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在不增加员工人数的情况下扩展您的团队。

部署人工智能工作人员来端到端处理定义的角色（从面向客户的交互到后台任务），其一致性、可用性和速度是人类团队无法单独维持的。

See it in action 了解问题

The problem

When headcount can't keep up with demand becomes the norm

  • Headcount can't keep up with demand

    Growth requires more people. Hiring takes months. Onboarding takes more. The gap between demand and capacity never closes.

  • Repetitive roles with high turnover

    The roles most needed at scale are the ones with the highest churn. Institutional knowledge walks out the door constantly.

  • Inconsistent performance across shifts and regions

    Quality depends on who's working, when, and how much training they've had. Standardisation is hard to enforce at scale.

运作方式

Digital workers for defined roles—measured like any other team

步骤 1

Design the role

Inputs, outputs, tools, and escalation paths are specified so the worker scope is explicit.

步骤 2

Operate with oversight

Humans approve edge cases; telemetry shows throughput, quality, and cost per task.

步骤 3

Iterate safely

Prompts, tools, and data access change through change control—same rigor as software releases.

Works best when wired to BPMN or runbooks your ops team already trusts.

在不增加员工人数的情况下扩展您的团队。

包含内容

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.

Role-based AI worker deployment

AI workers configured for specific functions (support, onboarding, data entry, outreach)

Digital Human interfaces

lifelike conversational AI for customer-facing roles that require a human presence

Back-office AI workers

non-customer-facing agents that handle data processing, classification and workflow execution

Onboarding and knowledge transfer

AI workers learn from existing documentation, recordings and workflows — not from scratch

Performance monitoring

tracks accuracy, throughput and quality per AI worker with configurable review thresholds

Human-AI teaming

clear handoff protocols so AI workers and human teams collaborate without confusion over ownership

技术提供 Thinkia Synapse

成果

What changes when this runs in production

4×

Volume of defined tasks handled per equivalent human FTE

24/7

No shift gaps, no sick days, no onboarding lag

4–6 weeks

From scoping to first production-ready AI worker

Results vary by role complexity, data availability and integration requirements.

合作方式

From headcount fog to skills, capacity, and scenarios leaders can act on

Workforce data

Week 1–2

HRIS, projects, and finance actuals are reconciled to roles, sites, and cost centres.

Forecast & gaps

Week 3–5

Hiring, attrition, and capability gaps are modelled with leadership assumptions explicit.

Leadership pilot

Week 6–9

Executives use the cockpit for one planning cycle; narratives and decisions are retrospected.

Embed in FP&A/HR

Week 10+

Rolling forecasts and workforce plans share definitions; sensitivity to strategy shifts is routine.

Organisation complexity and contractor mix affect data trust; phased by business unit.

开始

Ready to scope this for your context?

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

See it in action Explore AI Solutions