Laravel AI That Holds Up in Production
Curotec designs AI as part of your Laravel architecture, not a bolt-on, so retrieval, cost, and failure are handled before they reach users.
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How we build AI that lasts in Laravel
Laravel absorbed AI the way it once absorbed queues and jobs: into the framework itself, as a first-party SDK your team runs in PHP. Agents, search, and automation ship without a separate service. We build on that foundation so the AI in your app behaves like the rest of your codebase, tested and dependable.
Our capabilities include:
- AI feature design and build, from data model to deploy
- AI added to an existing Laravel product without new tech debt
- Legacy PHP modernization with semantic search and MCP
- Security and spend guardrails designed into every AI feature
- Audits and hardening for AI you've already shipped
- AI strategy focused on where it pays off
Who we support
Most teams adding AI to Laravel fall into one of three situations. The right move depends on which one you’re in, and each starts from a different place. Here’s who we work with and what the work looks like for each.
VC-Backed SaaS Teams
Your roadmap is outrunning your engineering team, and AI features keep getting shipped fast and breaking quietly. We add agents, search, and automation to your live Laravel app without piling on tech debt. Velocity goes up and your quality floor holds.
Mid-Market Modernizers
Your platform still runs the business, but it blocks every new initiative, and the board wants an AI story you can't safely build on it yet. We modernize the codebase and add AI where it earns its place. The business keeps running while the technology catches up.
Teams Inheriting AI
You took over a Laravel app with AI features bolted on by a team that's gone, and now no one can tell what's safe, what it costs, or what data it exposes. We start by diagnosing what's actually there, then harden it into an AI layer you can trust and maintain.
Ways to engage
We offer a wide range of engagement models to meet our clients’ needs. From hourly consultation to fully managed solutions, our engagement models are designed to be flexible and customizable.
Staff Augmentation
Get access to on-demand product and engineering team talent that gives your company the flexibility to scale up and down as business needs ebb and flow.
Retainer Services
Retainers are perfect for companies that have a fully built product in maintenance mode. We'll give you peace of mind by keeping your software running, secure, and up to date.
Project Engagement
Project-based contracts that can range from small-scale audit and strategy sessions to more intricate replatforming or build from scratch initiatives.
We'll spec out a custom engagement model for you
Invested in creating success and defining new standards
Why choose Curotec for the Laravel AI SDK?
Most AI work fails the same way. It works in a demo and falls apart in the app. We build differently. AI goes in as a defined, tested layer that fits how Laravel is meant to work. What you ship stays maintainable long after launch day.
1
Extraordinary people, exceptional outcomes
Our outstanding team represents our greatest asset. With business acumen, we translate objectives into solutions. Intellectual agility drives efficient software development problem-solving. Superior communication ensures seamless teamwork integration.
2
Deep technical expertise
We don’t claim to be experts in every framework and language. Instead, we focus on the tech ecosystems in which we excel, selecting engagements that align with our competencies for optimal results. Moreover, we offer pre-developed components and scaffolding to save you time and money.
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Balancing innovation with practicality
We stay ahead of industry trends and innovations, avoiding the hype of every new technology fad. Focusing on innovations with real commercial potential, we guide you through the ever-changing tech landscape, helping you embrace proven technologies and cutting-edge advancements.
4
Flexibility in our approach
We offer a range of flexible working arrangements to meet your specific needs. Whether you prefer our end-to-end project delivery, embedding our experts within your teams, or consulting and retainer options, we have a solution designed to suit you.
What you can build with the Laravel AI SDK
Route Work to the Right Agent
Search Your Data by Meaning
Keep AI Running When a Provider Fails
Turn Messy Files Into Clean Data
Run AI Work in the Background
Test AI Features Like Any Other Code
The stack behind your Laravel AI features
Provider Support and Failover
We wire your app to OpenAI, Anthropic, and Gemini through one API, so a model swap is a config change.
- Unified Provider API – Call OpenAI, Anthropic, Gemini, and other providers through a single interface. Swapping models becomes a configuration change rather than a rewrite of your application code.
- Automatic Failover – Pass a list of providers with each request and let the SDK move to the next one when the first is unavailable, with no fallback logic to write or maintain yourself.
- Defined Failure Triggers – Failover activates on rate limits, provider overload, and exhausted credits. Ordinary application errors surface to your team instead of being silently retried elsewhere.
- Local Model Support – Run models locally through Ollama when compliance or data sensitivity means requests cannot leave your network, using the same interface as any hosted provider.
- Per-Feature Model Selection – Assign different models to different features based on what each one actually needs, instead of routing every request through one default model.
- Vendor Independence – Keep provider choice a decision you can revisit as pricing, availability, and model quality shift, rather than a commitment baked into your codebase.
Agents and Sub-Agents
Our engineers give each agent a tight scope and hand off specialized work to sub-agents instead of one catch-all.
- Scoped Agent Definitions – Give each agent its own instructions, memory, and approved tool set. Its behavior is defined in code rather than left to whatever the model decides to do.
- Intent-Based Delegation – A parent agent reads what the user actually wants and hands the request to the specialist built for it, replacing manual triage rules that break as your use cases grow.
- Narrow Sub-Agent Scope – Keep each sub-agent responsible for one job. Small context is what keeps answers accurate as the number of things your system handles increases.
- Eloquent Model Access – Equip agents with tools that query your existing Eloquent models directly, letting them answer questions about real records instead of guessing from training data.
- Agent Middleware – Inspect, log, or block what an agent is about to do before it acts, giving your team a control point for anything that touches sensitive data or external systems.
- Structured Output – Return responses validated against a defined schema. Downstream code receives predictable JSON it can rely on rather than free text it has to parse and hope about.
Vector Search on PostgreSQL
Curotec builds the retrieval layer on pgvector in your own database, because bad retrieval means confident wrong answers.
- Embeddings in Your Own Database – Store vector embeddings in the PostgreSQL database your application already runs, using pgvector, keeping your content inside infrastructure you control.
- No Separate Vector Service – Skip the standalone vector database you would otherwise have to provision, secure, monitor, and pay for as a second system alongside the one you have.
- Similarity Search as an Agent Tool – Wire retrieval directly into your agents. They look up relevant material before answering rather than responding from whatever the model already knows.
- Reranking – Run a second pass over retrieved results to push the most relevant ones to the top, which matters when the first pass returns twenty passages and only three are useful.
- Document-Specific Chunking – Split your source material along the boundaries that make sense for it, since a contract, a support article, and a transcript each break apart differently.
- Retrieval Evaluated Before Launch – Test what the search layer returns for real questions before any agent depends on it, because weak retrieval produces answers that sound right and are wrong.
Background Processing
We move slow model calls onto the Laravel queues your app already runs. Nobody waits on a page for an AI response.
- Queued Model Calls – Push AI work onto the same Laravel queue workers your application already runs. Nothing new has to be provisioned or learned to handle it.
- Long-Running Jobs Off the Request Cycle – Move bulk analysis, transcription, and multi-step agent chains into the background, where they can take the time they need without holding a user’s browser open.
- Streamed Responses – Send output to the interface as the model generates it, letting a user see an answer forming instead of staring at a spinner while the full response is assembled.
- Broadcast on Completion – Push results to the browser the moment a background job finishes, which removes the need for polling and keeps the interface current without extra requests.
- Retries and Failure Handling – Handle AI job failures the same way your queues already handle every other failure, letting your team debug them with the tools and patterns they know.
- Per-Job Timeouts – Set a time limit on each job, keeping one slow or hanging model call from occupying a worker indefinitely and stalling the tasks queued behind it.
Cost and Reliability Controls
Our engineers route cheap work to cheap models and cache embeddings, keeping token spend predictable as usage grows.
- Cheaper Models Where They Fit – Route work to a less expensive model when the expensive one buys you nothing. You are not paying frontier prices to classify a support ticket.
- Cached Embeddings – Store embeddings once and reuse them, keeping the same document from being sent through a model and billed again every time something in your application needs it.
- Usage Tracked Per Feature – Attribute token spend to the feature that caused it, which turns a single unexplained invoice into a list you can act on.
- Limits Set Before Launch – Define rate limits and spend ceilings while you are building, rather than discovering where they should have been after a bill arrives.
- Lean Context Windows – Send the model what it needs and nothing else, since context is the thing you pay for and padding it is the easiest way to quietly double a bill.
- Cost Reviewed as Usage Grows – Revisit model choice and spend as real traffic arrives, because the economics that worked at a hundred requests a day rarely hold at a hundred thousand.
Testing and MCP Tools
Curotec fakes model responses in your test suite and connects agents to your live systems through MCP tools.
- Faked Model Responses – Replace live model calls with fixed responses in your test suite. Tests run fast, cost nothing, and return the same result every time you run them.
- Assertions on Agent Behavior – Verify which tools an agent reached for and what it did with them, rather than checking only that some text came back at the end.
- MCP Tool Connections – Connect agents to the systems your team already runs through MCP, letting an agent act on real data in real tools instead of working from a copy.
- Provider Tools Where They Earn It – Enable capabilities like web search and file search selectively, on the features that need them, rather than switching everything on by default.
- Evaluations for Quality Drift – Score agent output against known-good examples. A prompt edit or a model version change surfaces in a test run instead of in a customer complaint.
- AI Covered in CI – Run these checks in your existing pipeline alongside the rest of your test suite, where an AI regression blocks a merge the same way any other regression would.
FAQs about our Laravel AI implementation
Do we need Python or a separate AI service?
No. The Laravel AI SDK runs in PHP, inside the application you already have. Agents, embeddings, vector search, and structured output all work without standing up a second service in another language, which means your existing team can maintain what gets built instead of depending on a specialist who speaks a different stack.
Can we add AI without disrupting our current roadmap?
Yes, and this is more common than a greenfield build. We add AI to a live Laravel codebase as a defined layer, with its own tests, keeping it from entangling itself with the rest of your application. The work runs alongside your roadmap rather than pausing it.
What happens when our AI provider goes down?
The SDK supports passing a list of providers with each request. If the first one is rate limited, overloaded, or out of credits, the next one handles it, with no fallback code for your team to write or maintain. Provider choice stays a configuration decision rather than something buried in your application logic.
How do we keep token costs from getting out of control?
Cost is a design decision, not a billing problem. We route work to cheaper models where a more expensive one buys you nothing, cache embeddings to avoid paying for the same text twice, keep context lean, and track usage per feature to keep spend attributable. Limits get set before launch rather than after a surprise invoice.
Can AI features be tested like the rest of our code?
Yes, and this is one of the clearest advantages of building AI inside Laravel. Model responses can be faked in your test suite, which keeps tests fast and repeatable, and you can assert on what an agent actually did rather than just checking that text came back. These checks run in your existing pipeline alongside everything else.
Can you take over AI someone else built?
This is a large share of what we do. We start by finding out what is actually there: what data reaches the model, what it costs, where it fails, and what happens when it does. From there we harden it into a maintainable layer, or replace the parts that cannot be salvaged.
Ready to have a conversation?
We’re here to discuss how we can partner, sharing our knowledge and experience for your product development needs. Get started driving your business forward.