Real companies, real results —
proven at all four levels
From single-point efficiency to organizational transformation, all four levels — efficiency, revenue, trust, transformation — are already proven in real companies. These businesses are not "trying AI"; they are running on it.
Filter by product and scenario — find companies like yours
Accurate answers, resolved on the spot
Wired directly into order, logistics and payment systems; returns and refunds close automatically — humans take only the hard cases.
Every outreach email written for one reader
Automatically find the right buyers, read their pain points, write one-to-one emails, and deliver across languages and time zones.
3–5 days of wrangling → full proposal in half a day
Specs, BOM, cost estimates, risk assessment and production scheduling in one click — engineers review, the boss decides.
70+ roles all-in on AI, business up 20% in 9 months
Roles broken down to task level; 500+ Agents / Skills banked as organizational assets under unified access gates and audits.
A 5-person team doubled monthly sales in 3 months
Data intelligence spots trending topics, the content factory produces at scale, and AI converts customers at comment peaks.
Sales cycle cut from 4.5 to 2.8 months
Business-registration changes flag buying signals; decision-chain profiling and tiered nurturing mean reps only chase high-intent accounts.
Business spanning 4 systems, done in one sentence
AI autonomously calls CRM, orders, points and coupon systems; high-risk actions trigger confirmation.
Spots issues 24 hours before complaints arrive
100% conversation QA: a 312% spike in "slow claims" sentiment triggers an alert, auto-localizes and dispatches.
3,260 alerts converged into 1 root cause
Smart aggregation cuts noise and AI assists decisions — critical alerts never drown in clutter.
Root-cause time: 90 minutes → 10 minutes
Within 1 minute: fault classification, blast radius and owning team, auto-matched to the best SOP.
Rules changed 3× a day — AI updated in 30 seconds
A unified knowledge hub keeps agents and bots on the same page — new hires ramp faster.
Some Yunke, Keewise and STAR scenarios are representative deployments with client details anonymized; final metrics are based on acceptance testing.
AI resolution rate 45% → 92%,
humans only take the truly hard cases
An e-commerce customer-service company. The dividing line for service AI is whether it can operate business systems: Tianshu connects directly to orders, logistics and payments, turning returns and refunds from "queue for an agent, 1–2 days across shifts" into instant response and on-the-spot closure. Every step sits within access gates and audit view — it can act, but only within its grants.
Understands
Intent recognition plus emotion detection — even colloquial, emotional messages are read right.
Finds it
Real-time lookup of orders, logistics and account status — no order numbers to read out, no waiting.
Gets it done
Returns, refunds, exchanges, compensation — direct action on business systems, fully automated closure.
Escalates right
Complex disputes go to humans with full conversation context — customers never repeat themselves.
A Ningbo exporter:
outreach reply rates lifted to ~9%
The old way: blast one template to everyone — irrelevant content, replies stuck at the 0.5%–1.5% industry baseline. Now AI first reads each company's business and procurement pain points, then writes specifically for them. A 6–9× reply rate comes from precise targeting × bespoke content — not more volume.
A Dongguan manufacturer: 3–5 days of cross-team wrangling,
now a full AI proposal in half a day
Multi-role collaboration — digging up documents, matching parameters, checking quotes — compressed into a half-day delivery, with humans only reviewing and deciding. Custom manufacturing is the best counter-example to "AI can only do simple Q&A."
Before3–5 days
- SalesReceives the custom request, digs through documents and past cases, then asks engineering if it is even feasible
- EngineeringChecks spec manuals, calculates parameters item by item, compiles BOMs by hand
- Procurement / ProductionVerifies material prices, computes costs, schedules lines — every step waits on the last
- FrictionEndless alignment meetings and rework; by the time the proposal lands, the customer is gone
Nowwithin half a day
- AI employeeReads the custom requirements, auto-searches the enterprise knowledge base and past proposals
- AI employeeGenerates the full proposal in one click: specs + BOM + cost estimate + risk assessment + production schedule
- EngineerReviews the professional output — first-pass approval 60% → 90%+
- Decision-makerApproves at key checkpoints — signing authority stays human
A 4,000-person e-commerce customer-service company:
33% lower cost, 20% more business
Serving Taobao, Douyin, Pinduoduo and Vipshop. Rather than handing every employee an AI tool, the organization itself was rebuilt: 70+ roles broken into 1500+ task items, each assessed as "AI / human-AI / human", with replaceable items distilled into Agents and Skills.
Role audit · task-level decomposition · replaceability assessment
AI employees onboard in batches, taking over high-frequency standardized tasks first
Company-wide rollout · org tuning · human-AI collaboration locked in
Roles covered
Task items
Agents / Skills
AI hours replaced / person / day
- Standardized work (orders / tickets / refunds / reconciliation) run 24/7 under AI employees
- Human employees shift to high-value work: client relationships, complex disputes, service design
- The cost structure shifts from headcount to compute — growth no longer means more hires
Partnering with industry leaders, building the AI deployment ecosystem
The next story could be yours
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