Intelligent textile business management — from fibre to finished goods.
One connected workspace for cotton, spinning, weaving, processing, made-ups, sales, export and finance. TextileFlow AI gives your team one version of the truth about every order, lot and report — and gives you the visibility to act on it.
This is a business document, not a technical manual. It explains what TextileFlow AI does, which problems it solves, how your team would use it, and how to see it live. Every capability named here exists in the running product; the few that need an external provider or connector are labelled Configured, with what is required stated plainly. Capabilities shown during the walkthrough are labelled In the demo.
What TextileFlow AI is, why it exists, and who it is for — in the language of a textile business.
TextileFlow AI is a textile-native business management platform by MageTech Solutions. It connects the full operating chain — cotton and sourcing, ginning, spinning, weaving, processing, cutting, sewing, packing, sales, export and finance — into one browser workspace.
Your team signs in once and works from one data set. Every department updates the same order, the same lot and the same stock record — so the morning production meeting, the afternoon dispatch call and the month-end review are all looking at the same numbers.
Think of it as the control room for your textile business: production captured where it happens, stock that reconciles itself, quality recorded at the loom, and a dashboard that answers the questions owners actually ask.
Every module shares the same tenant, the same master data and the same audit trail. No re-keying between departments.
Lot traceability, stage yields, WIP valuation, fabric rolls, SPC, made-ups and export — modelled for textiles, not bolted onto a generic ERP.
Built-in advisory screens that work on day one, plus an optional provider-backed AI for document search, fine-tuning and governed tools.
TextileFlow AI exists to remove the information chase from textile operations. The answer to a simple question should not live in five spreadsheets, two notebooks and three people's memories.
| Today's situation | With TextileFlow AI |
|---|---|
| Production, sales and stock data sit in separate files | One order-to-shipment record with an explicit status at every stage |
| Fibre and yarn losses are discovered at month-end | Lot-level capture, stage yields and work-in-progress value updated as production happens |
| Quality failures are found by the customer, not the loom | Inline inspection, defect catalogue, NCR/CAPA and SPC control charts during production |
| Buyers and suppliers phone or email for status | A scoped external portal where each party sees only what concerns them |
| Export paperwork is assembled by hand | Shipments with a document checklist and scan-verified attachments |
| Costing is reconstructed after dispatch | Order cost plans and cost sheets built from recorded consumption |
Textile businesses run on tight margins and long chains. A small gap — a delayed lot, an uninformed purchase, a quality escape — compounds into weeks of lost margin. Traditional textile software and spreadsheets each cover a piece of the chain, but none was designed to carry a single record from fibre to finished goods.
TextileFlow AI was developed to be that record: modular enough for a spinning mill, deep enough for a multi-plant manufacturer, and consistent enough that finance, quality and sales trust the same numbers.
Need stage-wise production, lot traceability, yield and WIP visibility. Start the conversation at §5 production and quality modules.
Need bale lineage, gin yields and sourcing records from farm to lot. Start with cotton, ginning and sourcing.
Need cutting, sewing, packing and dispatch in one chain, with mobile capture at the line. Start with cutting, made-ups and packing.
Need stock, supplier and customer records without production overhead. Start with inventory, procurement and sales.
Need company-wide visibility with per-factory control and consolidated reporting. Start with the dashboard and facilities.
Need shipment, container, document and compliance workflow alongside production. Start with export and logistics.
| Traditional software / Excel | TextileFlow AI |
|---|---|
| Many tools, many versions of the truth | One tenanted workspace — your company, your factories, your data |
| Textile logic bolted onto a generic ERP | Textile-native modules: ginning, spinning, weaving, processing, cutting, packing, quality and costing |
| Desktop installs per machine; painful updates | Web app plus a mobile shop-floor app; updates reach everyone at once |
| Data entry at a desk, hours after the event | Capture at the machine, scan and sync, with an offline queue on the shop floor |
| Customisation becomes a new project | Roles, permissions, master data and approval policies are configuration, not code |
| Status reporting means a phone call | A scoped portal and a live dashboard answer status questions directly |
TextileFlow AI includes an advisory AI layer in two modes. Built-in advisory screens — demand outlook, maintenance risk, delivery risk, defect advisory and assistant chat — work immediately with no external service. An optional AI provider extends the platform with retrieval over your own documents, fine-tuning and governed agent tools, only when you choose to configure it.
The built-in screens are explainable advisory logic, not models trained on your business. They show inputs, factors and a confidence figure, and they are useful for structured previews and demo conversations. They are not a substitute for trained predictive analytics. Section 8 sets out exactly what each mode does, what it costs, and where the limits are.
To make every textile business — from a single-factory mill to a multi-plant group — run on one clear, trusted picture of its operations, so decisions are made from data rather than from memory.
Form: a hosted SaaS application with a web app and a mobile companion. The same product can run on customer infrastructure for larger groups or regulated environments. There is no per-machine installation and no separate "enterprise edition" to learn.
↑ Back to topThe problems your customers describe in the first five minutes of a demo — and the operational cost of leaving them unsolved.
| Problem | Where it hurts | Business cost |
|---|---|---|
| "Where is order X right now?" | Sales, planning, production | Daily status-chasing, missed delivery windows, unhappy buyers |
| Stock counts disagree between stores and finance | Warehousing, finance | Over- or under-purchase, stock-outs at the line |
| Fibre and yarn losses are invisible until month-end | Spinning, costing | Margin leakage you cannot explain or fix |
| Quality failures are discovered at the customer | Quality, export | Rejections, rework, claims and freight cost |
| Machine downtime is reactive | Maintenance, production | Lost shifts and missed throughput |
| Export and compliance paperwork is manual | Export, finance | Delays at port and filing errors |
| Buyers and suppliers phone or email for status | Merchandising, sales | The same conversation every day, with every party |
| Business problem | TextileFlow AI response | Module |
|---|---|---|
| Spreadsheet versioning chaos | One record per order, per lot, per shipment, with status history and audit trail | Operations |
| Manual stock counts | Movement ledger covering receipt, issue, transfer, adjustment and cycle counts | Inventory |
| Invisible process losses | Stage-wise yields, scrap capture and WIP valuation | Manufacturing |
| Reactive quality | Inline inspection, defect catalogue, NCR and CAPA, SPC control charts | Quality |
| Reactive maintenance | Machine registry, planned maintenance, breakdown records, IoT readings and alerts | Maintenance & IoT |
| Hand-built export files | Shipment, container and document checklist with scan-verified attachments | Export |
| Status by phone | Scoped external portal for buyers and suppliers | Portal |
| Costing after the fact | Order cost plans and cost sheets built from actual consumption | Finance |
System-generated document numbers, balances and reports replace retyping between departments and files.
Validation at entry checks quantities, units and mandatory fields. Bad data is stopped, not discovered a month later.
Approvals, hand-offs and status transitions are explicit. Everyone knows what happens next and who owns it.
Orders, stock, production and exceptions are visible in one place, filtered by company, factory or role.
Cost, yield, OEE and receivables are available before decisions are made, not reconstructed after the quarter.
Alerts, pending approvals and exception queues surface blockers early — while there is still time to act.
| Scenario | Excel / manual process | TextileFlow AI |
|---|---|---|
| Order status | Each department maintains its own sheet; status is reconciled in a meeting | One order record with stage status, history and the next action |
| Stock accuracy | Physical counts typed into a workbook; differences investigated later | Every movement posted to a ledger; on-hand is derived, not typed |
| Production loss | Shift output entered at the end of the day, sometimes from memory | Capture at the machine, with scrap, downtime and operator recorded at source |
| Quality | Paper inspection sheets filed in a cabinet | Inspection records linked to lot, defect catalogue, NCR, CAPA and SPC |
| Approvals | Email or WhatsApp approval with no reliable record | Multi-step approval with named approver, timestamp and audit trail |
| Reporting | Copy, paste, format, check — every week | Built-in reports exported to PDF, XLSX or CSV, optionally scheduled |
Faster answers, fewer reconciliation surprises, earlier visibility of exceptions, and decisions made from the same numbers the whole company sees. Section 10 maps these to business areas and shows how to measure them after implementation.
We do not publish percentage savings we have not measured for your business. What we can promise is a single source of truth, capture at the point of work, traceable changes and reports your team can act on. The size of the gain depends on your current process — and we will baseline it with you during the pilot.
Three ways in, one tenant, one set of numbers — explained without the architecture.
TextileFlow AI is one product with three ways in. Each entry point serves a different person, but all three read and write the same business data.
| Way in | For whom | What it does |
|---|---|---|
| Web app 57 screens | Owners, managers, office staff, planners, finance | Full management: orders, procurement, production, quality, finance, reports and settings |
| Mobile app 9 shop-floor screens | Supervisors, operators, inspectors | Capture production, scan lots, record inspections, work offline and sync later |
| Customer & supplier portal | Your buyers and suppliers | See only their orders, quotations and documents — nothing from any other relationship |
Each customer company is a tenant. Your data lives in its own isolated space. When your employee signs in, the system shows your factories, your stock and your orders — and nothing from any other customer. Isolation is enforced on every query, not by trust.
The walkthrough uses a demo tenant with sample textile data: two facilities, real production stages, live orders and a role per job function. The screens, roles, workflows and reports are the same ones a live customer uses — only the data is fictional.
The storekeeper records the received quantity and lot against the purchase order. Inventory, procurement and costing update from the same entry.
The planner checks capacity, releases work orders and sees orders at risk on the dashboard — before the shift starts, not after it ends.
The supervisor records good quantity, scrap, downtime and operator on the mobile app. If the network drops, the record queues and syncs when it returns.
The inspector records defects against the lot. Open NCRs and SPC trends are visible to the quality manager immediately.
Sales opens the order, sees production and dispatch status, and shares the buyer portal view instead of calling three departments.
The executive dashboard shows output, WIP value, orders at risk, pending shipments and approvals waiting on the owner.
It is built around textile entities: lots, rolls, batches, yarn counts, fabric specifications, made-up orders and stage yields.
It provides cost sheets, a general ledger and accounting exports — not statutory filing or tax advice.
AI is advisory and permission-gated. Nothing is posted or approved by the AI layer without a named user action.
Pricing is scoped to users, facilities, storage, AI usage and hosting choice. Section 16 explains what drives it.
Which textile businesses get the most from TextileFlow AI — and where the demo should start for each.
| Business type | Why it fits | Where to start the demo |
|---|---|---|
| Spinning, weaving & processing mills | Stage-wise production, WIP and yield tracking across the full manufacturing chain | Spinning → Weaving → Processing pages |
| Ginning & cotton operations | Bale and lot traceability from farm to gin, with sourcing records | Cotton & inventory → Ginning → Sourcing |
| Made-ups & garment factories | Cutting, sewing, packing and shipment in one chain | Cutting → Packing → Export |
| Fabric wholesalers & traders | Stock, supplier and customer records without manufacturing overhead | Inventory → Suppliers → Sales |
| Distributors & multi-branch groups | Company-wide visibility with per-factory control and one chart of accounts | Dashboard → Facilities → Billing |
| Export-oriented mills | Shipment, container, document and buyer portal workflow | Sales → Export & logistics → Portal |
Typical starting users. A single-factory business sees value from day one: one production chain, one stock ledger, one dashboard.
Facilities. Multi-plant groups use the same product with more facilities, shared master data and consolidated visibility.
Workspace. Web, mobile and portal are three interfaces to the same tenant — not three products to integrate.
| Signal | Good fit | Consider a simpler tool |
|---|---|---|
| Inventory | Real stock, lots, warehouses and movement volume | No stock to track |
| Operations | Production stages, quality or dispatch to coordinate | Pure services with no physical goods |
| Team | Several departments or more than a handful of users | A one-person desk with one spreadsheet |
| Reporting | Management needs regular operational and cost reporting | No recurring reporting requirement |
| Growth | Adding factories, branches or export customers | No expected change in scale |
The demo is the fastest way to decide. We will use your business type and two or three real problems to choose the modules shown — no obligation, and no production data required.
Every module that ships today — what it does, who uses it, what they enter, what the system shows, and the reports it produces.
The product is organised into 21 connected modules. Use the filters to follow a theme, then open any module to see its purpose, users, inputs, outputs, workflow and links to other modules.
/dashboard/organization · /factories/users · /roles · /sso/master-data/cotton · /ginning · /sourcing/suppliers · /requisitions · /rfqs · /purchase-orders · /procurement-controls/inventory/planning · /scheduling/spinning · /weaving · /processing · /cutting · /packing/quality · /spc/product-development · /products · /recipes/sales · /portal/finance · /ledger/exports/maintenance/workforce · /sustainability/iot/approvals · /documents · /audit/reports/notifications/billing · /support · /integrations · /developer · /platform-adminModule names follow the live navigation in the web app. Some capabilities share a module — for example, spinning, weaving, processing, cutting and packing are separate screens in the manufacturing execution group, while quality and SPC sit together. The demo will open the screens relevant to your business, not all 57.
| If this happens… | …these modules update |
|---|---|
| A purchase order is approved and goods are received | Procurement, inventory (stock and lot), finance (commitment and cost), audit trail |
| A work order is released and output is captured | Planning, production, inventory (WIP and consumption), quality, costing, dashboard |
| A sales order is confirmed | Sales, planning, finance (order value), documents, portal |
| An inspection records a defect | Quality, inventory disposition, NCR/CAPA, supplier quality, reports |
| A shipment is dispatched | Export, inventory, sales, finance, portal, audit trail |
| A user's role changes | Access across every module, audit trail, approval routing |
What a customer sees the moment they sign in — and how to explain it in sixty seconds.
The dashboard is the first screen after login. It answers four questions in one view: what is late, what is pending, what is worth money, and what needs my attention today.
| Order | Customer | Status | Stage | Delivery | Risk |
|---|---|---|---|---|---|
| SO-DEMO-001 | Northwind Apparel Group | Confirmed | Processing | 20 Nov | Low |
| SQ-DEMO-002 | Bluewater Retail Co | Quotation | Costing | — | Review |
| MU-DEMO-001 | Made-ups plan | In progress | Cutting | 19 Nov | Medium |
| Dashboard area | What you see | Why it matters |
|---|---|---|
| Production KPIs | OEE, stage output, WIP value, rejection rate | Is the plant running the way it should today? |
| Order health | Orders at risk, in progress and due for delivery | Which customer promise is in danger? |
| Shipment queue | Pending shipments and document readiness | What is ready to ship, and what is missing? |
| Alerts | Unacknowledged IoT, quality and AI advisory items | What needs a decision today? |
| Approvals | Items waiting specifically on you | What is blocked because you have not seen it? |
| Role | Emphasis | What is deliberately not shown |
|---|---|---|
| Owner / director | Company-wide output, WIP value, orders at risk, receivables | Operational detail they did not ask for |
| Plant / production manager | Line output, OEE, downtime, stage WIP | Commercial margin and payroll |
| Sales / merchandising | Order book, delivery dates, complaints, portal activity | Production cost detail |
| Stores / warehouse | Receipts, issues, stock exceptions, counts | Sales pipeline and costing |
| Buyer (portal) | Only their orders, quotations and documents | Everything else |
The dashboard reads from the same data the modules write. Change a production record and the dashboard reflects it. Role gating means a buyer and a plant manager see different views from the same product.
↑ Back to topA repeatable 45–60 minute sequence that follows one textile order from product setup to dispatch.
Total live walkthrough, including questions and commercial discussion.
Structured steps, with natural stopping points at each module.
Sample order followed end-to-end so the story never loses its thread.
Backend health is UP · demo tenant seeded · signed in as the demo administrator · browser zoom at 100% · notification bell cleared · labels printer path tested if labels will be shown · this document open at §7 · screen share at 1920×1080. Never demo on a customer's production data.
Position TextileFlow AI against the customer's current tools. Recap the problems they described in discovery — do not open the product yet. Confirm the two or three problems the demo should follow.
Sections 1–2Walk the problem-versus-solution table. Ask: "Which two of these cost you the most time today?" Write them down; the rest of the demo follows those two.
Section 2Sign in as the demo administrator. Read the dashboard out loud in business language. Switch to a supervisor role and show the view simplify. Open the buyer portal and show what the buyer cannot see.
Sections 5.1, 5.3, 6Follow one order end-to-end: create a product and BOM → raise a purchase order → receive goods → plan and start production → capture output on mobile → record a quality inspection → pack → create a shipment. Show the audit trail at each step.
Section 5Open AI & Predictions: delivery risk, maintenance advisory, assistant chat. Explain the two-mode model and the limits box honestly. If the customer asks for document-grounded AI, show AI Studio and state what configuration is required.
Section 8Generate one report as PDF and one as XLSX. Show scheduled delivery and print a lot label. This is the "no spreadsheet step" moment.
Section 5.19Map what they just saw to the benefit table and the use case nearest their business. Ask which outcome they would measure first.
Sections 9–10Let the customer drive. Use the FAQ (§18) and the demo checklist (§19) to stay on track. Answer security, hosting and integration questions from §12–§14, not from memory.
Sections 12–14, 18Propose a scoped pilot. Agree the next date while you are still on the call. Send this document and a short recap the same day.
Sections 16, 21The demo tenant already contains a product you can use. To show creation live, build a second one with the fields below. The values match the product's real data model and keep the story coherent.
| Step | Screen / action | Values to use |
|---|---|---|
| 1 | Products & BOM → New product | Code PRD-SHIRT-002 · Name "Poplin dress shirt — demo" · Category GARMENT · UOM PCS |
| 2 | Add variants | PRD-SHIRT-002-S / SKU SHIRT-POP-S-NAVY · PRD-SHIRT-002-M / SKU SHIRT-POP-M-NAVY |
| 3 | Create bill of material | Code BOM-SHIRT-002 · revision 1 · status ACTIVE |
| 4 | Add BOM lines | Dyed poplin fabric 1.60 M · fusible interlining 0.35 M · pearl button 18L × 11 PCS · poly bag and hang tag 1 SET |
| 5 | Sales → New quotation | Customer "Bluewater Retail Co" · 1,500 PCS · USD 12.50 · validity 30 days |
| 6 | Convert to sales order | Delivery in 20 days · two schedule lines: 800 PCS and 700 PCS |
| 7 | Planning → release | Assign the order to Unit 1; show capacity and the proposed line sequence |
| 8 | Production → capture | Record 420 PCS good, 6 PCS scrap, one downtime reason; show yield and WIP update |
| 9 | Quality → inspect | Log defect "Missing pick" on one roll; show NCR and disposition |
| 10 | Packing → dispatch | Create cartons, print a carton label, create the shipment and show the document checklist |
Cotton receipt → ginning → spinning → weaving → processing → quality → dispatch. Focus on stage yield, WIP and traceability. 15 minutes on the core chain.
Supplier → RFQ → purchase order → goods receipt → stock transfer → sales order → portal. Skip production; focus on stock accuracy and order status.
Product and BOM → cutting bundles → sewing output → packing cartons → export shipment. Focus on mobile capture and dispatch readiness.
| Customer question | Show this | Section |
|---|---|---|
| How does the customer log in? | Login screen, demo credentials, forgot-password flow | 7, 11 |
| What does the customer see after login? | Executive dashboard and exception banner | 6 |
| How is the dashboard explained? | Sixty-second dashboard script | 6.2 |
| How is the main workflow demonstrated? | One order from product to dispatch | 7.1 |
| How is a sample textile product created? | Product, variants and BOM recipe | 7.1 |
| How is inventory or order flow shown? | Receipt → issue → order → shipment | 5.6–5.7, 5.12 |
| How is AI demonstrated? | Delivery risk, maintenance advisory, assistant | 8 |
| How is reporting demonstrated? | PDF, XLSX and scheduled delivery | 5.19, 11 |
| How are permissions demonstrated? | Admin vs supervisor vs buyer portal | 5.3, 6.3 |
| How are business settings shown? | Organisation, master data, approval policy | 5.2, 5.4, 12 |
| How is the end-to-end journey shown? | The same order visible in four modules | 5.22 |
| What is the expected duration? | 45–60 minutes, 9 steps | 7 |
If asked "will the AI learn from our data automatically?", the accurate answer is: "The built-in advisory screens use fixed, explainable logic and show their inputs and confidence. The provider-optional capabilities — document-grounded answers, fine-tuning and agent tools — work once you configure a provider. We will show you both modes and their limits." That honesty is part of the product design.
What the AI does, how a user interacts with it, how results are governed, and where the limits are.
TextileFlow AI uses AI in a deliberately controlled way. Every AI answer carries its origin (which mode produced it), the inputs it used and a confidence figure the user can see. There are two modes: one that works on day one, and one you switch on when you are ready.
Works immediately inside the product with no external calls. Demand outlook, delivery risk, maintenance risk, defect advisory and assistant chat. Explainable, permission-gated and free of per-request cost.
Adds retrieval over your own documents (RAG), curated fine-tuning and governed agent tools. Requires an LLM provider account and configuration; usage is metered and permission-controlled.
| Capability | Mode | What it does | What you need |
|---|---|---|---|
| Demand outlook | In the demo | Monthly projection with a seasonal factor and a confidence band | Nothing — works offline |
| Delivery risk | In the demo | OTIF-style confidence with a three-factor explanation for a sales order | Nothing — works offline |
| Maintenance advisory | In the demo | Risk banding from machine signals with recommended action | Machine record; telemetry optional |
| Defect advisory | In the demo | Returns a defect classification, quality score and disposition for a roll | A roll or inspection context |
| Assistant chat | In the demo | Answers common questions from a defined knowledge set and cites its sources | Nothing — works offline |
| Document search & answers (RAG) | Configured | Retrieves and cites answers from your documents and knowledge base | Documents uploaded; provider configured |
| Agent tools | Configured | Governed tools with an approval gate and an audit entry for every call | Provider configured for language mode |
| Fine-tuning | Configured | Curated datasets, training jobs and a versioned model registry | A provider with a fine-tuning API |
| Insight lifecycle | In the demo | Persists insights, acknowledgement and feedback for evaluation | Nothing |
AI & Predictions for advisory screens, or AI Studio for document-grounded work and models.
Select a product, machine, order or roll — or type a question. The more specific the context, the more relevant the output.
Every result shows the inputs, the factors considered and a confidence figure. Sources are cited where the mode supports retrieval.
The user makes the decision. Acknowledgement and feedback are recorded for evaluation; the AI does not post transactions on its own.
| Question | Answer |
|---|---|
| What input is required? | A product, machine, sales order or roll context; or a natural-language question. Document mode additionally needs documents in the knowledge base. |
| What output is generated? | A structured recommendation or answer, with factors, confidence and — in document mode — cited sources. |
| Can AI provide business recommendations? | Yes, as advisory text: for example a delivery-risk factor list or a maintenance action. A named user still decides and records the action. |
| Can AI analyse textile business data? | The built-in screens analyse the context supplied to them and present factors. Provider mode can retrieve from documents and knowledge you configure. |
| Can AI forecast? | The demand outlook produces a monthly projection with a confidence band. It is advisory, not a trained forecast of your sales history. |
| Can AI support fabric decisions? | The defect advisory classifies a defect context and suggests a disposition. It does not inspect images or replace a qualified inspector. |
| Can AI generate reports or summaries? | Answers and insights can be summarised; formal reports come from the Reports module, where numbers are traceable to records. |
| How are results validated? | Every result exposes its inputs and factors. The user compares them with the source record; acknowledgement and feedback are stored. Operational decisions remain with accountable staff. |
| Can customers configure AI? | Yes — AI access is permission-gated per role and tenant, and provider mode is configured by an administrator. AI call limits are enforced by plan entitlement. |
| What are the usage limits? | Plan entitlements include AI call limits, enforced by the billing layer. Provider-mode costs are metered per tenant. |
| Question | Answer |
|---|---|
| Does the built-in mode cost anything? | No. It runs inside the product with no external calls and no per-request fee. |
| Does provider mode cost anything? | Only when you configure an LLM provider. You pay the provider directly; usage is metered in the product. |
| Is my data sent to a provider? | Only in provider mode, and only for the requests you make. In built-in mode nothing leaves the deployment boundary. |
| Can I turn AI off per user or tenant? | Yes. AI is permission-gated like any other module, and the platform can disable it at tenant level. |
| What is shared with AI providers? | In provider mode, the prompt and context needed for the request, sent to the provider you configured. Contracts should explicitly disable training on your data; this is a commercial and configuration item. |
The built-in advisory screens use fixed, explainable logic; they are not models trained on your history and they do not learn automatically from your data. The demand outlook is a structured projection, not a statistically validated forecast of your order book. The defect screen returns an advisory classification from the supplied context — it is not a camera-based computer-vision pipeline. Advanced language capabilities (document-grounded answers, fine-tuning, agent tools) require a configured provider and a commercial agreement covering data handling. We show both modes in the demo so you can judge the difference.
SO-DEMO-001, generate the risk view, and read the three factors aloud. Ask: "Would this change what you do on Monday morning?"LOOM-01, show the risk band and the recommended action, then link it to the maintenance work order.Nine realistic scenarios — the challenge today, the current workaround, the TextileFlow AI answer and the demo moment that proves it.
Current: shift output is written on paper and keyed into a workbook at the end of the week.
With TextileFlow AI: stage-wise production and yield are captured at the machine; WIP value updates as the lot moves.
Demo: follow one work order spinning → weaving → processing and compare plan versus actual.
Current: each godown keeps its own register; head office reconciles by phone.
With TextileFlow AI: one movement ledger by item, lot and location, with transfers and cycle counts.
Demo: goods receipt → issue → transfer between warehouses; show live on-hand and valuation.
Current: quotations live in email; supplier terms are remembered by one person.
With TextileFlow AI: supplier, RFQ, quotation and sales order in one workspace with approval records.
Demo: raise an RFQ → capture quotes → issue a purchase order → convert a quotation to an order.
Current: line supervisors report at shift end; defects are discovered at packing.
With TextileFlow AI: mobile capture at the line, an offline queue, and supervisor conflict resolution on sync.
Demo: capture cutting and sewing output on mobile, sync, confirm WIP and inspect a bundle.
Current: each branch reports a closing stock spreadsheet on different formats and dates.
With TextileFlow AI: facility-wise stock and a company-wide dashboard, with the same master data everywhere.
Demo: switch the factory filter on the dashboard; transfer stock between two sites.
Current: group consolidation takes days and never quite reconciles.
With TextileFlow AI: one tenant, many facilities, one chart of accounts and role-scoped visibility.
Demo: create a facility, assign a manager, and show consolidated versus site-level views.
Current: year-end counts produce a large unexplained adjustment.
With TextileFlow AI: a movement ledger, cycle counts and adjustment approval with a reason code.
Demo: run a cycle count, post a variance and show the approval and audit trail.
Current: the buyer calls the merchandiser, who calls production, who checks a whiteboard.
With TextileFlow AI: order-to-dispatch status and a buyer portal that shows only the buyer's orders.
Demo: open the sales order, then the portal view; show the same status with different access.
Current: machine stops are noted on a clipboard and summarised weekly.
With TextileFlow AI: downtime reasons at the machine, OEE by line and IoT threshold alerts.
Demo: record downtime, read OEE, acknowledge an IoT alert and raise maintenance.
| Use case | Outcome the customer can expect | How to verify in a pilot |
|---|---|---|
| Manufacturer | Losses visible within the shift, not at month-end | Compare stage yield captured live with the current weekly report |
| Wholesaler | One stock number per item and location | Reconcile a sample of SKUs against the physical count |
| Trader | Quotation and supplier history in one record | Time a quote-to-order cycle with and without the system |
| Garment maker | Line output visible before the shift ends | Measure capture lag for cutting and sewing |
| Distributor | Head office sees branch stock the same day | Compare dashboard on-hand with each branch closing report |
| Multi-branch group | Consolidation without a spreadsheet round-trip | Produce one group view from the same master data |
| Inventory team | Count variance explained by movement and reason | Run one cycle count and trace every adjustment |
| Order processing | Status questions answered without a call | Count status calls per order before and after |
| Production tracking | Downtime and OEE available during the shift | Compare OEE from the system with the shift log |
What changes after implementation, by business area — and how to measure it honestly.
The value of TextileFlow AI is not a single number. It is the compounding effect of one trusted data set: fewer reconciliations, faster answers, earlier exceptions and decisions made with evidence.
| Business area | Customer benefit | How you will know it is working |
|---|---|---|
| Business operations | Centralised workflow management — one record per order, lot and shipment | Average time from order to dispatch, visible on the dashboard |
| Productivity | Reduced manual processes where automated — system-generated numbers and documents | Hours spent on reporting and reconciliation, measured before and after |
| Visibility | Easier access to business information — the same numbers for every department | Stock accuracy and on-hand variance, derived from the ledger |
| Decision-making | Data-driven business insights — cost, yield, OEE and receivables in one view | Order margin known before dispatch, not after |
| AI | Intelligent assistance for supported workflows — advisory screens plus optional provider AI | Insight acknowledgement rate and feedback quality |
| Management | Better visibility into business activities — roles, approvals and audit | Approval turnaround time and audit findings |
Stock, orders and production come from one data set, updated by the people doing the work.
Production, quality and stock are recorded where they happen, including offline on the shop floor.
Each record has a named user and a timestamp. Disputes are settled by the audit trail.
Formatted PDF, XLSX and CSV output — not another spreadsheet exercise.
The same system from one factory to many, with more roles and facilities — not a replatform.
We baseline the metrics with you and track them during the pilot instead of quoting generic percentages.
For example: stock variance, time to answer order status, and reporting hours per week. Record the current values before go-live.
Use the §7 sequence with your own data. Capture the same three metrics during the pilot.
The benefit case is the difference you measured, not a vendor percentage. Include licence, implementation and internal time.
Add the next factory, module or integration once the first result is proven — not before.
We do not promise percentage savings or ROI figures in this document. Those depend on your current process, your data quality and your team's adoption. We will measure them together during the pilot.
↑ Back to topThe screens the presenter opens, what the customer sees on each, and the action that carries the story forward.
TextileFlow AI is used entirely in a web browser — no installation on employee machines. The previews below are rendered from the product's actual interface language so a reader can follow the tour without a live session. During the walkthrough, the same screens are shown live with the demo tenant.
Cotton, spinning, weaving, processing, made-ups, sales, export and finance — connected.
| Report | Period | Format | Action |
|---|---|---|---|
| Stage-wise production & yield | October 2026 | PDF · XLSX | Generate |
| Stock valuation & ageing | As at 31 Oct | XLSX · CSV | Generate |
| Order book & delivery status | Open orders | Scheduled | |
| Quality defects & NCR | Last 30 days | PDF · CSV | Generate |
| Batch | Work order | Fabric | Output | Scrap | Status |
|---|---|---|---|---|---|
| WB-1042 | WO-2211 | Poplin 40s | 1,840 m | 1.2% | Running |
| WB-1043 | WO-2212 | Twill 20s | 1,260 m | 2.1% | Running |
| WB-1044 | WO-2215 | Poplin 40s | 980 m | 3.4% | Paused |
| WB-1045 | WO-2216 | Poplin 40s | 740 m | 0.8% | Queued |
| Item | Description | Qty | UOM | Unit price | Line total |
|---|---|---|---|---|---|
| PRD-SHIRT-001 | Resort poplin shirt (assorted sizes) | 500 | PCS | 12.50 | 6,250.00 |
| # | Screen / route | What to show | The action that moves the story |
|---|---|---|---|
| 1 | Login /login | Sign-in, password reset, demo account | Sign in as administrator |
| 2 | Dashboard /dashboard | KPIs, exceptions, order health | Open the at-risk order |
| 3 | Products & BOM /products | Product, variants, BOM cost | Create the sample product |
| 4 | Purchase orders /purchase-orders | PO, approval, receipt status | Follow the PO to goods receipt |
| 5 | Inventory /inventory | On-hand, lot, movement | Issue material to production |
| 6 | Weaving /weaving | Batch, output, yield, NCR | Capture output and open the defect |
| 7 | Quality /quality | Inspection, NCR, disposition | Record the defect and CAPA |
| 8 | Sales /sales | Order, schedule, status | Show the buyer portal view |
| 9 | AI /ai | Delivery risk, factors, assistant | Ask the order-status question |
| 10 | Reports /reports | PDF/XLSX export, schedule | Generate one report live |
| 11 | Roles /roles | Permission matrix, custom roles | Show what a buyer cannot see |
| 12 | Organisation /organization | Factories, departments, lines | Switch factory and show the dashboard scope |
The previews above are rendered from the product's real interface system so the document reads well without a live session. For a customer pack, the sales team can replace each preview with a fresh capture from the running web app at the route shown — using demo data only. No generic textile stock photos are used anywhere in this document.
What you can configure without code, what is integration-ready, and what is scoped as a project.
| Area | Configurable today |
|---|---|
| Users & roles | Create custom roles from 91 permissions; assign per user and scope to a factory |
| Approval policies | Multi-step approvals with amount and factory conditions; approver roles |
| Master data | Units and conversions, categories, yarn and fabric specifications, colours, defect and reason codes |
| Organisation | Factories, buildings, departments, warehouses and production lines |
| Notification preferences | Which alerts, on which channel, per user |
| Workspaces | Per-user defaults, active factory and visible modules |
| Branding | Logo and organisation identity in the app |
| AI access | Permission-gated per role; provider mode configured by an administrator |
| Integration | Status | What it does |
|---|---|---|
| Accounting (Tally, QuickBooks) | In the product | Export journals and ledgers in Tally, Tally-CSV and QuickBooks formats |
| Carrier logistics | Configured | Rate, book, track and receive carrier webhooks |
| GST e-invoice & e-way bill | Configured | Generate IRN / e-way bill records; live portal filing with your credentials |
| Bank reconciliation | Configured | Import statements, match by rules, confirm reconciliation |
| Marketplace orders | Configured | Ingest orders with signed webhook verification |
| SFTP / EDI | Configured | File exchange and EDI mappings with trading partners |
| Industrial gateways | Configured | MQTT, Modbus TCP and OPC-UA bridge for machine telemetry |
| Single sign-on (OIDC / SAML) | Configured | Per-tenant identity-provider federation |
| Payments (Stripe / Razorpay) | Configured | Live collection when gateway credentials are present; manual invoicing otherwise |
| Developer API keys | In the product | tf.* keys with hashed secrets and daily usage metering |
| Outbound webhooks | In the product | Signed event delivery with retry and a delivery log |
| Object storage (S3-compatible) | Configured | Move documents off local disk for multi-instance deployments |
| Request | What is possible | How it is delivered |
|---|---|---|
| Customer-specific fields | Configurable reference data and many required fields are covered today. Additional fields on a transaction are a development item. | Scoped and quoted after a data-model review |
| Custom reports | Built-in reports cover the common operational views; new report definitions can be added. | Scoped as a report pack with acceptance criteria |
| Custom workflows | Roles, permissions and multi-step approval policies are configuration. New state machines are development. | Workshop → written scope → implementation |
| Existing data import | Customers, suppliers, items, open orders and opening stock are imported during onboarding. | Template + validation + reconciliation sign-off |
| ERP / accounting integration | Accounting exports ship today; a live two-way connector is scoped per system. | Interface specification and test plan |
Implementation timeline for customization: a standard configuration and data migration is typically part of the 4-week onboarding (§15). Custom fields, reports or connectors add 2–6 weeks depending on scope, integrations and acceptance criteria. MageTech confirms the timeline in a written implementation plan — not in a sales call.
↑ Back to topHow your business data is protected, isolated, backed up and returned to you.
BCrypt cost-12 password hashing, a 12-character policy, short-lived access tokens, rotating refresh tokens and lockout after repeated failures.
21 system roles and 91 permissions, enforced at method level — not by hiding buttons in the browser.
Every query is scoped to your tenant. Cross-tenant access returns 404, indistinguishable from "does not exist".
Uploads are type- and size-limited and scanned by a ClamAV adapter, fail-closed when enabled.
API secrets are stored hashed; outbound webhooks are HMAC-signed and SSRF-guarded against private targets.
Logins, permission changes and sensitive actions are recorded with a named user and timestamp.
| Question | Answer |
|---|---|
| How are accounts protected? | BCrypt password hashing (cost 12), a 12-character minimum policy, 15-minute access tokens, 7-day rotating refresh tokens, and automatic account lockout (HTTP 423) after repeated failures. |
| How are permissions managed? | Method-level authorization on 538 of 577 endpoints, with 21 system roles and 91 permissions. Tenant scoping comes from a signed token claim — not from anything the user can type. |
| Is data isolated between customers? | Yes. Every query is tenant-scoped; cross-tenant access returns 404. |
| Are documents scanned? | Yes — ClamAV adapter, fail-closed when enabled; uploads are size- and type-limited. |
| Are API keys safe? | Secrets are stored hashed (SHA-256), usage is metered per day, and keys can be revoked. |
| Are webhooks safe? | Deliveries are HMAC-SHA256 signed; outbound URLs are SSRF-guarded against private and loopback targets. |
| Can I export my data? | Yes — reports in CSV, PDF and XLSX, plus accounting exports and API access. Your data is yours to take with you. |
| What happens if I stop using the product? | Standard off-boarding: export your data, confirm the export, then the tenant is closed. No lock-in by design. |
| What is shared with AI providers? | Nothing in built-in mode. In provider mode, only the requests you make, to the provider you configured. A data-processing agreement should explicitly disable training on your data. |
Backup is part of the deployment plan, not an afterthought. The technical baseline is a nightly database dump plus WAL archiving for point-in-time recovery, with a documented restore procedure and periodic restore drills. Managed hosting uses provider-managed snapshots; a self-hosted deployment follows the same documented procedure. Retention is agreed per deployment — the technical baseline recommends 30 days.
| Item | What to agree at contract stage |
|---|---|
| Backup frequency | Nightly full dump plus continuous WAL archiving for PITR |
| Retention | 30 days recommended; confirm for your deployment |
| Restore objective | Target recovery point and recovery time agreed in the implementation plan |
| Verification | Restore drill executed and signed off; repeat schedule agreed |
| Document storage | Included in the storage plan; object storage recommended for multi-instance deployments |
Account lockout (HTTP 423) after repeated login failures, and tenant isolation enforced on every query. Both are present in the running product and can be demonstrated live — not just described.
Cloud, customer infrastructure, or a single-host pilot — the same product and the same modules either way.
MageTech-hosted. Zero infrastructure for your team; we operate the platform.
For large groups, regulated environments or a strict data-residency requirement.
A pragmatic starting point for a pilot or a single-factory site.
| Area | Cloud SaaS | Customer infrastructure |
|---|---|---|
| Application and modules | Full product | Full product |
| Web, mobile and portal | Included | Included |
| Tenant isolation and permissions | Enforced by the application | Enforced by the application |
| Database | Managed PostgreSQL | Your PostgreSQL, with the same migrations |
| Backups | Provider-managed snapshots plus the documented procedure | Your schedule, following the documented procedure |
| Updates | Applied by MageTech | Coordinated release windows |
| Monitoring | Included | Prometheus/Grafana assets provided |
| Object storage | Included | S3-compatible storage recommended for scale-out |
Deployment requirements: a supported Java runtime, PostgreSQL, and a reverse proxy; object storage and a shared cache are optional until you scale beyond one application instance. Exact versions and sizing are confirmed in the implementation plan.
↑ Back to topFrom signature to daily use — a four-week path for a single factory, longer when integrations are involved.
Confirm business type, factories, users and the two or three problems from §2 you most want solved. Sign off the demo scope from §19 and pick the pilot order.
Company tenant, first administrator, factories and facilities are configured. You receive the admin invitation and the environment URL.
Customers, suppliers, items, opening stock and open orders. Units, categories and approval policies are agreed and validated.
Map your job titles to the 21 system roles or create custom ones. Invite users and confirm least-privilege access.
Use the §7 sequence with your own sample data: one product, one purchase, one lot, one shipment. Fix process gaps before training.
Managers, stores, finance and shop floor train separately. Mobile capture is trained at the machine, including the offline queue.
Stock and open orders are reconciled. If you prefer, run a parallel week. Go live with the MageTech team on call.
Reports, alerts and permissions are tuned from real use. The first value review uses the metrics agreed in week 1.
| Activity | Customer | MageTech Solutions |
|---|---|---|
| Scope and success measures | Decides priorities; names a business owner | Facilitates the workshop; documents the plan |
| Tenant and facilities | Confirms structure | Configures and validates |
| Master data | Provides current data in any readable format | Maps, imports, reconciles and reports exceptions |
| Roles and permissions | Approves job-title mapping | Configures roles and invites users |
| Process walkthrough | Provides one real order and the process owner | Runs the session and resolves gaps |
| Training | Releases staff and provides a venue | Delivers role-based training and materials |
| Acceptance | Signs off the agreed checklist | Provides evidence and fixes defects |
| Hypercare | Raises issues through the support desk | Triages, responds and tunes configuration |
Written before go-live: imported record counts reconcile, the pilot order reaches dispatch with correct documents, the agreed roles see the agreed screens, and the reports produce the expected totals. Acceptance is a signed checklist, not a verbal "looks good".
Administrator and roles · stores and inventory · production and mobile capture · quality and NCR · sales and portal · finance and costing · reports and scheduled delivery. Refreshers are available for new joiners and seasonal staff.
Typical timeline: 4 weeks for a single-factory pilot; 6–8 weeks for multi-factory or integrated rollouts. These are estimates — MageTech confirms the timeline in your implementation plan.
↑ Back to topHow TextileFlow AI is packaged, what drives the price, and the terms we can commit to now.
Introductory 2026 launch prices are shown below (§16.1 – §16.2). Prices are indicative, exclusive of applicable taxes, and Custom-quoted Enterprise Plus deals are scoped individually. Outside the standard editions, the following reasons still apply: number of users, facilities, storage, AI usage and hosting choice. The product ships with a complete commercial model — plans, entitlements, usage metering, invoices and payment gateways — so custom commercial terms are configured, not improvised.
Introductory 2026 launch licences for the first 10 qualifying customers. Standardised editions only — the scope is the agreed product scope, not unlimited custom development. USD is a separate international price list, not a currency conversion.
| Edition | India launch | International launch | Named users | Plants |
|---|---|---|---|---|
| Starter | ₹1,99,000 | $2,499 | 5 | 1 |
| Professional | ₹4,99,000 | $5,999 | 15 | 1 |
| Enterprise | ₹9,99,000 | $11,999 | 40 | 3 |
| Enterprise Plus | ₹17,99,000 onwards | $19,999 onwards | 100 | Custom |
Additional: 5 users ₹10,000 / yr (or $150); 10 users ₹18,000 / yr (or $250); additional plant ₹25,000–₹75,000 / yr depending on edition and scope.
For customers who prefer lower initial investment. Annual = approximately 10 months of monthly fees.
| Edition | India monthly | India annual | Intl monthly | Intl annual |
|---|---|---|---|---|
| Starter | ₹7,999 | ₹79,990 | $99 | $990 |
| Professional | ₹14,999 | ₹1,49,990 | $199 | $1,990 |
| Enterprise | ₹29,999 | ₹2,99,990 | $399 | $3,990 |
| Enterprise Plus | Custom | Custom | Custom | Custom |
| Capability | Starter | Professional | Enterprise | Enterprise Plus |
|---|---|---|---|---|
| Procurement & suppliers | Basic | Full | Full | Full |
| Inventory & warehouse | Basic | Full | Full | Full |
| Production management | Basic | Full | Advanced | Custom |
| Spinning & weaving | Limited | Full | Full | Full |
| Dyeing & processing | — | Basic | Full | Full |
| Garment manufacturing | — | Basic | Full | Full |
| AI business insights | Basic | Advanced | Advanced | Custom |
| Predictive analytics | — | Add-on | Basic | Advanced |
The full 17-capability entitlements matrix, AMC and support terms, early-customer offers, example quotations and partner margins are in the 2026 Launch Pricing Catalogue in the sales proposal.
Best Entry Offer — ₹2.24 lakh (TextileFlow AI Starter + implementation) · Best Growth Offer — ₹5.59 lakh (TextileFlow AI Professional + implementation) · Best Enterprise Offer — ₹11.49 lakh (TextileFlow AI Enterprise + implementation). Amounts are pre-GST subtotals; applicable taxes extra. Figures are indicative — a written quote is issued after discovery and scope approval.
See the product with sample data before any commercial commitment.
Managed hosting with monthly or annual billing, scoped to your usage.
Run the product on your infrastructure with a licence and support agreement.
| Driver | What it means for you |
|---|---|
| Users | Your team size drives the plan; you pay for the seats you use. |
| Facilities | More factories move you to a higher tier — not to a new system. |
| Storage | Documents and telemetry are metered and capped by entitlement, so there is no surprise overage. |
| AI usage | Built-in advisory is included; provider-mode calls are metered per tenant and passed through at the provider's rate. |
| Integrations & SSO | Available on higher tiers and in private deployments. |
| Implementation | Migration, configuration, training and custom work are scoped and quoted separately. |
| Hosting | Managed cloud is included in the subscription; private infrastructure carries a licence and support agreement. |
| Question | Answer |
|---|---|
| Is it available as SaaS? | Yes — the primary commercial model is a managed cloud subscription. |
| Monthly or annual? | Both exist. Annual plans are discounted against the monthly equivalent. |
| Is there a one-time licensing option? | Private deployments can be licensed, typically as a one-time licence plus an annual support agreement. Scoped individually. |
| What does implementation cost? | Scoped separately: migration, configuration, training and any custom work. You receive a written plan before work starts. |
| Are AI API costs included? | Built-in advisory: yes. Provider-mode usage: metered and passed through at the provider's rate. |
| Are there user, facility or storage limits? | Yes — plan entitlements define seats, facilities, storage and AI calls. Limits are visible and enforced by the billing layer. |
| How do I pay? | Stripe or Razorpay when a gateway is configured, or manual invoicing. |
| How does renewal work? | The subscription renews on the plan anniversary; the renewal date and amount are visible in advance. |
| How do I cancel? | Cancel any time; access continues to the end of the paid period and you can export your data. |
| What is not included? | Third-party provider fees, carrier charges, statutory filing fees, custom development not in the signed scope, and taxes. |
All commercial terms are confirmed in a written quotation and agreement. Nothing in this document constitutes a binding offer or a final price.
↑ Back to topWhat you get after the project team hands over to daily operations.
Support tickets with SLA timestamps, reply threads, severity and a support console — inside the product.
Product updates are released centrally. Cloud customers receive them automatically; private deployments use agreed windows.
Health checks, Prometheus metrics, alert rules and a starter Grafana dashboard for the operations team.
Backup schedule, retention and restore procedure agreed per deployment; restore drills with sign-off.
Available for new joiners, seasonal staff and teams adopting additional modules.
A named MageTech contact and a documented escalation path — info@magetechsol.com.
From the Support module or by email. Include the record reference, what you expected and what happened.
MageTech assigns a severity and responds within the agreed support terms. Access or data-integrity issues are prioritized.
You receive the resolution and, where relevant, the configuration change or workaround. The ticket history remains available.
Recurring issues feed the product backlog and the quarterly service review. The SLA timestamps make the conversation factual.
Specific response and resolution targets are set in your support agreement, not in this document. What we can state is that every ticket carries SLA timestamps, has an owner and remains visible to you. Private deployments can add a named support contact and an annual maintenance agreement.
The questions customers ask most — answered from the product, not from a brochure.
A presenter's checklist that keeps every demo consistent, honest and on time. Tick items as you prepare.
| Before | During | After |
|---|---|---|
Backend healthy (/actuator/health = UP); demo tenant seeded; signed in as the demo admin; browser at 100%; labels printer tested; this document open at §7. |
Follow the 9 steps in §7; stop at each module; show two roles and the portal; open the §8 limitations unprompted; note the customer's top two pains. | Send this document and a short recap the same day; log requirements and the agreed next step; book the follow-up before closing the call. |
admin@demo.textileflow.local / TextileFlow#2026 (demo profile).The five questions that turn a demo into a scoped next step.
Lets us scope the pilot to what matters, instead of showing everything.
Sets the onboarding order in §15 and the first training session.
Tests whether our §8 honesty is landing — and whether the customer values explainability.
Goes straight into scope, or into the product backlog with a named owner.
Turns interest into a dated next step and a measurable acceptance gate.
Identifies the real decision-makers and the follow-up audience.
It is recorded against your company in a support ticket so MageTech can follow up with the right person and the right answer. Feedback on AI outputs is stored with the insight for evaluation. Nothing you say in the demo is used as production data.
From "this looks good" to a measured pilot — without a long leap of faith.
Share your business type, factory count, user estimate and top two problems. We confirm the modules, hosting option and plan tier that fit.
One factory, your real master data, one real order end-to-end. Baseline the metrics in §10 and measure them together.
A written plan covering migration, roles, integrations, training, acceptance and go-live, with a committed timeline and a named owner on both sides.
Your team runs the business on TextileFlow AI with MageTech in hypercare. Acceptance is signed against the agreed checklist.
Add facilities, integrations and provider-optional AI as the business needs them. Same product, no replatform and no retraining from scratch.
Your factory and user counts · the two problems you most want solved · one sample order or lot · the systems you need to connect · your preferred hosting option. With those five inputs, MageTech can return a scoped plan and a commercial proposal instead of a generic brochure.
Experience smarter textile business management — with your business, your questions and your data.
TextileFlow AI by MageTech Solutions is designed to support textile businesses through modern business management technology and intelligent digital workflows. We welcome textile manufacturers, traders, wholesalers, distributors and business owners to explore how TextileFlow AI can support their operational requirements.
Customer Demo & Product Guide, version 2.0 — 5 November 2026. Prepared by MageTech Solutions for customer-facing demonstrations. Capabilities are described as they exist in the running product; provider-dependent features are labelled Configured. Commercial terms are indicative and confirmed in a written quotation. Product screenshots in this edition are interface previews rendered from the product's design system; replace them with live captures for a delivered customer pack.