從想法到
自動化的旅程
三個步驟，一次轉型。看看 AI 如何從理解您的第一個任務，進化到自主運行和持續改善。
Why This Process Matters
Most SMBs bleed margin to 6 operational leaks, not 1
Before seeing how JieGou runs, it helps to see what it fixes. Six connected problems, not one — and single-point automations patch one hole while the bucket keeps emptying through the others.
Unanswered inquiries
Staff turnover cost
After-hours silence
Inconsistent triage
Manual reports
Owner bottleneck
The process below patches all six at once — and each pillar makes the next one stronger. That's the compounding argument. Here's how it runs.
典範轉移
任務優先介面
其他平台從工具和整合開始——您必須先知道要連接什麼才能自動化。JieGou 從您需要完成的事情開始。描述您的目標，平台自動判斷該使用哪些模型、工具和方法。
Who It's For
Businesses that want outcomes, not tools
Every persona below comes with a dollar figure — because "you're bleeding money here" only persuades if we can put a number on it.
You're paying an agency $3-10K/month and want better results at lower cost
$14K–$48K/year in agency overhead (management + delay + markup), not execution
You've been trying to hire a marketing/social media manager for months
Every unhired month = $8K–$15K of output not shipped. Avg SMB hire cycle: 12–16 weeks.
Your customer inquiries go unanswered on evenings and weekends
62% of SMB contact is outside 9–5. 63 hours/weekend of silent exposure.
You know AI can help but don't have time to set up tools yourself
14–42 hrs/week learning/configuring tools vs. 15 min/week reviewing outcomes
You operate across multiple channels and can't manage them all coherently
Cross-channel response-time variance predicts churn better than absolute speed
You need operations that scale without hiring proportionally
Traditional scaling ≈ 1 ops hire per $1–2M revenue. JieGou breaks that ratio.
Who this isn't for
If you already have a mature ops team with standardized SOPs, published SLAs, and automated reporting — JieGou probably won't move the needle. We're built for the gap between "winging it" and "fully automated."
Step 1 · Week 1
Connect 13 channels and 250+ integrations
Our ops team wires up your inbound channels and the systems where your operations already live. You hand off credentials; we do the connection work.
- 13 messaging channels — LINE, Instagram, Facebook, WhatsApp, YouTube, email, Slack, Discord, MS Teams, SMS, web chat, Telegram, phone (Vapi).
- 250+ business integrations — Calendar, CRM, helpdesk, billing, scheduling, e-commerce, file storage. Salesforce, HubSpot, Zendesk, Stripe, Google Calendar, Notion, and many more — via Composio MCP.
- Vertical-aware connections — Open Dental for medical/dental, Clio for legal, your PSA for IT/MSP, your shop calendar for home services. We bring industry-specific connectors so the AI talks to your real systems.
Channels we manage on your behalf
Step 2 · Week 1
AI agents that sound like you
We train per-service agents on your brand voice and your knowledge base. The same customer who chats with us on LINE feels like the same business when they email — because the agents share context, not just credentials.
- Brand-voice extraction — We sample your existing replies, blog posts, and customer conversations to learn your tone. The agent escalates instead of inventing when it does not know.
- Knowledge bases auto-built — Upload PDFs, paste URLs, point us at your help center. Documents get chunked, embedded, and retrieved through RAG so every reply is grounded in your real source material.
- Per-service specialists — Customer-engagement, content publishing, scheduling, compliance, internal-ops, document-ops — each managed-agent service runs as its own specialist with the right tools and the right escalation pattern.
What we ingest
- • Your existing customer reply transcripts
- • Brand guidelines, FAQ, help center, blog
- • Industry-specific protocols (medical, legal, etc.)
What you get
- ✓ Agents that match your tone, not generic ChatGPT voice
- ✓ Replies grounded in your actual knowledge — no hallucinations
- ✓ Escalation to a human when the agent is uncertain
Step 3 · Week 2 → Ongoing
Graduate from shadow review to autopilot, at your pace
Nothing reaches your customer without you seeing it first. You stay in shadow mode until you trust the AI for that service — then flip individual services to autopilot. Per-service granularity means you can be autopilot on FAQ replies and shadow on billing inquiries simultaneously.
Mode 1 · Default
Shadow
AI drafts every reply; your team reviews and approves before send. Nothing customer-facing without explicit human OK.
Typical: 2-4 weeks at 90%+ approval rate before graduating.
Mode 2 · Per-service
Assisted
AI auto-sends low-risk replies (clear FAQ matches, status updates). Holds anything ambiguous for human review.
Most customers stabilize here — humans handle ~10-20% of inquiries.
Mode 3 · Per-service
Autopilot
AI sends everything for that service. Humans monitor metrics + audit log, intervene only on edge cases or quality dips.
Reach this when accuracy compounds — typically 2-3 months in.
Each service (customer-engagement, content, scheduling, etc.) graduates independently. Stay shadow on what's still risky; flip autopilot on what's proven. You're never forced into all-or-nothing.
Always On · From Day 1
Three safety nets, before and after autopilot
AI will be wrong sometimes. The architecture assumes that and provides three independent ways to catch + correct it. Approval gates, audit logs, kill switch — active in shadow, in assisted, and in autopilot.
Approval gates
Any workflow can declare an approval gate at any step. The agent pauses, notifies via email/Slack/in-app, the approver sees full context (input, draft, prior steps), and approves, edits, or rejects. Routable to specific people, roles (Owner/Admin/Manager), or departments. Single-approver and multi-approver policies both supported.
Audit logs
Every AI action is logged with input, output, model version, timestamp, and HMAC-signed for tamper detection. If something looks wrong three months in, you can reconstruct exactly what happened, when, and why. SOC 2 / GDPR / HIPAA-aligned retention; configurable per account.
Kill switch + feedback loop
One click flips a service back to shadow or disables it entirely. Plus: when you reject or edit an AI draft, that signal feeds the agent's memory so the same mistake doesn't repeat. The system learns from corrections, not just from successes.
What you get every month
Concrete deliverables, not platform-shopper feature checklists. This is what shows up in your inbox / dashboard / Slack as a result of the system above.
Every customer inquiry answered in minutes — not hours
Across LINE, Instagram, Facebook, WhatsApp, email, web chat, phone, and more. 24/7, including weekends and holidays.
Content published on schedule, in your brand voice
Blog posts, social media, email newsletters — created by AI, reviewed for quality, published automatically.
Weekly insights report with AI analysis
Not a PDF of vanity metrics. Actionable insights: what's working, what's not, what to try next. With trends and benchmarks.
Proactive alerts before problems escalate
Sentiment drops, complaint spikes, missed SLAs — you hear about it from us before you hear about it from customers.
Operations that get better every month
AI learns from every interaction. Response accuracy improves. Content engagement increases. The compound effect is real and measurable.
A dedicated success manager
One person who knows your business, reviews your performance, and proactively suggests improvements. Not a ticket queue.
How managed AI ops compares
72% of SMBs already use managed service providers. They don't want another tool — they want outcomes. Here's how JieGou compares to the alternatives.
| JieGou Managed | Traditional Agency | AI Tools (DIY) | |
|---|---|---|---|
| Monthly cost | $1.5K–$10K, all-inclusive | $3–10K + tool stack | $200 + your team's time |
| Setup time | < 1 week to live | 2–4 weeks | Weeks to months |
| Coverage | 24/7/365, every channel | Business hours only | When your operator is awake |
| Time you spend | 15 min/week reviewing reports | Weekly briefs + revisions | 14–42 hrs/week configuring |
| When staff turns over | AI keeps your knowledge | New AE relearns from scratch | Your config knowledge walks out |
| Improves over time | AI compounds with every reply | Resets with staff turnover | Only if you invest more time |
Frequently asked questions
How onboarding, approvals, and the shadow→autopilot graduation actually work