ProposalJewelex (JIPL) — Back Office Automation
AI-Assisted Order Processing · From PO Receipt to Production
ProblemThe Current Reality — Manual, Error-Prone, Slow
- Orders arrive across email, WhatsApp, and 8+ customer portals
- Every customer has different PO formats and sender patterns
- Client identification depends on memory and experience
- Wrong identification creates downstream errors
- New masters require work across multiple LN sessions
- Judgmental decisions depend on product and customer rules
- Specialist coordination happens through WhatsApp / verbal follow-ups
- Limited audit trail before master is finalized
- 97 fields are manually compared between PO and CSO
- Client-specific rules are remembered, not system-driven
- Mismatch resolution lacks structured tracking and SLA visibility
- Address, CSO, SO, stock link, requisition, and factory PO entered manually
- JNS/JAN direct customers follow a separate path
- Mistakes require correction across multiple LN screens
SolutionFour Connected Stages, One Automated Pipeline
- Reads email, WhatsApp, and 8+ portals
- 70+ client identification rules
- Confidence scoring
- Human review for low-confidence cases
- Channel source tracking
- Clerical checkpoints automated
- Special scenario rules applied
- Judgmental rules routed for confirmation
- 12+ LN sessions updated in sequence
- Senior sign-off before master lock
- 97-field validation
- Client-specific field values
- Exception rules configurable by admin
- Live metal rate integration
- Auto-fix for rule-based corrections
- Address → CSO → SO → Stock → Requisition → Factory PO
- Smart routing for JNS/JAN direct customers
- LN posting log with reference numbers
- Retry handling
- Kanban-based tracking
How It WorksHow RapidData Brings This to Life
- 1Order arrives via email, WhatsApp, or portal.
- 2Workflow auto-picks the PO.
- 3AI identifies customer, type, priority, and items.
- 4System checks if master exists in LN.
- 5Missing master triggers Copy Master workflow.
- 6Auto-creates CSO, SO, stock link, requisition, factory PO.
- 7AI validates all 97 fields and exceptions.
- 8Clean orders release; exceptions route to review.
ConfigurationJewelex Stays in Control — Human Approval, Configurable Rules, and LN Sync Visibility
AI completes the work, but Jewelex controls the rules, approvals, exceptions, and final system actions.
- AI prepares the output; Jewelex approves before final action
- Senior Creator sign-off before master lock
- Low-confidence AI decisions routed for review
- Exceptions sent to the right user/team
- Final approval before production handoff
AI comes back to the user for approval before critical actions.
- 70+ customer identification rules
- Copy Master special scenarios and judgmental rules
- 97-field checking values by client
- Exception rules for diamond quality, stamping, GIA and labour codes
- Admin-maintained rules — no code changes needed
Jewelex can update rules directly as business norms change.
- LN posting log for every system write
- Success, failed and retry statuses visible
- Reference numbers captured after sync
- Failed actions highlighted for quick correction
- Full audit trail for every LN push
Teams can instantly see what synced and what needs attention.
ImpactStreamlining the Back-office Operations
- 014.5 hrs average pipeline time
- 02Manual order identification
- 03LN sessions updated manually
- 0497 fields checked manually
- 05Client exceptions stored in people's heads
- 06WhatsApp coordination
- 07Limited audit trail
- 08Low cross-team visibility
- 0130–40 min average pipeline time
- 0285% auto-identified, 15% human-confirmed
- 03LN updates automated in sequence
- 04AI validates all 97 fields
- 05Exceptions configured in system
- 06In-system review queues
- 07Full audit log
- 08Real-time pipeline dashboard
DeliveryDelivery Timeline & Team
Phased implementation across Order Intake, Copy Master, Order Feeding, and Order Checking.
*2 weeks time considered for WhatsApp and Other Portals Integration
| Platform Developer | LN integration, workflow orchestration, backend services, database, rules engine |
| AI Engineer | Order parsing, client identification, AI comparison, confidence scoring, exception handling |
| App Developer | UI screens, queues, dashboards, review screens, status tracker |
| App Dev Lead | Solution design, sprint planning, QA oversight, stakeholder coordination, delivery governance |
- 4 connected workflow stages
- 4 AI agents
- 13 screens + 5 detail views
- Email, WhatsApp, and portal-based intake
- LN read/write integration
- Configurable rule engines
- RBAC, audit logs, dashboards
- Training, documentation, and hypercare
ProductLet's See How It Works
A live walkthrough of the full pipeline — from PO receipt to production release.
Why UsWhy RapidData — Built to Deliver, Not Just Demo
A practical AI partner that brings advisory, workflow execution, configurable rules, governance, and fixed-cost delivery together.
We help identify high-value AI opportunities, define success metrics, and prioritize what to automate first.
Every AI decision is tied to visible rules, exception logic, confidence scores, and audit logs.
Intake, master creation, order feeding, checking, exceptions, approvals, and LN actions connected in one governed workflow.
Clear scope, timeline, and investment — no open-ended AI experimentation or surprise overruns.
RapidData manages design, configuration, build, testing, training, and rollout while Jewelex stays focused on operations.
RBAC, audit logs, approval gates, confidence scoring, exception queues, and human-in-the-loop governance built in.
Next StepsHow We Begin — A Clear 5-Step Path
- 1Walk through proposal with Jewelex stakeholders
Review scope, assumptions, and success criteria with the core team.
- 2Confirm scope and phasing
Lock the phased delivery plan aligned to your production calendar.
- 3Run 2-day discovery workshop
Deep-dive into rules, exceptions, and LN integration touchpoints.
- 4Complete channel credential onboarding
Secure access to email, portals, and LN test environments.
- 5Begin development with Order Intake first
Ship the first automation stage in Sprint 1 with live feedback loops.
Let's move from manual order processing to a governed AI-assisted order-to-production workflow.