Choosing LLMs for F&B: A CFO + COO Decision Framework (Craveva AI Enterprise)
Model selection is a budgeting and operational decision. The wrong model mix shows up as higher AI spend, slower service, and inconsistent customer experiences.
This applies across F&B verticals: QSR, casual dining, fine dining, cloud kitchens, catering, bakeries, and franchise groups.
Start with two questions leadership actually cares about
- What is the cost per outcome? (cost per order captured, cost per ticket resolved, cost per report delivered)
- What is the risk if it is wrong or slow? (refunds, stockouts, bad decisions, brand damage)
A simple 2x2 that works in practice
Plot every workflow by:
- Volume: how many times per day/week it runs
- Impact: how expensive a mistake is
Then assign model tiers:
- High volume / lower impact: prioritize speed and cost
- Low volume / high impact: prioritize reasoning and accuracy
- High volume / high impact: redesign the workflow (guardrails, validations, smaller prompts) before paying for premium everywhere
Recommended model strategy by agent type
Examples of how CXOs structure choices:
- Sales and ordering (kiosks, WhatsApp): fast + reliable, because latency kills conversion
- Customer service: strong dialogue quality and policy adherence, because inconsistency creates refund leakage
- Procurement and inventory: balanced models with validations against live data, because errors become waste and stockouts
- Data analysis and executive reporting: premium selectively, because decisions compound
- SOP and training: long-context models if you have heavy documentation
Guardrails that keep quality high without premium spend
Most cost savings come from workflow design:
- Validate against POS and inventory before answering
- Use structured outputs for orders, refunds, and purchase suggestions
- Add approval gates for high-risk actions
- Route to premium only when a request crosses a complexity threshold
What to measure weekly
- Cost per interaction by agent
- Latency at peak periods
- Containment rate (issues resolved without escalation)
- Incremental gross profit from upsell workflows
- Error rates that create waste, refunds, or remakes
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Craveva AI Enterprise lets leadership run a tiered model strategy with governance, so you can scale AI usage without losing cost control.
KPIs to track
| Metric | Area |
|---|---|
| Channel conversion (WhatsApp/web/kiosk) and drop-off points | Sales |
| Returned goods and vendor credit recovery time | Other |
| Supplier lead-time variance and fill-rate by outlet | Procurement |
| Reorder recommendation accuracy vs actual consumption | Inventory |
| Incident escalation rate and time-to-resolution | Other |
| Onboarding time to proficiency (by role) | Other |