Boardroom Summary
- Audience: CXOs and founders running catering, franchise groups, casual dining, cloud kitchens.
- Core outcomes (what moves the business):
- Time savings: remove manual exports, reporting, and SOP Q&A with Craveva AI Enterprise automation.
- 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.
How the platform works
- Data layer: connect POS, databases, Google Drive, and APIs into a unified view inside Craveva AI Enterprise.
- AI layer: agents query and act on governed data (no fragile spreadsheet workflows) in Craveva AI Enterprise.
- Deployment layer: deploy agents to WhatsApp, web widget, kiosks, or internal tools with Craveva AI Enterprise.
Execution Flow (Ops + Finance + 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.
Go-live Checklist
- 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.
- 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.
Leadership Metrics
- Waste % and yield variance by outlet/daypart
- Supplier lead-time variance and fill-rate by outlet
- Supplier SLA adherence and dispute rate
- AOV, attach rate, and margin-weighted upsell success
- Refund/void rate, order accuracy issues, and root causes
- Labor hours saved (outlet + back office) and training time
Platform References
- Documentation: /documentation
- Models: /ai-models
- Templates: /templates
- Architecture: /solutions/architecture
- Deployment: /solutions/deployment
AI Cost Control: A CFO Playbook for LLM Spend (Craveva AI Enterprise)
AI costs do not “explode” because the price per token is high. They explode because usage grows without governance, and premium models get used for routine work.
This applies across F&B verticals: QSR, casual dining, fine dining, cloud kitchens, catering, bakeries, and franchise groups.
What good looks like
Leaders should be able to answer these weekly:
- What is our AI spend by workflow? (ordering, support, reporting, training)
- What is the cost per outcome? (per order, per ticket, per report)
- What guardrails prevent premium usage creep?
The three biggest spend drivers
- Premium model overuse on high-volume workflows
- Long prompts and repeated context (wasted tokens)
- Low validation leading to rework (users re-ask and escalate)
Controls to implement in week one
In Craveva AI Enterprise, treat AI spend like any other enterprise spend:
- Budget caps per agent and per brand/outlet
- Alerts before limits are hit
- Model permissions (who can run premium)
- Routing rules that upgrade only when needed
A practical optimization pattern
- Use fast models for ordering, FAQs, and routine support
- Use balanced models for standard reporting
- Reserve premium models for complex analysis and leadership reporting
Most savings come from this mix, not from negotiating token prices.
ROI framing that makes sense to execs
Track benefit categories explicitly:
- Cost savings: fewer labor hours on ticket handling, reporting, training
- Sales growth: better conversion and AOV from ordering/upsell flows
- Operational improvement: fewer remakes, refunds, stockouts
- Time savings: faster close, faster decisions, fewer escalations
Next links: /pricing /panel/admin/billing /contact
Craveva AI Enterprise provides the governance layer for AI spend so you can scale usage with predictable unit economics.
KPIs to track
| Metric | Area |
|---|---|
| Channel conversion (WhatsApp/web/kiosk) and drop-off points | Sales |
| Waste % and yield variance by outlet/daypart | Waste |
| Safety stock breaches and recovery time | Other |
| Price change alerts: time-to-detect and time-to-act | Procurement |
| Kitchen ticket time variance by outlet/daypart | Other |
| Time-to-close (EOD) and reporting cycle time reduction | Operations |