Customers ask the same questions every day. This use case shows how Pilotstarter books the right experts and runs a pilot to production so your team gets an assistant that answers common questions and helps agents in real time.
Problem: Customers wait too long for answers. Your knowledge lives in many places. Agents repeat themselves.
Solution: An assistant powered by a Large Language Model (LLM) that reads your help articles and past tickets using Retrieval-Augmented Generation (RAG) so answers are grounded and cite sources.
What Pilotstarter does:
Captures your goals (top questions, channels, success metrics).
Finds and introduces experts (double opt-in), handles Non-Disclosure Agreement (NDA), scheduling, and payment.
Creates a small project plan (micro Statement of Work (SOW)), chases tasks, and tracks results to launch.
Which AI experts we bring & why (just a few):
RAG/LLM Engineer: builds the “read our docs and answer with citations” piece.
Integration Engineer: connects your help desk (e.g., Zendesk/Intercom) and chat widget.
Prompt Designer: sets safe reply rules and tone of voice.
Pilot scope (typical):
Ingest 200–500 articles + recent tickets; enable chat + email.
Add Single Sign-On (SSO) for staff; set escalation when confidence is low.
Go live to a small % of traffic first.
What we measure (attribution):
Average Handle Time (AHT), First-Contact Resolution (FCR), Customer Satisfaction (CSAT), % of questions answered without an agent.
