HK
FDE.HK
Case Scenarios

Typical deployment scenarios, not fabricated client cases

Until real authorisation exists, FDE.HK presents reusable scenario templates instead of client names, logos, licences, or media claims.

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Scenario Library

Reusable delivery templates

Each scenario is structured around client type, pain point, deployment goal, modules, deliverables, and risk boundary.

Hong Kong brokerages / research teams

Brokerage AI research assistant scenario

Pain point

Reports, announcements, financials, and market data are scattered across teams and folders.

Deployment goal

Build a searchable research knowledge base and AI summarisation workflow.

Report summariesIssuer data preparationMorning meeting draftsSource references
AI output is research support and not investment advice.
Fund managers / asset management teams

Fund AI workbench scenario

Pain point

Research, portfolio monitoring, and investor communication materials are dispersed.

Deployment goal

Create a fund knowledge base, portfolio reporting workflow, and investor Q&A support.

Project databaseMonthly report draftsInvestor FAQIC material support
Generated content requires review under the fund team's internal process.
Single and multi-family offices

Family office private knowledge base scenario

Pain point

Investment projects, meeting notes, service records, and family documents need private control.

Deployment goal

Deploy a permissioned private knowledge base, project database, and meeting assistant.

Private knowledge baseProject comparisonMeeting minutesPermission isolation
Sensitive materials should wait until NDA and access boundaries are confirmed.
RWA platforms / asset issuers

RWA investor Q&A scenario

Pain point

Project materials are non-standard and investor Q&A plus due diligence preparation are costly.

Deployment goal

Build an RWA project knowledge base, investor Q&A assistant, and due diligence data room.

Project knowledge baseInvestor Q&ADue diligence preparationWhitepaper summary
AI Q&A does not replace legal, compliance, or investment judgement.
Hong Kong listed companies / IR teams

Listed company IR AI assistant scenario

Pain point

Announcements, annual reports, roadshow materials, and investor FAQ need repeated preparation.

Deployment goal

Build an IR knowledge base, announcement summaries, and investor FAQ support.

Announcement summariesAnnual report Q&AInvestor FAQIR email drafts
Disclosure and investor communications require internal review.
Banks / brokerages / payment and digital asset businesses

KYC / AML document processing scenario

Pain point

KYC collection, missing-item alerts, filing, and AML process Q&A consume significant time.

Deployment goal

Deploy KYC document structuring, missing-item alerts, and AML workflow Q&A support.

KYC filingMissing-item alertsAML workflow Q&AApproval record preparation
Final client risk rating and approval remain with authorised personnel.
Retail brands / e-commerce teams

Retail and e-commerce AI service desk scenario

Pain point

Product data, campaign rules, pre-sales guidance, after-sales policy, and member follow-up are scattered across systems and people.

Deployment goal

Build a product knowledge base, pre-sales and after-sales Q&A, and WhatsApp / Website lead reception workflow.

Product knowledge basePre-sales Q&AAfter-sales policy supportMember follow-up suggestions
AI replies should follow brand service policies; refunds, after-sales, and complaints remain with authorised staff.
Education providers / enterprise learning teams

Education and training AI knowledge base scenario

Pain point

Course content, training materials, learner Q&A, and assignment feedback require repeated organization and updates.

Deployment goal

Deploy a course knowledge base, learner Q&A assistant, lesson-plan support, and learning follow-up workflow.

Course knowledge baseLearner Q&A assistantLesson-plan draftsLearning path suggestions
AI supports teaching and training workflows and does not replace teachers, mentors, or accountable owners.
Chain businesses / property / service teams

Operations SOP AI assistant scenario

Pain point

SOP updates are not synchronized, and frontline employees repeatedly ask supervisors about exceptions.

Deployment goal

Build an SOP knowledge base, frontline Q&A assistant, quality checklist, and training Q&A workflow.

SOP knowledge baseFrontline Q&A assistantQuality checklistTraining Q&A
AI supports workflows; safety, complaints, refunds, and exception approvals still require human review.
Manufacturing / trading / supply chain teams

Manufacturing and supply chain AI assistant scenario

Pain point

SOPs, quality records, supplier data, quotation files, and maintenance knowledge are scattered and costly to process across languages.

Deployment goal

Build SOP Q&A, supplier data, document templates, and quality issue review workflows.

SOP Q&ASupplier databaseQuotation document handlingMaintenance knowledge base
AI can assist with data organization and process Q&A; quality, engineering, safety, and procurement decisions remain with authorised personnel.
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Enterprise Procurement Route

Start with non-sensitive context, then move into an accepted AI deployment route

FDE.HK connects intake, diagnosis, solution design, delivery, and managed operations into one enterprise buying path for business, technology, compliance, and management teams.

Non-sensitive first
Sensitive materials wait until NDA, permission, and data boundaries are confirmed
Diagnose before build
Clarify scenarios, users, data, budget, and acceptance criteria first
Accepted delivery
Plans, workflows, training, operations, and acceptance checklists stay traceable
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