01SaaS
Draft replies and live account context in every conversation — a production support copilot with human review in the loop.
Timeline · 7 weeks
AI-drafted reply
01Challenge
The support team was buried in repeat tickets. Agents spent more time hunting for account context across helpdesk, CRM and billing tools than writing useful replies.
Previous experiments with chatbots collapsed under edge cases — no grounding in real customer data, no audit trail, and no way to measure whether answers were actually correct.
02Approach
We mapped the highest-volume ticket types and defined what a “correct” reply meant for each — with citations back to source systems.
An LLM agent drafts replies while a RAG layer pulls live account context into the conversation. Agents review, edit and send — with full visibility into what the model used.
AI UX flows were designed so the copilot feels like a senior teammate in the inbox, not a black-box bot bolted onto the side.
03Results
First-response time dropped 38% after launch.
Agents stopped context-switching across tools for the majority of routine tickets.
The team left with the full repo, eval hooks and deployment access — no platform lock-in.
04Project details
Deliverables
- AI strategy
- AI UX flows
- LLM agent
- RAG
Stack
- OpenAI
- LangChain
- Pinecone
- Next.js
- Helpdesk API
- Industry
- SaaS
- Timeline
- 7 weeks
Related services
- 03Artificial IntelligenceAI applications, conversational agents and automation for enquiries, workflows and repetitive work.
- 02SaaS DevelopmentProduction-grade SaaS with billing, auth and multi-tenant architecture.
- 02AI ChatbotsCustom AI chatbots for customer questions, lead intake and team knowledge.
- 04Custom AI SolutionsRAG, document intelligence and problem-specific knowledge systems — evaluated before release.
06Contact
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