AI Feature Development for Laravel Applications

Laravel 13 ships a first-party AI SDK: text generation, tool-calling agents, structured output, embeddings, and vector-store integration behind one Laravel-native API that works across OpenAI, Anthropic, Gemini, and other providers. That changes the economics of adding AI to a Laravel application. The feature no longer needs a separate Python service, a second deployment pipeline, and a team that speaks two stacks. It can be a controller, an agent class, a queued job, and a migration — code your existing Laravel developers can read, test, and own.

Polish & Pixel designs and builds production AI features inside your existing Laravel application. Senior US-based Laravel consultants, working in your codebase, with the same engineering discipline we bring to every other part of your system.

What the Laravel AI SDK gives you out of the box

• Agents as PHP classes. An agent encapsulates its instructions, conversation history, tools, and output schema. php artisan make:agent scaffolds one; prompting it is a single method call.
• Structured output. Define a JSON schema on the agent and get typed, validated results back instead of parsing free text.
• Tools. Give an agent the ability to look things up in your database, call your services, or search your documents, with a built-in SimilaritySearch tool for retrieval-augmented generation (RAG).
• Embeddings and vector search. Generate embeddings from strings, store them in a native vector column on PostgreSQL with pgvector, and query with whereVectorSimilarTo straight from Eloquent.
• Streaming, queueing, and broadcasting. Stream responses to the browser as server-sent events, run long generations on the queue, or broadcast events over Reverb.
• Conversation memory, failover, and caching. Persisted conversations per user, automatic provider failover on rate limits, and cached embeddings.
• Testing fakes. Agent::fake() and assertion helpers mean your test suite never calls a paid model.

Use cases we build

Support and knowledge chat on your own data. A chat assistant grounded in your help centre, product docs, or internal policies, built as a RAG pipeline with the SDK's embeddings and vector search so answers cite real sources rather than hallucinating.

Document and intake extraction. Turn PDFs, emails, and uploaded forms into structured records: invoices into line items, applications into validated fields, contracts into key terms. Structured output plus Laravel validation gives you data you can trust enough to write to the database.

Semantic product and content search. Replace brittle keyword search with meaning-based retrieval over your catalogue or content, using pgvector and an HNSW index, with reranking where precision matters.

Internal copilots. Agents that draft replies, summarise tickets, classify inbound requests, or answer "what is the status of…" questions against your live data through tools, with human approval gates on anything that writes.

Provider strategy, cost, and latency

The SDK is provider-agnostic, and we treat provider choice as an engineering decision, not a brand preference. We will help you pick models per task (a small fast model for classification, a stronger one for generation), set token and latency budgets, use the OpenAI-compatible driver for self-hosted or local models where data residency demands it, and configure failover so a provider outage degrades gracefully. Every feature ships with a lightweight evaluation set so you can tell whether a model or prompt change made things better or worse before it reaches users.

Security and governance

AI features open a new attack surface. We design for it from the start: prompt-injection defences on any text that comes from users or documents, PII minimisation and redaction before content leaves your infrastructure, audit logging of prompts and responses, human-in-the-loop approval for tool calls with side effects, and rate limiting per user. Where your compliance posture requires it, we keep embeddings and conversation history in your own PostgreSQL instance rather than a provider-hosted store.

Why a Laravel-native build beats a bolt-on sidecar

A Python microservice for "the AI part" doubles your operational surface: separate auth, separate deploys, separate monitoring, and a network boundary inside every request. Building on the Laravel AI SDK keeps authentication, authorisation, queues, caching, validation, and observability exactly where they already are. Your existing tests cover the new code. Nightwatch and Pulse see the new requests. Your team can maintain it after we leave.

How an engagement runs

1. Discovery (one to two weeks). We identify the highest-value use case, confirm data access and provider constraints, and prototype against real data.
2. Build. Agents, tools, migrations, UI, evals, and tests, delivered in your repository through normal pull requests.
3. Harden and launch. Security review, cost and latency tuning, monitoring, and a staged rollout.
4. Hand-off. Documentation and a working session with your developers.

Ready to add AI features to your Laravel application with a team that knows both sides? Contact us to discuss your use case, or read our tutorial on getting started with the Laravel AI SDK.

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