From scattered transactions
to one live picture.
Six steps. No replacement of your current systems. This is how raw data from every store becomes a single management surface owners and operators can actually run a network on.
Quick answer
marql reads POS, ERP, accounting, e-commerce, and CSV or API exports, maps them into one chain-wide data model, computes daily KPIs and anomalies, and turns that into role-based cockpits and tracked store actions.
Access model
Read-only by default
Sources
POS + ERP + e-commerce + CSV/API
Replacement
No rip-and-replace
What the flow produces
What owners see after the first sync.
This is not an integration diagram for its own sake. The flow produces a morning briefing, role-based views, and an action layer the network can actually operate from.
What we read
Transactions, stock movements, invoices, prices, schedules
What we standardize
SKU, store, margin, labor, supplier, and compliance data
What the team gets
Morning briefing, alerts, role-based views, checklists, tracked actions
Morning briefing
Today · 08:30
Revenue vs plan target for the current month.
Gross margin across active stores. Review P&L if it keeps moving.
Average transaction value from connected POS data.
Average SKU lines per receipt. Below 2 can signal missed upsell.
Sources connect
ERP · POS · E-commerce · WMS · 1C · Excel
marql plugs into the systems you already run. No replacement, no rip-and-replace. Read-only connectors pull transactions, stock movements, price lists, schedules, suppliers and HR data from every store and channel.
- SAP / Oracle / 1C
- Square / iiko / R-Keeper
- Shopify / WooCommerce
- Custom ERP via API
Data is normalized
One schema across the entire network
Every store calls a SKU something different. Every POS reports margin its own way. marql maps them into a single canonical model — products, stores, transactions, costs, headcount, suppliers — so cross-store comparisons actually mean something.
- SKU mapping & deduplication
- Currency & VAT handling
- Cost & margin reconciliation
- Time-zone alignment
Layers are built
From raw events to management metrics
On top of the unified data, marql computes the layers a network operator actually uses: real-time KPIs, daily P&L per store, margin per category, stock health, labor cost ratios, franchise compliance scores.
- Network KPI layer
- Margin & loss layer
- Operational tasks layer
- Franchise governance layer
Anomalies surface
Problems found before they hurt margin
Rules and statistical baselines flag what doesn't fit: a store dropping 18% week-over-week, shrinkage spike on a SKU, a manager skipping cash reconciliation, a franchisee out of compliance. Each anomaly comes with context, not just a red dot.
- Margin drift alerts
- Stock & shrinkage alerts
- SLA / checklist breaches
- Franchise audit flags
Manager sees the result
One AI operator · every role · every store
The owner sees the network. The COO sees operations. The store manager sees today's tasks and their numbers. The franchise lead sees compliance. Same data, different views — no spreadsheets, no Monday-morning dashboard rebuilds.
- Owner / CEO view
- COO / Ops view
- Store manager view
- Franchise partner view
Decisions go back to stores
Playbooks · standards · actions
Insight without action is a dashboard. marql pushes standardized playbooks, daily checklists, price updates and operational corrections back into the network — and tracks whether they actually got done.
- Daily ops checklists
- Standardized playbooks
- Price & promo rollouts
- Compliance follow-ups
Data Flow FAQ
Questions buyers ask before they grant data access.
No. marql is read-only by default. We pull data from your existing systems, compute the operating model, and show it back as briefings, alerts, and cockpits without changing source records.
Usually read-only API credentials, database views, or scheduled exports approved by the owner, accountant, or IT lead. We scope the least invasive path first.
That depends on the source. Some systems can sync hourly, others daily. The goal is operational visibility, not unnecessary polling, so we match the cadence to the workflow.
That is still workable. marql can normalize scheduled CSV or Excel exports as a first integration path, then move to API or direct connector paths later if needed.
If you want to validate more stores, roles, or metrics, we usually turn that into a 7-day pilot.
Yes. Mixed-system networks are a core use case. The normalization layer is specifically built to reconcile different POS, ERP, accounting, and export formats across the chain.
Ready to see your own data flow?
We will scope the least invasive connector path first.