AI That Actually Runs
I don’t build demos. I don’t build proof-of-concepts that fall apart the moment real data hits them. I build AI systems that run in production with Laravel queues, PostgreSQL storage, proper retry logic, and predictable costs.
Dadooo.ai is the clearest example. I co-founded and built it from scratch: multi-model LLM integration (GPT-4o, Claude 3.5, Gemini, DeepSeek), credit-based usage tracking, streaming responses, image generation, text-to-speech, and computer vision. Not a wrapper around an API. A full SaaS platform with billing, multi-tenancy, and domain-separated architecture across 8 bounded contexts. It runs 24/7, costs are predictable, and the AI is just another service with the same reliability guarantees as everything else in the stack.
Picking the Right Approach
Not everything needs AI, and not every AI problem needs a custom model. I’ve talked clients out of building RAG systems when a well-designed prompt would solve their problem. I’ve also built full vector search pipelines when the data volume demanded it. The question is always: what’s the simplest thing that actually works?
RAG Pipelines
When your data changes frequently, when context windows aren’t large enough, or when users need citations back to source documents, RAG is the right call. I build these with pgvector inside PostgreSQL. Documents get chunked, embedded, and indexed. At query time, relevant chunks get retrieved and passed to the LLM alongside the question.
Why pgvector instead of Pinecone or Weaviate? Operational simplicity. Embeddings live in the same database as application data. One backup strategy, one deployment, one set of access controls. I don’t want to manage a separate vector database that could drift out of sync.
Direct LLM Integration
For classification, extraction, summarization, or generation tasks, direct API calls with structured output parsing are often enough. The LLM becomes just another service in the application. I build these with typed contracts, JSON mode responses, retry logic, and timeout handling. Nothing exotic, just reliable.
On Dadooo.ai, the AI text generation module works this way: configurable system prompts, template-driven generation, model switching mid-conversation, and credit deduction per request. Users never see the complexity behind it.
Structured Data Extraction
The most underrated use case. Documents, emails, PDFs, invoices. Every business has unstructured data sitting in folders that someone manually copies into a system. I build pipelines that extract, validate, and structure this automatically. On Dadooo.ai, the invoice parser handles PDF, DOCX, and XLSX with VAT calculation and QR payment code generation.
How I Structure AI in a Codebase
AI pipelines don’t get special treatment in my architecture. They follow the same DDD boundaries as everything else:
- Ingestion: receives input from the application, API, or file upload
- Queue processing: Laravel Horizon distributes jobs across workers with priority routing. Heavy AI work never blocks the main request.
- AI service layer: behind an interface. If I need to swap Claude for GPT-4 on a specific task, I change one binding. The rest of the application doesn’t know or care which model answered.
- Validation: structured outputs get validated against business rules before touching the database. AI output that doesn’t conform gets rejected, retried, or routed to a human.
- Storage: results, embeddings, and full audit logs in PostgreSQL
- Cost tracking: per-request token counts, daily and monthly budgets, automatic fallback to cheaper models near thresholds
On Dadooo.ai, this architecture handles multi-model chat streaming, image generation through Runware, ElevenLabs text-to-speech, and trend analysis, all through the same pipeline structure.
The Things Demos Skip
The gap between a demo and production is where most AI projects die:
- Cost controls: I track tokens per request, set budget limits, and fall back to cheaper models automatically. On Dadooo.ai, every user action that touches AI deducts credits with full transparency on what was consumed.
- Rate limiting: exponential backoff and request queuing. If OpenAI rate-limits you at 3 AM, the job retries in 30 seconds instead of crashing.
- Data privacy: sensitive data doesn’t leak into prompts. All processing stays on EU infrastructure via Hetzner. No data gets sent to US-hosted services without explicit consent flows.
- Audit trails: input, output, model, cost, latency, all logged. When a client asks “why did the system decide X?” I can show them the exact prompt and response.
- Fallback chains: if Claude is down, the system tries GPT-4. If that’s down, it queues the job and alerts me. It never just… fails silently.
- Human-in-the-loop: confidence thresholds route uncertain results to human reviewers. Bad automated decisions are more expensive than slow human ones.
Tech I Use
Claude for complex reasoning, long-context tasks, and structured extraction. It’s my default for anything that needs to think. Tool use enables clean integration patterns.
OpenAI for embeddings (text-embedding-3-small), high-throughput classification, and as a fallback in multi-model setups.
pgvector inside PostgreSQL. Same connection pool, same transactions, same backup infrastructure. No operational overhead from a separate vector store.
Redis for caching embedding results, rate limit state management, and pub/sub for real-time pipeline updates.
Laravel Horizon for queue management, job prioritization, and visibility into what’s processing, what failed, and why.
Who Needs This
You have a business process that involves repetitive pattern recognition, classification, extraction, or content generation at a volume where doing it manually is either too expensive or too slow. You want structured, reliable outputs you can build business logic on top of. Not a chatbot. Not a novelty.
If you want a ChatGPT-like widget on your website, use an off-the-shelf solution. If you need AI as a real part of your backend that handles failure, tracks costs, and produces outputs your system can trust, let’s talk.
Frequently Asked Questions
How much does AI integration cost to run monthly?
It depends on volume and model choice. A typical extraction pipeline processing 1,000 documents per day with Claude costs €200-500/month in API fees. I build cost tracking into every system so you see exactly what you’re spending per operation. Budget limits prevent surprises, and I configure model fallback chains so high-volume, low-complexity tasks use cheaper models automatically.
Will my data be used to train AI models?
No. Both Anthropic (Claude) and OpenAI’s API terms explicitly state that API data isn’t used for training. I also keep all processing on EU infrastructure via Hetzner, and I implement PII redaction when prompts might contain sensitive information. Your data goes to the model, gets a response, and that’s it.
Can you integrate with our existing Laravel application?
Yes. AI integration doesn’t require a separate system. I add it as a domain module within your existing Laravel codebase following the same DDD patterns as the rest of your application. Queue workers handle async processing, and the AI service sits behind an interface that your existing code calls like any other service.
What happens when the AI gives wrong answers?
Every system I build has validation layers. Structured outputs get checked against your business rules before anything touches the database. Confidence scoring routes uncertain results to human review. Retry logic handles transient failures. And full audit logging means you can trace any decision back to the exact prompt and response that produced it. The system fails safe, not silent.
Do I need to choose between Claude and OpenAI?
No. I build model-agnostic pipelines behind a clean interface. On Dadooo.ai, users can switch between GPT-4o, Claude 3.5 Sonnet, Gemini Pro, and DeepSeek mid-conversation. The architecture supports multiple providers simultaneously with automatic fallback. You pick the best model for each task, not one vendor for everything.