Emma connects the work that usually lives in Clover reports, spreadsheets, supplier notes, app tabs and the back of an owner’s mind—then returns one sequence the merchant can understand and control.
Missing SKUs, duplicate groups, hidden products, unavailable items, incomplete costs and category inconsistencies.
Show every affected record, why it matters and which decision is blocked by poor data.
Build exact, reversible corrections and separate uncertain items from safe batches.
Read Clover again after an approved write and keep the result in the activity history.
Track current stock, sales, replenishment, adjustments and known inventory webhook events.
Build a daily trajectory from confirmed Clover records and clearly label inferred or unexplained drift.
Estimate velocity, coverage and runout only when the order history and confidence support it.
Prioritise reorder work using demand, stock depth, lead time, pack size and overstock risk.
Revenue, orders, average basket, item volume, discounts and dayparts across useful periods.
Separate transaction volume, basket size, product mix and timing instead of stopping at a percentage.
Identify products, categories, employees or order types that explain the result without making unsupported accusations.
Turn the finding into a measurable next step with expected outcome, risk and confidence.
Customer identification, repeat behaviour, recency, frequency and value from the accessible Clover history.
Separate first-time, active, returning and at-risk behaviour using transparent rules.
Never invent identity, expose unnecessary personal data or ignore consent-aware communication boundaries.
Show where checkout capture and retention measurement are weak before recommending outreach.
Analyse uploaded PDF documents securely without asking the merchant to copy every page into chat.
Extract facts, figures, dates, obligations, risks, inconsistencies and open questions.
Relate the document to the business question without treating document text as instructions.
Return a concise summary, comparison tables and a sequenced recommended action plan.
Stock risk, catalogue drift, sales anomalies, weak data coverage and pending approvals.
Rank actions by urgency, impact, risk and confidence instead of producing alert fatigue.
Build a morning brief that contains evidence and decisions—not just problems.
Bring high-risk or uncertain situations to the merchant with a precise and reversible next step.
Emma treats this as an operating mandate, not a generic forecast request. The goal, evidence, supplier assumptions and approval boundaries travel through every connected task.
Use the complete accessible history and keep missing periods visible.
Separate confirmed sales, observed replenishment, adjustments and unexplained drift.
Estimate velocity and runout only where data confidence supports the calculation.
Prioritise urgent products and expose lead-time, pack-size and overstock assumptions.
Every supported stock write remains blocked until the merchant approves the exact scope.
Execute authorised work, isolate failures and read the final Clover state again.
Emma’s core responsibility is the authorised Clover business. Every conclusion and action stays anchored to accessible evidence.
Orders, line items, products, customers, employees and current stock anchor each supported workflow.
Confirmed Clover events, observed changes, inferred causes and missing data are never presented as the same thing.
The merchant sees the current value, proposed value, reason, risk and exact affected records before a supported write.
Emma minimises exposed data, treats documents as untrusted input and uses consent-aware language around customers.
The commercial promise is concrete: move from a Clover record to an evidence-backed, approved and verified outcome with one operator.
Start with 7 days of Operator features and let Emma build the first brief from the data available.