AI Revenue Intelligence for E-commerce

Make Every Lead Move Forward.

讓每一個潛在客戶，都能被持續推進至成交。

ASSENS connects lead signals, commercial intent, dynamic scoring, next-best-action, guided selling, follow-up and conversion feedback into one measurable revenue flow.

DISCOVER ASSENS↘
Representative e-commerce operations environment with a business operator managing product and customer activity
AI IN REAL COMMERCE REPRESENTATIVE OPERATING SCENE
SCROLL DOWN ↓

What We Build

What We Do＋

01

Turn fragmented customer touchpoints into one continuous revenue decision system.

E-commerce does not lose revenue only because of insufficient traffic. Revenue is often lost between acquisition, content, inquiry, recommendation, follow-up and conversion. ASSENS is engineering an AI Revenue Agent architecture that keeps commercial context moving across those steps.

電商營收流失，不只發生在「流量不足」。真正的斷點常出現在找客戶、內容觸達、詢問、推薦、後續跟進到成交之間。阿森斯以 AI Revenue Agent 將這些分散流程連續化，讓每個商機都能帶著上下文往下一步移動。

＋SEE THE SYSTEM
Representative product knowledge and commerce data preparation scene
PRODUCT KNOWLEDGE / RAG / CONTENT
Representative customer signal and digital commerce interaction
CUSTOMER SIGNAL / FIND + ENGAGE
Representative human handoff scene with two operators reviewing a commercial workflow
HUMAN HANDOFF / CONVERT

Why Now

The Revenue Gap＋

More traffic does not guarantee more sales. The critical question is whether commercial intent is detected, acted on and followed through.

流量增加，不等於成交增加。關鍵是能否辨識商機、採取正確行動，並持續跟進到結果。

SIGNAL 01

Lead context is scattered across tools.

潛客資訊分散，客服、行銷與銷售無法共享同一個脈絡。

DATA
SIGNAL 02

Inquiry does not automatically become opportunity.

有人詢問，不代表商機已被有效承接與推進。

SALES
SIGNAL 03

Follow-up is inconsistent and hard to prioritize.

未成交 Lead 容易被遺忘，也缺少一致的優先排序。

ACTION
SIGNAL 04

Conversion outcomes rarely feed back into decisions.

成交與流失原因沒有回到前端，下一輪決策仍然靠猜。

LEARN

Services

Service＋

Start with the revenue gap that matters most. The modules share one lead memory and one event chain without requiring a full CRM rebuild.

先從最影響營收的斷點開始，不必重建完整 CRM；所有模組共用 Lead Memory 與事件鏈。

SERVICE 01

Lead Intelligence

商機意圖與潛客優先排序

Commercial Intent Detection, Dynamic Lead Scoring and Lead Priority.

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SERVICE 02

Guided Selling

AI 導購與銷售轉換

Need discovery, Product RAG, recommendations, objection handling and lead capture.

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SERVICE 03

Revenue Action

下一最佳行動與 Follow-up

Next Best Action, follow-up queue, escalation and human handoff.

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SERVICE 04

Conversion Learning

成交回饋與 Revenue Intelligence

Conversion feedback, funnel leakage, loss reasons and action effectiveness.

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Where It Works

Industry Context＋

AI Agent decisions become useful only when they are grounded in real products, real customers and real operating constraints.
AI Agent 只有放進真實商品、顧客與營運限制中，才會產生商業價值。

Representative e-commerce brand operations scene
01

E-commerce Brands

商品、內容、詢問承接與成交推進

Representative B2B procurement and corporate order discussion
02

B2B & Procurement

數量、預算、交期、詢價與高價值商機承接

Representative service-commerce operator using a business system
03

Service Commerce

預約、會員、服務匹配與高意向轉人工

ASSENS founder editorial portrait
FOUNDER / STRATEGY / TRANSFORMATION

Founder

Human Trust＋
AI System

“Technology creates leverage. Commercial judgment decides where that leverage should be applied.”

ASSENS is founder-led because the hardest part of an AI sales system is not generating more messages. It is deciding which commercial problem matters, what the Agent is allowed to do, when a human should take over, and how business outcomes should change the next decision.

阿森斯以創辦人主導，是因為 AI 銷售系統最難的部分，不是生成更多訊息，而是判斷哪個商業問題最重要、Agent 可以做什麼、何時必須交回真人，以及成交結果如何改變下一次決策。

FOUNDER-LED REVENUE INTELLIGENCE

Technology

Shared Agent Core＋

Models and infrastructure can change. The durable value is how ASSENS makes commercial decisions, coordinates Agents and learns from conversion outcomes.

底層模型與元件可以替換；真正的自主價值在商機判斷、Agent 協作、下一步行動與成交回饋。

01

Intent Engine

Detect buying, quotation, customization and procurement intent.

02

Dynamic Lead Scoring

Update priority from need clarity, timing, engagement and other signals.

03

Next Best Action

Choose whether to ask, recommend, capture, quote, follow up or hand off.

04

Lead / Conversation Memory

Keep historical needs, promises, recommendations and human notes.

05

Product RAG + Tool Calling

Ground responses in product facts, FAQ, policy and approved tools.

06

Conversion Feedback

Write inquiry, quote, order and loss outcomes back into the decision layer.

SYSTEM OVERVIEW

Two Agents.
One Shared Intelligence.

LEAD SOURCESWeb · Chat · Social · Store
→
AGENT AIntent / Score / Next Best Action
→
SHARED AGENT COREMemory · Workflow · RAG · Tools · Guardrails
→
AGENT BGuided Selling / Follow-up / Human Handoff
OUTCOMEInquiry / Quote / Order
↺
LEARNING LOOPConversion Feedback

Validation

Built to Prove Value＋

ASSENS is engineering and validating the system in a real e-commerce operating environment. Public positioning remains within verifiable boundaries.

阿森斯以真實電商場域進行工程化與驗證，對外敘事僅採目前可被證明的技術與專案狀態。

TECH FOUNDATION

Patent / Functional Design Foundation

The intelligent targeted lead aggregation concept provides a patent-application and functional architecture foundation for the validation project.

「智能定向匯集潛在客戶系統」具專利申請與功能架構設計基礎，作為本驗證計畫的既有技術基礎。

CURRENT STATUS ENGINEERING
& VALIDATION APPLICATION IN PROCESS / NOT A CLAIM OF GRANTED PATENT
PHASE 01

Foundation & Integration

  • Knowledge base and product data
  • Shared Agent Core + two Agent MVPs
  • Event tracking and KPI structure
PHASE 02

Live Validation

  • Real guided-selling conversations
  • A/B or same-period comparisons
  • Prompt, rule and knowledge iteration
PHASE 03

Measure & Standardize

  • KPI and commercial impact analysis
  • Repeatable data / Agent templates
  • Three-month deployment SOP

Contact

Start With
One Revenue
Gap.

先找出最容易漏掉成交的一個斷點。

Many inquiries but weak conversion? Leads not followed up? Product recommendations inconsistent? Start with one measurable problem and validate whether an AI Revenue Agent can improve it.

詢問很多卻沒成交？Lead 沒有人追？推薦品質不一致？先選一個能量測的營收斷點，驗證 AI Revenue Agent 是否真的有效。