Let's talk numbers. In my e-commerce operation, 6 out of every 10 customer messages are now resolved without a single human touching them. No canned auto-replies. No "please wait for an agent." Actual resolution — the customer gets their answer, the ticket closes, and nobody on my team even knows it happened.
The engine behind this? OpenClaw, running as a daemon on a Tencent Cloud Lighthouse instance, connected to WhatsApp and Telegram. Here's exactly how I got there — and how you can replicate it.
If you've worked in e-commerce support, you already know the Pareto principle applies hard. The vast majority of tickets fall into a handful of categories:
That's 70% of your volume that's completely predictable and rule-based. An LLM-powered agent with access to your FAQ and order data can handle most of these with near-human accuracy. My measured auto-resolution rate sits at ~60%, with the remaining 10% of "easy" questions occasionally needing a human nudge due to ambiguous phrasing or edge cases.
Nothing exotic here — just OpenClaw doing what it does best:
Customer (WhatsApp/Telegram)
|
v
OpenClaw Agent
(Tencent Cloud Lighthouse)
|
+---------+---------+
| | |
FAQ Browser Escalation
Lookup Skill Rules
| | |
v v v
Instant Live Human
Answer Tracking Agent
The agent checks its knowledge base first. If the answer is there, it responds immediately. If the customer asks about a specific order, the agent-browser skill can navigate to the tracking page and pull status. If the query is complex or the customer is upset, escalation rules kick in and I get a Telegram ping with a full context summary.
Go to the Tencent Cloud Lighthouse Special Offer page:
Grab a 2-core / 4 GB instance in an overseas region for international customer coverage.
# SSH into your Lighthouse instance
openclaw onboard
# Select WhatsApp → paste your Meta Business API token
# Select Telegram → paste your BotFather token
# SECURITY: Never hardcode API keys in plain-text config files.
# The onboard wizard stores them securely.
Full guides: WhatsApp | Telegram
This is the single most impactful step. I created a structured document covering:
The more specific and structured this document is, the higher your auto-resolution rate climbs.
loginctl enable-linger $(whoami) && export XDG_RUNTIME_DIR=/run/user/$(id -u)
openclaw daemon install
openclaw daemon start
openclaw daemon status # Should show "active (running)"
Before routing real customers, test exhaustively. Send the agent every type of question you can think of. Check for:
I spent about 3 hours on testing and prompt refinement. That investment paid for itself within the first day of live operation.
After two weeks of production use, here are my actual metrics:
| Metric | Before OpenClaw | After OpenClaw |
|---|---|---|
| Avg. first response time | 3.5 hours | 47 seconds |
| Auto-resolution rate | 0% | 60% |
| Human tickets/day | ~40 | ~16 |
| Customer satisfaction (CSAT) | 3.8/5 | 4.3/5 |
| Monthly support cost | ~$2,500 | ~$400 |
The CSAT improvement surprised me the most. Turns out, speed matters more than perfection. Customers would rather get an accurate-enough answer in 47 seconds than a perfect answer in 3 hours.
A 60% auto-resolution rate isn't a ceiling — it's a starting point. As you refine your knowledge base and prompt engineering, that number climbs. Some operators in the OpenClaw community report hitting 75–80% for well-defined product lines.
The fastest way to get there: Tencent Cloud Lighthouse Special Offer.
Sixty percent of your support tickets are waiting to be automated. Why not start today?