Your AI Feature Shipped Last Quarter. Nobody Uses It.
If you're a product lead who greenlit 'AI' and got a chatbot nobody opens, here's how we build RAG, semantic search, and automation your users actually need.
AI in production web applications
I build AI features that are genuinely useful, not feature theatre. Retrieval-augmented generation (RAG) for documentation search, automated content pipelines, semantic search over structured data, and AI-assisted workflows that reduce repetitive work for your team.
What I build
Content generation pipelines using Claude and GPT-4. Semantic search with pgvector and Supabase. RAG systems over your documentation or knowledge base. Automated image generation workflows with FAL API. AI scoring and quality assessment pipelines. Every project is built with the Vercel AI SDK or Anthropic SDK -- battle-tested, production-ready.
The honest limits of AI in web apps
AI features add latency, cost, and non-determinism to your application. I scope AI features carefully -- using them where they create genuine value, and not adding them because they are fashionable. Every AI feature I build has a fallback for when the model behaves unexpectedly.
Need help with your ai feature shipped last quarter. nobody uses it.?
Get a free quoteCommon questions
What AI models do you use?
Claude (Anthropic) for content generation and reasoning tasks — it produces the most consistent, high-quality text output. GPT-4o for multimodal tasks. Smaller, faster models (Claude Haiku, GPT-4o mini) for high-volume, latency-sensitive operations.
What is RAG and when do I need it?
RAG (Retrieval-Augmented Generation) lets an LLM answer questions based on your specific content — your documentation, knowledge base, or product data. Without RAG, the model only knows its training data. With RAG, it can answer accurately about your specific content.
How do you handle AI costs in production?
I implement caching for repeated queries, use smaller models for classification and routing, and larger models only for generation. I set up cost monitoring alerts in the AI provider dashboard and optimise prompts to reduce token usage.
Can you add AI search to my existing site?
Yes. The typical implementation: embed your content with a text embedding model, store vectors in pgvector (Supabase), and query them semantically at search time. I have added this to Next.js and Astro sites in existing codebases.
Is AI content generation good for SEO?
It depends entirely on the quality and the process. AI-generated content that passes through proper human review, NLP scoring, and originality checks can rank well. Unreviewed, low-quality AI output is increasingly penalised by Google. I build content pipelines with quality gates, not bulk generators.
Ready to get started?
Free consultation. No commitment. Just an honest conversation about your project.
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Whether it's a migration, a new build, or an SEO challenge — the Social Animal team would love to hear from you.