Solutions / Customer Service & Success

Agents that close tickets. Teams that close accounts.

Routine tickets shouldn't reach a human. Tray.ai runs the agents that deflect them — and surfaces the full customer context for the ones that do.

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ticket deflection in first two weeks at Apollo
40%
CSAT maintained at Apollo after agent deployment
4.95 / 5
monthly active users on Life360's platform, orchestrated by Tray
88M
Slack channels consolidated into one agent entry point at Life360
2,500+

The tickets that shouldn’t reach a human are the ones that do.

Password resets. License provisioning. Order status. These are solvable before a human opens the ticket — if the agent has the right context and the right connections. Apollo built that agent on Tray.ai and hit 40% deflection in two weeks at 4.95/5 CSAT. Life360 gave 88M monthly active users a single orchestrated entry point across 2,500+ Slack channels. The platform that runs the agents runs the orchestration layer beneath them — one vendor, one governance model.

What service and success teams build on Tray.ai

AI agents that resolve, not just respond

Apollo hit 40% ticket deflection in two weeks at 4.95/5 CSAT. Life360 consolidated 2,500+ Slack channels into one agent entry point.

Full customer context before the conversation starts

CRM, billing, support history, usage — unified and surfaced automatically. The agent sees what the rep needs before the ticket opens.

Routing that doesn't miss

Intelligent iPaaS routes to the right queue, tier, and team automatically. No manual triage, no tickets sitting unassigned.

Governance on every agent action

Approval gates on sensitive operations. Audit trails on every tool call. RBAC down to the individual user.

40% ticket deflection in first two weeks

“The moment that really stuck with me was when one of our Directors of Product got locked out of his laptop on a Saturday night. The agent had him back online in seconds. That's when everyone on the team realized this isn't just a pilot. It's real value.”

IT Lead
Apollo

Peers running this play

Teams the customer service & success role tracks for fit, scale, and proof.

Apollo 40%

Tech / SaaS

Apollo's IT team was overwhelmed by tickets that scaled linearly with headcount. Existing AI vendors offered rigid, prebuilt agents — Apollo …

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Life360 88M

Tech / SaaS

Life360 runs a consumer platform with 88 million monthly active users and a growing internal team. Early attempts at single-purpose bots lef …

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Auctane 356%

Logistics

Auctane — parent company to ShipStation, Stamps.com, ShipEngine, and more — was onboarding up to 30 users a month through a mix of manual da …

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Where this maps in your work

The use cases this role pursues first on Tray.ai.

Frequently asked questions

How do agents handle tickets that need a human? +

The pattern that works — agents triage every request and only respond when they can fully or partially resolve it. When they can't, they escalate cleanly with full context already attached. Apollo's agent converts unresolvable requests into tickets with all the thread context, so the human starting the handoff isn't starting from scratch.

Where does the customer data come from? +

Wherever it lives — CRM, billing, usage, support history. Tray's 700+ connectors pull the customer record from every system that knows something about them. The agent sees a unified view before the conversation starts. Zuora's teams get a full account brief in 5 minutes instead of 23 hours on this pattern.

What's the governance story for customer-facing agents? +

Agent Gateway gives you managed MCP with RBAC, rate limits, observability, and audit logs on every tool call. Approval gates on sensitive operations — password resets, account changes, billing modifications. The governance model that satisfies enterprise compliance review is how the platform works, not a layer added after.

How fast can we get a first agent into production? +

Apollo's first agent was in production in weeks. The pattern that works is starting with IT service desk — lower customer-facing risk, high-volume, clear deflection metric — then expanding to customer-facing use cases once the team is confident in the platform's behavior.

See how Customer Service & Success teams use Tray.ai

Walk through your real systems with someone who's been there.

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