Tier-1 queries resolved automatically — complex cases escalated with context
Managed services provider, B2B, structured support tiers with SLA requirements
The situation before
Tier-1 support was a bottleneck. A large share of inbound tickets — password resets, connectivity checks, standard configurations — were going through the same queue and triage process as genuinely complex issues. SLA timers started running from first ticket receipt regardless of complexity, which put pressure on the team for tickets that shouldn't require their time at all.
What was built
An agent that sits at the front of the support queue and handles all tier-1 classifications directly — running through a structured resolution workflow (guided troubleshooting, configuration checks, account resets) using integrations to the client's systems. Tickets resolved at tier-1 are closed with a full activity log. Tickets that don't resolve are escalated to the appropriate tier-2 specialist with a complete diagnostic summary pre-attached — reducing the time a senior engineer spends re-diagnosing from scratch.
See it in action
Demo coming soon
A walkthrough of this scenario — how the agent runs, what the output looks like, and how the handoff to your team works. Content to be added before launch.
What changed
A meaningful proportion of inbound tickets were resolved without human involvement. Tier-2 engineers reported that escalations arrived with sufficient context that resolution time dropped. SLA compliance improved. [Specific T1 resolution rate and SLA improvement — metric TBD]
More in this category
Support inbox handled in the team's voice — drafts ready before staff arrive
E-commerce brand, 8-person operations team, high support volume relative to team size
The support team spent a significant portion of every morning working through an overnight inbox backlog — mostly order status questions, return requests, and shipping queries that required checking two or three internal systems and writing a consistent response. The work was necessary but not where the team added value.
Website chat that answers from the actual documentation — not generic AI
SaaS company, product-led growth, high volume of pre-sales and onboarding questions via website chat
The chat widget on the website was either ignored (no one available) or handled by a sales or support rep who was frequently pulled away from other work. Pre-sales questions were being lost, and onboarding questions were creating unnecessary support load when the answers existed in their documentation.