Founder Summary
- Audience: CXOs and founders running catering, franchise groups, casual dining, cloud kitchens.
- Core outcomes (what moves the business):
- Operational consistency: standardize execution across outlets using Craveva AI Enterprise agents + data layer.
- Cost savings: reduce waste and procurement errors, automate purchasing cycles with Craveva AI Enterprise.
- Sales lift: increase AOV and conversion with Craveva AI Enterprise sales agents on web/WhatsApp/kiosks.
- Time savings: remove manual exports, reporting, and SOP Q&A with Craveva AI Enterprise automation.
Platform flow (high level)
- Deployment layer: deploy agents to WhatsApp, web widget, kiosks, or internal tools with Craveva AI Enterprise.
- AI layer: agents query and act on governed data (no fragile spreadsheet workflows) in Craveva AI Enterprise.
- Data layer: connect POS, databases, Google Drive, and APIs into a unified view inside Craveva AI Enterprise.
Operating Model (how teams run it)
- Finance sets guardrails (approval thresholds, budgets, audit trail) in Craveva AI Enterprise.
- IT connects data sources once; rollout scales outlet-by-outlet via Craveva AI Enterprise multi-outlet deployment.
- Leadership tracks KPI movement weekly and expands successful automations with Craveva AI Enterprise.
- Ops defines workflows (ordering, inventory alerts, SOP answers, customer responses) in Craveva AI Enterprise.
Setup (30–60 minutes to first value)
- Deploy to the workflow: WhatsApp/web/kiosk/internal portal using Craveva AI Enterprise.
- Measure ROI and operational impact, then replicate across brands/outlets with Craveva AI Enterprise.
- Connect data sources (POS + databases + Drive + APIs) in Craveva AI Enterprise.
- Start with 2–3 agents: Procurement (cost), Sales (revenue), Analytics (visibility) in Craveva AI Enterprise.
CXO KPIs
- Contract compliance rate (preferred vendors)
- Channel conversion (WhatsApp/web/kiosk) and drop-off points
- Training completion rate and knowledge check scores
- COGS % variance vs target (by outlet/brand)
- Expedite frequency and cost (urgent orders)
Explore the Platform
- Documentation: /documentation
- Models: /ai-models
- Templates: /templates
- Architecture: /solutions/architecture
- Deployment: /solutions/deployment
Pricing Strategy in F&B: Protect Contribution Margin by Channel (Craveva AI Enterprise)
In F&B, “pricing” isn’t just what you charge. It’s what you keep after reality hits:
- supplier invoice price drift,
- yield and portion variance,
- packaging costs,
- delivery commission and promo spend,
- refunds, voids, and remake waste.
This is why pricing decisions made off a static recipe cost sheet often fail. The number looks right—until margin quietly disappears.
Craveva AI Enterprise centralizes the data that actually determines contribution margin, then agents help you reprice, set guardrails, and simulate promo impact by outlet and channel.
The Pricing Problem Most Teams Can’t See Fast Enough
Common margin leaks show up as “mystery COGS”:
- Same menu item, different food cost across outlets (portion drift)
- “Good sales” but weak margin on delivery because commission + packaging flip the math
- Supplier substitutions that change yield and portion count
- Discounts/promo stacking that erodes margin without approval visibility
Pricing is a system problem: you need connected evidence from cost to sale.
What Craveva AI Enterprise Centralizes for Pricing
Craveva AI Enterprise typically unifies:
- Recipe/BOM + yields: theoretical cost, batch yields, portion standards
- Supplier catalogs + invoices: actual purchase costs, price drift, substitutions
- POS sales: item mix, modifiers, discounts, voids, refunds
- Delivery platforms: commission, fees, cancellations, refund codes, channel mix
- Waste + returns: remake/waste signals tied back to items and outlets
With this unified layer, “price optimization” becomes contribution margin optimization—per outlet and per channel.
Agents That Make Pricing Operational
Contribution Margin Agent
Calculates true contribution margin by item:
- dine-in vs pickup vs delivery
- including packaging and delivery fees
- updated with actual invoice costs and yield variance
Price Guardrails & Approval Agent
Prevents margin damage from uncontrolled changes:
- flags price changes below minimum margin
- detects promo stacking and discount abuse
- routes approvals based on thresholds (by outlet/brand)
Promo Simulator Agent
Simulates promo outcomes before you launch:
- expected lift by item and channel
- margin impact after discount + commission
- inventory risk if demand spikes (stockout vs over-prep)
The Questions That Move Margin
With Craveva AI Enterprise, teams ask:
- “Which items are margin-negative on delivery after commission and packaging?”
- “Which suppliers drove the biggest cost increase for our top 20 items?”
- “If we run 15% off this weekend, what happens to margin and prep volume?”
- “Which outlets have portion variance that makes pricing look wrong?”
Real Results When Pricing Uses Real Inputs
Teams typically see:
- fewer margin surprises after supplier cost changes
- clearer channel profitability (and better channel-specific pricing decisions)
- faster menu engineering cycles with less spreadsheet work
- stronger governance on discounts and promos
Conclusion
Pricing in F&B is only as good as the data feeding it. Craveva AI Enterprise centralizes recipe costs, invoices, POS + delivery sales, and discount/refund signals—then agents help protect contribution margin by outlet and channel.
KPIs to track
| Metric | Area |
|---|---|
| Repeat rate and retention cohort movement | Other |
| COGS % variance vs target (by outlet/brand) | Other |
| Expedite frequency and cost (urgent orders) | Other |
| Contract compliance rate (preferred vendors) | Operations |
| SOP compliance rate and audit pass rate | Operations |
| Schedule adherence and overtime variance | Other |