AgentPing for customer support

Trust the answers
your AI support gives.

Deflection bots and triage agents answer customers around the clock, and a confident wrong answer still returns success. AgentPing scores what your support AI actually says, tracks what it costs at volume, and pages you when it goes down, so quality is something you measure, not something you hope for.

Track your first AI workflow
summariser · judge score↓ 4.2 to 3.8
  • cites a source pass
  • answers the question pass
  • stays on policy fail

What support teams need to watch.

Answer quality first, then the cost behind the volume and the uptime customers depend on. One run record per reply carries all three.

Answer quality and policy

Score every reply against your rules so a confident wrong or off-policy answer is caught, not shipped.

summariser · judge score↓ 4.2 to 3.8
  • cites a source pass
  • answers the question pass
  • stays on policy fail

Explore Verify →

Cost at support volume

See cost per bot, customer and conversation, so a prompt change does not quietly double cost per ticket.

spend · this month↑ on budget
$5,382
spent
$0.094
cost / successful run
content-writer$2,189
research-agent$1,474
support-triage$685
email-classifier$262

Explore Spend →

Bot uptime

Know the moment a support bot stops answering or stalls, before the queue backs up and customers notice.

support-triage · schedulelive
support-triage missed its 14:00 run paged on-call · last ok 13:00 · 247 runs clean before

Explore Pulse →

How do we know our AI support bot gives good answers?
AgentPing scores production replies two ways: deterministic checks on every answer at no model cost, and a plain-English rubric scored by an LLM-as-judge on a sample. You get a quality number per bot, trended over time, so a drop shows up before it shows in your CSAT.
Can we catch off-policy or wrong answers?
Yes. Write the rules your bot must follow as a rubric, in plain English, and AgentPing flags the replies that break them. The danger with support AI is a confident wrong answer that still returns "success"; quality scoring is what watches for it.
What does high-volume support AI cost us?
Every reply is priced server-side and attributed to the bot, customer and conversation that spent it. At support volume the bill moves fast, and a prompt change or a retry loop can quietly double cost per ticket. AgentPing shows the cause, not just the total.
How do we know the bot is actually up?
Pulse watches the bot like a production service: a workflow that stops answering, stalls or misses its schedule raises an incident and pages the channel you watch, so an outage is something you catch, not something a customer reports.

Stop a confident wrong answer reaching your customers.

Score one support bot's replies, write your policy as a rubric, and watch quality, cost and uptime in one place within minutes.

Track your first AI workflow

For sales and RevOps → For e-commerce and retail → All features →