Retrieval-augmented responses
Embeds knowledge base articles with Supabase Vector to ground every answer in quoted sources.
AI Support Copilot
Fine-tuned LLM copilots that resolve tier-one tickets in seconds.
The AI Support Copilot blends vector search, guardrails, and human handoff to automate repetitive support flows while keeping humans in control.
Problem · Approach
The challenge
Support teams spent 40% of their time triaging repetitive questions with inconsistent tone and slow resolution times.
How we solved it
Fine-tuned an open-weight LLM with retrieval-augmented generation, moderation layers, and analytics that surface automation opportunities.
Impact at a glance
Resolution time
38s
Average response for tier-one tickets after automation.
Human handoff
<12%
Escalations thanks to guardrails and confidence thresholds.
Agent hours saved
+2 days
Per agent per month reclaimed for deep work.
What made the difference
Moderation guardrails
Custom safety filters and confidence scoring prevent hallucinations and escalate seamlessly.
Analytics loop
Tracks automation coverage, CSAT, and suggestions for new scripted flows.
Delivery timeline
Design sprint
Mapped support journeys and identified high-volume intents for automation.
MVP + evals
Ran offline evals across 2k historical tickets before enabling live traffic.
Production
Shipped live evaluation metrics, feedback review tools, and handoff dashboard.
Results
Tier-one resolution time dropped to 38 seconds on average, and teams reclaimed two workdays per agent each month.