Added structured tools for account lookups, plan limits, and feature flags
• Human-in-the-loop routing:
  • Clear rules for escalating complex, emotional, or high-risk conversations
  • Agents annotate tickets with suggested responses and context when escalating
• Feedback loops:
  • Inline thumbs-up/down and free-text feedback on AI answers
  • Daily digest for the support lead with "risky" answers and improvement candidates

Results

Within the first 60 days of rollout:

• ~70% of new inbound chats are resolved entirely by AI agents
• Median first-response time dropped by ~80%, from minutes to seconds
• Customer satisfaction (CSAT) on AI-resolved conversations improved by 18 points
• Support leads now spend their time on edge cases and complex accounts, not password resets

Stack & Implementation Details

• Backend: Node.js API layer with typed request/response contracts
• LLM: Provider-agnostic orchestration, so the client can switch models without rewrites
• Data: Vector database for semantic search + relational store for guardrails and metadata
• Instrumentation: Structured logging, tracing, and redaction of sensitive fields
• Integration: Embedded into the existing chat widget and admin dashboard

What This Means for You

This project is a good example of what Sybil Solutions and 0xSero like to build: practical AI agents that are deeply connected to your product, safe by default, and measured by real business outcomes—not just model benchmarks.