Back to Blog
CAITO (Chief Artificial Intelligence Technology Officer)

Complete Guide to 342+ AI LLM Models in Craveva AI Enterprise

Explore the comprehensive collection of 342+ AI LLM models available through **Craveva AI Enterprise**, including GPT-4, Claude 3.5, Gemini, and more. Learn how **Craveva AI Enterprise** helps you choose the right model for your F&B business.

Craveva AI Enterprise Team · Apr 25, 2025 · 8 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.

342+ AI Models: A CXO Guide to Building a Governed Model Portfolio (Craveva AI Enterprise)

For founders and CXOs, model choice is not a tech preference. It is a business control layer: cost per interaction, response time, accuracy, and operational risk.

This applies across F&B verticals: QSR, casual dining, fine dining, cloud kitchens, catering, bakeries, and franchise groups.

What “342+ models” actually gives you

Access to hundreds of models only matters if it creates three outcomes:

  • Lower unit cost for high-volume workflows
  • Better quality for high-impact decisions
  • Vendor resilience (you are not blocked by one provider outage)

The executive view: match models to workflows

Think in workflows, not providers:

  • Frontline conversations (WhatsApp ordering, support): prioritize speed + cost
  • Analysis and reporting (weekly margin pack, outlet variance): prioritize reasoning + accuracy
  • Document-heavy tasks (SOPs, policy, contracts): prioritize long-context understanding
  • Multimodal (menu images, invoices): prioritize image/document handling

The “portfolio” approach that prevents cost spikes

Most teams overspend by putting premium models on everything. A portfolio approach assigns tiers:

  • Tier A (high volume): fast, cost-effective models for routine interactions
  • Tier B (balanced): default models for standard ops and reporting
  • Tier C (premium): limited to executive reporting, complex investigations, and strategy

In Craveva AI Enterprise, you can set model choices per agent, per workflow, and evolve them over time.

Where the ROI comes from (practical examples)

Examples where model selection directly impacts cost, sales, time, and operations:

  • Order capture and upsell: cheaper models at scale reduce cost per order while improving conversion and AOV
  • Customer service: faster responses reduce refunds, chargebacks, and queue pressure
  • Procurement assistant: better accuracy reduces over-ordering and stockouts
  • Data analysis agent: higher-quality reasoning reduces leadership time and decision lag

Governance leaders should require

Model access is a governance topic:

  • Role-based model permissions (who can use premium models)
  • Budget caps and alerts by agent and by brand/outlet
  • Audit trails for model changes
  • Fallback rules when a provider is unavailable

A simple starting point

  1. Choose one revenue workflow (upsell, ordering, retention)
  2. Choose one cost workflow (procurement, waste, labor planning)
  3. Assign models by tier and track cost per outcome weekly

Next links: /solutions/architecture /pricing /contact

Craveva AI Enterprise gives F&B groups a model portfolio that can be governed like any other enterprise spend, with clear trade-offs between cost, speed, and quality.

KPIs to track

MetricArea
Peak-hour conversion vs queue timeSales
Ingredient substitution rate and margin impactOther
Safety stock breaches and recovery timeOther
Receiving errors and reconciliation timeOther
Customer rating trends vs operational driversOther
Schedule adherence and overtime varianceOther

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.

Showing 40 of 40 terms