解決方案

面向行業的智慧化方案，開箱即用

AML

AML End-to-End Compliance

Alert triage, AI SAR drafting, sanctions screening, large-value reports and regulatory message validation.

挑戰

Alert volumes grow 30%+ yearly under tightening regulation; manual triage averages 15 minutes per alert with inconsistent standards; SAR drafting takes 2 hours per report; the deadliest risk is a missed sanctioned party.

方案

Rebuild the full chain: automated data reconciliation, daily indicator runs, AI triage scoring every alert with evidence summaries, AI-drafted SARs with human review, multi-path watchlist recall. Every rule, threshold and schedule is adjustable at runtime.

成果

~70% less manual triage, unified triage standards, SAR drafting down from 2h to 30min, zero missed sanctions in backtesting.

行業痛點

  • Slow manual triage with inconsistent standards
  • Time-consuming SAR report drafting
  • Sanctions screening miss risk

35 pages of real-device frontend walkthrough all passed

20M customers / 46.1M transactions benchmark

89,016 sanctions screening hits in production tests

Data Governance

Data Quality & Lineage

Metadata discovery, 7-type quality checks, lineage tracing and metric reconciliation.

挑戰

The same customer looks different across CRM, orders and risk systems; reports never reconcile; nobody can trace where a metric comes from; incidents cannot be attributed.

方案

Lineage graph covering every table and field, a quality rule engine scanning daily for nulls, format errors and cross-system inconsistency, and a ticket-based remediation loop with no direct production edits.

成果

Report disputes gone, issue detection from "next meeting" to same-day, onboarding cut to one week, zero data incidents for the year.

行業痛點

  • Inconsistent metrics across sources
  • Quality rules scattered with no reuse
  • Nobody owns mismatched numbers

Auto knowledge discovery into the glossary

DQ rule engine + execution reports

Cross-database lineage graph

Workflow Orchestration

Cross-System Workflow Orchestration Engine

Declaratively orchestrate cross-system flows: data ingestion, AI processing, result persistence, notifications — zero-code with resume-from-checkpoint.

挑戰

One process spans four systems glued together by people copying, pasting and chasing; handover breaks it; every change needs two weeks of cross-team meetings.

方案

An orchestration chain across systems: schedulers, conditional routing, cross-system data fetch, AI document generation, automatic retries and alerts. Steps are configurable, auditable and rollbackable without code changes.

成果

End-to-end time from 3 manual days to 40 automated minutes; exceptions alerted within the hour; change lead time from two weeks to same day.

行業痛點

  • Flows glued together by hand-written code
  • Cross-system handoffs rely on human coordination
  • Failures restart from scratch

Unified cross-system orchestration

Zero-code flows, config-driven

Resume-from-checkpoint with quality gates

E-commerce

E-commerce Full Chain

From product modeling to remarketing: seven links in one chain, editable by chat.

挑戰

Every ecommerce task is a siloed tool with humans moving data between them: listing, design, night-shift support, month-end books — no time left for actual decisions.

方案

One chain runs the whole business: auto-generated product admin, AI-produced content and store pages, AI support with human escalation, automated order reconciliation, and a daily morning report that only alerts on anomalies. Every step adjustable by conversation.

成果

Listing time from 3 days to half a day, 24/7 support coverage, same-day business visibility, changes shipped by sentence instead of dev sprints.

行業痛點

  • 商品信息散在表格里
  • 商品图视频外包贵且慢
  • 咨询靠人盯
  • 经营数据要等开发取数
  • 复购营销没工具

一句话建商品进销存系统

一张商品图自动出营销视频

独立站自动上架

AI 咨询应答转工单

微信支付宝收款对账

老板随口问数

Content & Media

Content Media Matrix

Topic bank, batch production, scheduled multi-platform publishing, data feedback, AI pattern-finding.

挑戰

Running 5-6 platforms with gut-feel topics, manual publishing, slow data loops and no institutionalized hit-making.

方案

A matrix pipeline: unified topic library, AI batch content production, scheduled multi-platform publishing, automated performance datafeedback, and AI analysis of what works where — feeding back into the library. Per-platform schedules and rules configurable by conversation.

成果

Production-to-publish time halved, no more manual cross-posting, data-driven topic decisions, team capacity shifted to creativity.

行業痛點

  • 选题靠灵感没有沉淀
  • 产能上不去
  • 多平台手动发布
  • 哪条爆了说不清

选题库对象化管理

AIGC 批量出片

多平台定时自动发布

表现数据自动回流

AI 对比找爆款规律反哺选题