The ticket is half the story. The account is the rest.
Support AI that only reads the ticket gives generic answers. The leverage comes from AI that knows the account history, contract, and status before it responds.
CarbonSilicon Labs builds support automation that combines ticket content, account context, and policy — drafting answers, routing issues, and escalating the ones that matter, with a person in the loop where it counts.
AI customer support automation makes support faster without letting bots freelance. Every engagement answers:
History, contracts, renewal status, and usage pulled in before the AI responds.
Answers anchored to your real policies, not the model's guesswork.
Issues sorted by severity, account value, and the right owner.
The signals that pull a human in before something breaks.
Drafted replies and summaries that make reps faster, not redundant.
A record of what the AI suggested, sent, or escalated.
We pick the support workflow worth automating first, usually agent assist before automation.
We define the context, the policy grounding, and the escalation rules.
We connect the account context and engineer the drafting and routing.
We launch with review paths and audit live from the first ticket.
We tune the routing, widen the coverage, and move toward automation as trust grows.
Support AI that combines the ticket with account context, policy, and human review — drafting answers, routing issues, and escalating the ones that matter. The goal is faster support that stays accurate, not a bot left to freelance.
An agent that takes scoped support actions across your systems — pulling account context, drafting a reply, routing or escalating — within limits you set, rather than just chatting.
AI that assists your reps instead of replacing them: it drafts answers, summarizes the account, and suggests next steps before the rep sends. It is the safer first step before any customer-facing automation.
We usually start with assist — the AI drafts and the rep approves — because it captures most of the speed with far less risk. Direct customer-facing automation comes later, on low-risk interactions, once trust is established.
By pulling the customer's history, contracts, renewal status, and product usage into the answer before it responds. That is the difference between a generic reply and one that actually fits the account.
By triaging on severity, account value, sentiment, and policy triggers, then sending each ticket to the right owner. Important issues stop getting buried under volume.
It reads severity, sentiment, deadlines, and escalation signals to flag the tickets that cannot wait — surfacing the at-risk account before it churns instead of after.
We ground answers in your actual policy documents, add review paths for sensitive responses, and set guardrails on what the AI can state. It answers from your policy, not its own guess.
Yes. The AI condenses prior tickets, account context, and the current issue into a short brief, so the rep starts with the full picture instead of reading the whole thread.
Sensitive, high-risk, or relationship-critical interactions — escalations, exceptions, anything where the wrong answer carries real cost. The AI handles the volume; people handle the moments that matter.