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CAITO (Chief Artificial Intelligence Technology Officer)

Choosing the Right LLM Model for Your F&B Business with Craveva AI Enterprise

A comprehensive decision framework to help F&B businesses select the optimal AI LLM model using **Craveva AI Enterprise**. Compare models, understand use cases, and make informed decisions.

Craveva AI Enterprise Team · May 5, 2025 · 7 min read
Supported today (auto-updated)
Deployments
  • Web widget (JavaScript embed)
  • WhatsApp Business
  • E-commerce: Shopify, WordPress, WooCommerce, Magento, BigCommerce
Data sources & integrations
  • Offline files + Google Drive
  • Databases: PostgreSQL, MySQL, MongoDB, BigQuery, Snowflake, Redshift, Athena, ClickHouse, Trino, SQL Server, Oracle, DuckDB
  • Online APIs: REST, GraphQL, Webhook
  • POS (Singapore): Qashier, Eats365 (others appear in roadmap/partials)
Note: Some connectors may exist as base classes/framework but are not yet available as production deployments.

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

  1. What is the cost per outcome? (cost per order captured, cost per ticket resolved, cost per report delivered)
  2. 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

Next links: /pricing /solutions/ai-layer /contact

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

MetricArea
Channel conversion (WhatsApp/web/kiosk) and drop-off pointsSales
Returned goods and vendor credit recovery timeOther
Supplier lead-time variance and fill-rate by outletProcurement
Reorder recommendation accuracy vs actual consumptionInventory
Incident escalation rate and time-to-resolutionOther
Onboarding time to proficiency (by role)Other

Connect Now: AI Enterprise Consultants

Ready to transform your F&B operations with Craveva AI Enterprise? Book a meeting with our AI Enterprise Consultants to discuss how we can help your business.

Technical Glossary

Artificial Intelligence (AI)

AI/ML

The simulation of human intelligence in machines that are programmed to think and learn like humans. In F&B, AI is used to automate decisions, analyze data, and provide insights.

Machine Learning (ML)

AI/ML

A subset of AI that enables systems to learn and improve from experience without being explicitly programmed. ML algorithms identify patterns in data to make predictions or decisions.

Large Language Model (LLM)

AI/ML

Advanced AI models trained on vast amounts of text data that can understand and generate human-like text. Used in chatbots, content generation, and natural language processing.

RAG (Retrieval-Augmented Generation)

AI/ML

An AI technique that combines information retrieval with text generation. RAG systems retrieve relevant information from a knowledge base and use it to generate accurate, context-aware responses.

AI Agents

AI/ML

Autonomous software programs that use AI to perform tasks, make decisions, and interact with systems. In F&B, agents can automate customer service, procurement, inventory management, and more.

Embeddings

AI/ML

Numerical representations of text, images, or other data that capture semantic meaning. Embeddings enable AI systems to understand relationships and similarities between different pieces of information.

Vector Database

AI/ML

A specialized database designed to store and query high-dimensional vectors (embeddings). Used in RAG systems to quickly find relevant information based on semantic similarity.

Neural Networks

AI/ML

Computing systems inspired by biological neural networks. They consist of interconnected nodes (neurons) that process information and learn patterns from data.

Natural Language Processing (NLP)

AI/ML

A branch of AI that enables computers to understand, interpret, and generate human language. Used in chatbots, sentiment analysis, and text analysis.

Deep Learning

AI/ML

A subset of machine learning that uses neural networks with multiple layers to learn complex patterns in data. Particularly effective for image recognition, speech recognition, and natural language processing.

Data Centralization

Data

The process of consolidating data from multiple sources (POS systems, databases, files, APIs) into a single unified platform. Essential for AI systems to work effectively with all business data.

Data Integration

Data

The process of combining data from different sources into a unified view. Enables businesses to access and analyze all their data in one place.

ETL (Extract, Transform, Load)

Data

A data integration process that extracts data from source systems, transforms it to fit business needs, and loads it into a target database or data warehouse.

Data Warehouse

Data

A centralized repository that stores integrated data from multiple sources. Designed for querying and analysis rather than transaction processing.

API (Application Programming Interface)

Data

A set of protocols and tools that allows different software applications to communicate and share data. APIs enable integration between systems.

Database

Data

An organized collection of data stored and accessed electronically. Common types include relational databases (PostgreSQL, MySQL) and NoSQL databases (MongoDB).

Data Pipeline

Data

A series of data processing steps that move data from source systems to destination systems, often with transformations along the way.

Data Governance

Data

The overall management of data availability, usability, integrity, and security. Ensures data quality and compliance with regulations.

Data Quality

Data

The measure of data's fitness for its intended use. High-quality data is accurate, complete, consistent, and timely.

Business Intelligence (BI)

Data

Technologies and strategies used to analyze business data and provide actionable insights. Includes reporting, analytics, and data visualization.

POS (Point of Sale)

Operations

The system where customers complete transactions. POS systems record sales, manage inventory, process payments, and generate receipts. Examples include Qashier, Eats365, and Dinlr.

Inventory Management

Operations

The process of ordering, storing, and using inventory. Effective inventory management ensures the right products are available at the right time while minimizing waste and costs.

Supply Chain

Operations

The network of organizations, people, activities, and resources involved in moving products from suppliers to customers. Includes procurement, logistics, and distribution.

Procurement

Operations

The process of finding, acquiring, and managing goods and services needed for business operations. Includes supplier selection, negotiation, and purchase order management.

Food Cost

F&B

The cost of ingredients used to prepare menu items. Food cost percentage is calculated as (cost of ingredients / menu price) × 100. A key metric for profitability.

Labor Cost

F&B

The total cost of employee wages, benefits, and related expenses. Labor cost percentage is calculated as (total labor cost / total revenue) × 100.

Menu Engineering

F&B

The analysis of menu items based on profitability and popularity. Helps restaurants optimize menu offerings to maximize revenue and profit.

Average Order Value (AOV)

F&B

The average amount spent per customer transaction. Calculated as total revenue divided by number of orders. Increasing AOV is a key revenue growth strategy.

Customer Lifetime Value (CLV)

F&B

The total revenue a business can expect from a single customer over their entire relationship. Helps prioritize customer retention and acquisition strategies.

Waste Reduction

Operations

Strategies and processes to minimize food waste, inventory spoilage, and operational inefficiencies. Reduces costs and improves sustainability.

Cloud Computing

Technology

The delivery of computing services (servers, storage, databases, software) over the internet. Provides scalability, flexibility, and cost efficiency.

SaaS (Software as a Service)

Technology

A software delivery model where applications are hosted by a vendor and made available to customers over the internet. Users access software through web browsers.

API Integration

Technology

The process of connecting different software systems using APIs. Enables data sharing and workflow automation between applications.

Microservices

Technology

An architectural approach where applications are built as a collection of small, independent services. Each service handles a specific business function.

Automation

Technology

The use of technology to perform tasks with minimal human intervention. In F&B, automation can handle repetitive tasks like order processing, inventory updates, and reporting.

Workflow

Technology

A series of steps or tasks that need to be completed to achieve a business goal. Workflow automation uses technology to streamline and automate these processes.

Real-time Processing

Technology

The processing of data immediately as it is received, without delay. Enables instant insights and responses, critical for operational decision-making.

Scalability

Technology

The ability of a system to handle growing amounts of work or to be easily expanded. Critical for businesses that plan to grow or handle variable workloads.

Dashboard

Technology

A visual display of key business metrics and KPIs. Provides at-a-glance views of performance and helps identify trends and issues quickly.

KPI (Key Performance Indicator)

Technology

Measurable values that demonstrate how effectively a business is achieving key objectives. Common F&B KPIs include food cost percentage, labor cost percentage, and AOV.

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