68%
Tickets resolved autonomously
$500K
Pre-seed raised post-launch
50
Beta users in first 2 weeks
40%
Support cost reduction
“Alex” (name changed) runs a B2B SaaS product with around 800 paying customers. His support team of four was processing over 400 tickets per day — the majority of which were repetitive questions already answered in his documentation. He was spending $18,000/month on support staff just to answer the same 50 questions in different ways.
He had evaluated off-the-shelf solutions like Intercom's AI and Zendesk's bots, but none could learn from his existing ticket history or understand the nuance of his product well enough. He came to us wanting a purpose-built AI agent — with no technical co-founder and no idea where to start.
“I was quoted $180K by one agency and told it would take 9 months. Idea to MVP scoped the same project in a week and shipped it in six.”
— Alex, Founder (B2B SaaS, anonymized)
We designed a multi-agent LangChain system with three specialized agents working in concert: a classification agent (routes tickets by type), a resolution agent (answers using RAG over the knowledge base + ticket history), and an escalation agent (flags tickets that need human review with a confidence score).
Discovery & Architecture
Scoped the system, mapped the ticket taxonomy, set up Pinecone, ingested historical data, finalized the agent chain design.
RAG Pipeline & Core Agent
Built the retrieval-augmented generation pipeline. Tested accuracy across 500 historical tickets — achieved 74% match rate before tuning.
Multi-Agent Chain & Zendesk Integration
Built classification and escalation agents. Integrated with Zendesk API bidirectionally. Implemented confidence scoring.
Prompt Engineering & Accuracy Tuning
Iterated on prompts with the founder reviewing 200 test responses. Resolution accuracy improved from 74% to 91% on the test set.
Admin Dashboard & Staging
Built the Next.js admin dashboard. Set up staging environment, ran end-to-end tests with the support team.
Production Launch & Handover
Deployed to production. Ran parallel (human + AI) for one week to validate. Delivered documentation, async training session, and 30-day support.
68%
of incoming tickets resolved autonomously, with no human intervention
$500K
pre-seed funding secured — investors cited the AI system as a key differentiator
50
beta users onboarded in the first 2 weeks post-launch
40%
reduction in monthly support costs — from $18K to ~$10.8K/month
<2 min
average ticket response time, down from 4.5 hours
4.8/5
average CSAT score on AI-resolved tickets
The biggest unlock wasn't the model — it was the data pipeline. Ingesting 14,000 real historical tickets (not just documentation) gave the RAG system the context it needed to match how actual customers ask questions, not how the founder expected them to.
The confidence-based escalation was also critical. Rather than having the AI guess on hard questions, it hands off with a pre-drafted answer — which means even escalated tickets take less time to resolve. This is what pushed the founder's support team buy-in from skeptical to enthusiastic.
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