If you're running a product built on Laravel, AI integration is no longer theoretical. Two official Laravel packages - Laravel AI and Laravel MCP - give you practical ways to bring AI into your product. Not as a gimmick, but as genuine functionality your users and your team can benefit from.
Two Different Directions
There are two sides to AI integration, and they solve different problems:
- Laravel MCP lets AI tools your team already uses - ChatGPT, Claude, and others - talk directly to your application. Your app becomes a data source and action layer for AI assistants.
- Laravel AI brings AI capabilities into your product itself - summarising data, generating content, powering in-app chat, or automating repetitive admin tasks.
Both are worth understanding, even if you only end up using one.
Laravel MCP: Bring Your App to the AI Tools You Already Use
MCP stands for Model Context Protocol. In plain English: it's a standard way for AI assistants to connect to external systems. Think of it as an API, but designed specifically for AI tools to understand and use.
Most teams already use AI chatbots day-to-day - asking questions, drafting documents, analysing data. The limitation is that those tools don't know anything about your business. They can't see your customer data, your orders, your reports. You end up copy-pasting information in and out.
Laravel MCP changes that. You expose parts of your Laravel application - data, actions, reports - through MCP, and AI assistants can access them directly. Securely, with authentication and permissions built in.
What that looks like in practice:
- Ask your AI assistant "show me this month's sales by region" and it pulls the data straight from your app.
- Say "create a draft invoice for client X" and it uses your app's logic to do it.
- Get a summary of open support tickets without logging into your dashboard.
The key insight is that your team doesn't need to learn a new tool. They keep using the AI assistants they're already comfortable with - those assistants just become more useful because they can see your data.
Laravel AI: Bring AI Capabilities Into Your Product
The other direction is embedding AI directly into your application. Laravel AI is a new official package that makes this straightforward - text generation, summarisation, classification, image generation, audio transcription, and more, all with familiar Laravel conventions. Whether you're building from scratch or adding to an existing product, AI features can be introduced incrementally.
The use cases I find genuinely valuable - rather than gimmicky - tend to fall into a few categories:
Summarising and Reporting
AI is good at turning large amounts of data into readable summaries. I've built features on an education platform that automatically generate introductory text and summaries from underlying data - work that previously took staff significant time to write manually. Reports, dashboards, data digests - anywhere your users spend time reading and interpreting data, AI can help.
Scoring and Classification
Another feature on the same platform uses AI to score school websites against a set of criteria and produce structured reports. The AI handles the assessment; the application handles the logic around it. This kind of automated analysis - classifying support tickets, scoring leads, flagging anomalies - is where AI integration delivers real, measurable value.
Smarter Admin
With appropriate safeguards, AI can speed up admin tasks significantly. Instead of clicking through dozens of records to make a bulk change, an admin could describe what they need: "enable this feature for all customers with a UK address." The AI interprets the request, the application validates and executes it. This needs careful implementation - you want confirmation steps and audit trails - but it can transform hours of repetitive work into seconds.
In-App Chat and Knowledge Search
Laravel AI supports agents with memory, tool use, and streaming - everything you need to build an AI assistant within your product. A user could ask questions about their account, get help navigating features, or request data in natural language.
What makes this genuinely useful - rather than a novelty - is RAG (retrieval-augmented generation). Without it, an AI chatbot only knows what's in its training data. With RAG, it searches your documents, help articles, product guides, or historical data first, then answers grounded in your actual content. Laravel AI has built-in support for file search and vector stores, so you can turn existing documentation into a searchable knowledge base without a separate infrastructure stack.
For products with a lot of unstructured content - support portals, internal tools, policy documents - this is where in-app AI starts to pay for itself.
A Word of Caution
AI integration is powerful, but it needs careful work. Prompts need thorough testing - not just for the happy path, but for edge cases and unexpected inputs. Anything that writes or modifies data needs extra caution: confirmation steps, validation, logging.
The risk isn't that AI doesn't work - it's that it works confidently even when it's wrong. Good implementation means building in the guardrails so your application stays reliable. This isn't something to bolt on without thought. Like any feature that touches your data, it needs the same rigour you'd apply to API design or database architecture.
What This Means for Your Product
If you have a Laravel application - or you're planning one - AI integration is now a practical option, not a research project. Whether it's letting your team's AI tools talk to your app via MCP, embedding AI features your users interact with directly, or both, the tooling is ready.
The question isn't whether the technology works. It's which parts would genuinely help your business. That depends on your product, your users, and what problems you're trying to solve.
If you'd like to explore what AI integration could look like for your Laravel product, let's talk. I work with product teams across Bath, Bristol, Wiltshire, and the UK, and I'd be happy to look at where AI could add real value to what you're building.
Common Questions
Do I need to rebuild my Laravel app to add AI?
No. Both Laravel AI and Laravel MCP are designed to integrate into existing Laravel applications. You can add AI features incrementally - start with one use case, see how it performs, and expand from there. There's no need to rewrite your codebase.
Is it safe to let AI access or modify my application data?
With proper implementation, yes. Laravel MCP includes authentication and permission controls, so you decide exactly what an AI assistant can see and do. For anything that writes or modifies data, you should add confirmation steps, validation, and audit logging - the same safeguards you'd apply to any admin action.
What is MCP (Model Context Protocol)?
MCP is a standard that lets AI assistants like ChatGPT and Claude connect to external systems. With Laravel MCP, your application exposes data and actions that AI tools can use directly - securely, with authentication built in. Your team keeps using the AI tools they already know; those tools just become more useful because they can see your data.
What is RAG (retrieval-augmented generation)?
RAG is a technique where AI searches your documents and data before answering a question, so responses are grounded in your actual content rather than just the model's training data. Instead of generic answers, you get answers based on your help docs, policies, product guides, or whatever content matters for your users. Laravel AI has built-in support for this via file search and vector stores.
Which AI providers work with these packages?
Laravel AI supports multiple providers including OpenAI and Anthropic (Claude), with automatic failover built in. Laravel MCP works with any AI tool that supports the Model Context Protocol standard - which already includes ChatGPT, Claude, and a growing list of others.