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For the Head of Customer Experience

Better numbers on every metric you report

Containment, average handle time, first-contact resolution and CSAT all move for the same reason: the customer stops repeating themselves, and your agents stop searching for answers.

  • Start with one module
  • No rip-and-replace
  • Hosted in the region you choose
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Illustrative interface. The figures are sample data from a demo tenant — not a benchmark, a containment rate or any other performance claim. The screen shows an OptiML analytics overview for a customer-experience leader: containment, average handle time, first-contact resolution and quality-score tiles above a weekly conversation chart splitting volume contained by AI agents from volume handed to human agents.

What the platform measures for you

Not a sample, not a survey response rate, and not four tools that count the same thing three different ways. One conversation model underneath every number on this page.

15 quality dimensions scored on every interaction, AI-handled and human-handled, voice and chat
76 report definitions across every channel at once, exportable in six formats
8 channels on one conversation model, so first-contact resolution means the same thing everywhere

What you're measured on

Six numbers decide whether the year went well. Each one moves for a mechanical reason, and the right-hand column names that mechanism instead of implying it.

Each metric a head of customer experience reports on, and what OptiML changes about it
Metric What OptiML changes
Containment AI agents resolve end to end, grounded in your own content, rather than deflecting to a form
Average handle time The copilot has the answer before the agent finishes reading the question; context arrives with the handover
First-contact resolution Cross-channel history means the second contact isn't a repeat of the first
CSAT / NPS Surveys run automatically post-interaction and feed the improvement loop
Cost per contact One platform instead of six licences, with a provider router that puts a floor under AI spend
Quality coverage 100% of interactions scored on 15 dimensions, not a manual sample

What changes in your week

  • You stop reconciling numbers from four tools that count things differently.
  • Coaching conversations start from evidence rather than from the three calls someone had time to review.
  • The emerging issue surfaces in topic mining in week one instead of in the monthly review.
  • The board question “what is AI doing for us” has a report behind it.

See quality management

Illustrative interface. Scores, counts and trends are sample data, not a performance claim. The screen shows OptiML quality scoring for a single voice conversation: six of the fifteen evaluation dimensions with their scores, a note that nine more dimensions were scored, a generated coaching plan, and a coverage panel showing that every interaction that week was scored rather than a manual sample.

Before it reaches a customer

These are the four things a CX leader is asked about in the first review. Each one is answered by how the platform is built, not by a roadmap slide.

Grounded, or it says so

A groundedness gate blocks an ungrounded answer before the agent speaks, and every answer carries a citation to the source it came from.

Scored, not sampled

Every interaction is evaluated on 15 dimensions, AI-handled and human-handled, voice and chat. The result becomes a coaching plan.

Run by us, in your region

OptiML runs in OptiML Cloud, provisioned in the region you choose and operated, patched and scaled by RMT against a 99.9% monthly uptime commitment.

Live in 6–11 weeks

Standard time to go-live is 6–11 weeks across five phases, and you can start with one channel or one queue.

Objections you'll hear, answered

These three come up in every evaluation, usually from someone who was right to be sceptical the last time. Each answer is about architecture, not enthusiasm.

“Our customers hate bots.”

They hate menu trees and scripted deflection. A voice agent is a different experience if the caller can interrupt it mid-sentence, if it answers from your actual policy with a citation, and if it hands over to a human with the full context. The groundedness gate means it says “I don't know, let me get someone” rather than inventing.

“We tried AI and it hallucinated.”

Ungrounded AI does. Retrieval-grounded AI is a different architecture, not a better prompt: a groundedness gate before it speaks, a hallucination grader, a citation auditor and 15-dimension scoring on every answer.

“This is a two-year migration.”

Standard go-live is 6–11 weeks across five phases. You can start with one channel or one queue and expand, on the same platform.

Where this goes next

The same argument, read from the seat next to yours. The customer story will sit here once a named customer has approved it in writing.

We publish outcome figures only with the customer's written approval, so this slot stays empty until one is signed off. In the meantime the mechanism above is the honest version.

Until thenBring your own scenario and we will run it live

Talk to our team

Customer quotes and outcome numbers need written approval from the named customer, so this slot stays visibly empty until one exists. An invented one would be worse than the gap.

The platform underneath

Everything on this page is the OptiML CX Platform configured for Head of CX, not a separate product. These are the modules it leans on.

Book a working session

See it handle one of your real conversations

Bring a call recording, a policy document or a WhatsApp thread from your own operation. We ground an agent in it and run it live on the call — not a canned demo.

  • 30 minutes
  • A working agent grounded in your content
  • No slide deck unless you want one

Book a demo

30 minutes, on your own content. No slide deck unless you want one.

or reach us directly

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