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Lower cost-to-serve with AI process redesign.

Redesign support operations so AI handles repeatable demand, lowers cost-per-ticket, and protects CSAT through governed automation and better handoffs.

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The problem

Cost per ticket keeps climbing with no lever to pull

  • Cost per ticket keeps climbing

    Headcount scales with volume. There is no lever to pull that doesn't hurt quality or budget.

  • No visibility on what's automatable

    Teams know AI could help but can't quantify how much — so nothing moves.

  • Failed automation attempts

    Previous chatbot deployments disappointed. Now there's internal resistance to trying again.

  • Savings that do not stick

    One-off automation wins do not compound. Without governance and measurement, cost per ticket creeps back as volume and complexity grow.

How it works

从信号到成果——治理内置

Step 1

连接与治理

接通你的系统、政策与知识，让 AI 在你的规则之内工作，而不是绕过它们。

Step 2

自动化与辅助

让工作流上线：分流、起草、解决或升级，全程带完整上下文与审计追溯。

Step 3

度量与改进

跟踪运营 KPI、质量与风险——再和你的团队一起调优剧本。

流程序列会适配你的工具、渠道与风险态势。

Lower cost-to-serve with AI process redesign.

What's included

What you get when you run this with Thinkia

A governed layer across data, workflows, and handoffs—so teams ship safely and scale with metrics.

Automation opportunity audit

Maps your ticket categories and identifies which are safe to automate and which need humans.

Tier-1 full automation

Resolves FAQs, order status, account queries and standard requests without agent involvement.

Cost-per-ticket dashboard

Real-time view of AI vs. human handling cost across channels.

Deflection rate tracking

Measures exactly how much volume is being kept off the human queue.

Graceful fallback

When AI can't resolve, it hands off cleanly — no dead ends, no frustrated customers.

FinOps for support

Model spend visibility so AI costs don't silently replace headcount savings.

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Results

What changes when this runs in production

Results vary by ticket volume, complexity mix and existing tooling.

40–60%

Range across comparable deployments at scale

Orientative — confirmed in discovery; depends on the starting point.

65%+

Share of inbound queries resolved without human agent

Orientative — confirmed in discovery; depends on the starting point.

<6 months

Typical time to recover implementation cost

Orientative — confirmed in discovery; depends on the starting point.

How we work

From cost pressure to containment—without burning out your agents

Cost & quality baseline

Week 1–2

Handle time, cost per contact, rework, and CSAT are baselined by segment and channel.

Automation envelope

Week 3–5

Which intents can self-serve, which need draft-and-review, and which stay human-only—documented.

Savings pilot

Week 6–9

A slice of volume runs on the new model; finance validates unit economics and risk.

Optimise & expand

Week 10+

Progressive automation with quality gates; staffing plans update with transparent assumptions.

Outsourcing mix and regulatory lines affect what can automate; scope stays conservative first.

Ideas, trends, and tools to stay ahead

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Get started

Ready to scope this for your context?

We start with a focused session—no commitment—to map constraints and a sensible path.

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