Your AI Workflow Automation Partner for Hong Kong SMEs

Zistrat AI focuses on turning repetitive, scattered, error-prone internal admin into more stable AI-assisted workflows. The goal is not to pile on tools, but to help your team move information, drafts, approvals, and reports with less manual effort.

Customised for Your Reality

We work through your actual processes and constraints step by step, not off a template.

Built to Be Maintained

Solutions include clear rules, handoff points, and room for future optimisation so they work long-term.

Results-Oriented

The focus is on less duplicate entry, cleaner handoffs, faster quote preparation, and clearer reporting.

Where We Usually Start

The most common areas of focus are admin handoffs, customer records, quote preparation, follow-up workflows, and reporting.

Understand the Workflow Before Automating

We start by mapping how information currently moves between people, tools, approvals, and reports before deciding what AI should handle.

Hong Kong SME Context

Our content, enquiry scenarios, and workflow cadence are grounded in how Hong Kong SMEs actually operate — not adapted from overseas templates.

Human-Controlled AI

We design workflows where AI prepares the work and your team approves sensitive updates, outgoing messages, and business decisions.

Focused on Common Hong Kong SME Scenarios

Whether retail, trading, services, manufacturing, or logistics — the core challenges usually revolve around enquiries, data flows, and repetitive admin.

Retail
Trading
Services
Manufacturing
Logistics

Our Approach Prioritises Clarity and Practicality

We prefer breaking complex problems into steps that can go live, be tested, and be continuously adjusted.

01

Workflow Audit

Understand source tools, data flow, approval points, follow-up cadence, and the most time-consuming manual steps.

02

Phased Implementation

Start with one internal workflow, then connect CRM, quoting, reporting, or other automation components only when useful.

03

Continuous Optimisation

Monitor real usage, adjust extraction rules, refine prompts, and improve handoff arrangements as the workflow matures.

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