Web App Development
AI-Powered Web App Development With LLMs, RAG, and Vector Search
We build AI-powered web apps that go beyond ChatGPT wrappers — retrieval-augmented generation, vector embeddings, streaming UX, and guardrails that keep your AI product reliable in production.
Why AI Web App Development Is Harder Than It Looks
The gap between 'adding an AI feature' and 'building an AI-powered web app' is enormous. Wrapping a GPT-4o API call in a fetch request is the former. The latter requires vector embedding pipelines for your knowledge base, retrieval-augmented generation architecture that returns accurate results without hallucinating facts your documents do not contain, streaming responses that update the UI token by token, usage-based billing that does not bankrupt you at scale, and guardrails that prevent the AI from going off-script in production. We have built both levels and know exactly which problems only emerge at the second.
AI feature performance is non-trivial to get right. A naive RAG implementation that embeds queries at request time and performs brute-force similarity searches adds 3–5 seconds of latency to every AI response — unacceptable for a responsive user experience. The correct architecture: pre-compute embeddings for your knowledge base documents, store them in pgvector (PostgreSQL's vector extension, built into Supabase), use approximate nearest-neighbor indexes for sub-100ms similarity search, and cache common query results. We build AI web apps with this performance-conscious architecture from day one.
LLM output reliability is the most underestimated challenge in AI web app development. Language models hallucinate, contradict themselves, and produce outputs in unexpected formats — even with structured output modes and detailed system prompts. We build AI web apps with validation layers that parse LLM outputs against expected Zod schemas, fallback prompts for when first-pass outputs fail validation, and monitoring dashboards that surface hallucination rates and validation failure frequencies so you can improve prompts systematically over time.
Our Approach to Ai web app development
Every project follows our 4-step vibe-coding process — AI handles the boilerplate, senior engineers handle the craft. From idea to live product in 3–7 days for MVPs.
Discovery
We map your AI feature requirements: what knowledge base does the AI draw from, what questions will users ask, what output format does the product require, and what happens when the AI gets it wrong. We define the accuracy requirements, the acceptable failure modes, and the monitoring strategy before selecting the LLM provider and embedding model.
Design
We design the RAG pipeline architecture: document chunking strategy, embedding model selection, vector index configuration, retrieval query design, and prompt template structure. We also design the streaming UI: how does the user experience partial responses, and how does the interface signal uncertainty or failure? These design decisions affect AI accuracy and perceived performance more than model selection.
Build
pgvector in Supabase for vector storage with HNSW approximate nearest-neighbor indexes, Anthropic Claude API or OpenAI API for generation, streaming responses via the Vercel AI SDK for real-time token delivery to the UI, Zod schemas for structured output validation, and usage tracking in PostgreSQL for per-user token consumption and cost monitoring.
Launch
Pre-launch AI evaluation: test suite of representative user queries with expected outputs, automated evaluation of RAG retrieval accuracy, and manual review of 50 diverse prompt-response pairs. We configure Langfuse or similar for production prompt monitoring before go-live. We do not launch AI features without baseline accuracy metrics established.
What You Get
Every ai web app development engagement includes these deliverables — scoped before we start, delivered before we invoice.
- RAG pipeline: document ingestion, chunking, embedding generation, and vector storage in pgvector
- Vector similarity search with HNSW index for sub-100ms retrieval at production scale
- LLM integration via Anthropic Claude API or OpenAI API with retry logic and timeout handling
- Streaming response UI with Vercel AI SDK for real-time token display
- Structured output validation with Zod schemas and fallback prompt on validation failure
- Usage tracking: token consumption per user, per session, and per feature for cost monitoring
- Prompt management system: version-controlled prompt templates with A/B testing capability
- AI guardrails: input sanitization, output filtering, and jailbreak detection hooks
- Production monitoring: response latency, validation failure rate, and hallucination flagging
- Admin dashboard for reviewing AI conversations, flagging problematic outputs, and tuning prompts
Tech Stack We Use
AI web app development at Greta uses the Anthropic Claude API as the primary LLM provider — Claude's instruction following and structured output reliability are best-in-class for production applications. For vector embeddings, we use the Voyage AI embedding model or OpenAI text-embedding-3-small stored in Supabase's pgvector extension with HNSW indexes for fast approximate nearest-neighbor search. The Vercel AI SDK handles streaming response delivery from server to client with proper backpressure management. All LLM outputs are validated against Zod schemas before being displayed to users. Usage is tracked in PostgreSQL per user and per session, with daily cost aggregation alerts that fire when per-user token consumption exceeds a configured threshold. We have built AI features on this stack across multiple production applications and know which prompt patterns produce reliable structured outputs.
Case Study
SEO Pilot — AI-Powered Keyword Analysis
SEO Pilot uses AI to analyze keyword competitiveness and generate content briefs for each analyzed keyword cluster. We built the AI pipeline on the Anthropic Claude API: a structured output prompt that returns keyword difficulty scores, semantic cluster labels, and content brief outlines in a Zod-validated JSON format. The pipeline processes keyword batches asynchronously via a BullMQ job queue, so users see streaming progress updates without blocking the UI on API response times. We implemented a validation layer that retries with a clarifying prompt when Claude returns malformed JSON — an edge case that occurred on roughly 2% of requests without the retry layer. Post-validation failure rate dropped to under 0.1% after the retry prompt was implemented.
Read full case studyPricing Transparency
AI-powered web app development starts at $8,000 — higher than our standard floor due to the RAG pipeline complexity, LLM provider setup, evaluation infrastructure, and production monitoring requirements. Full AI-powered applications with custom knowledge bases, fine-tuned prompts, and multi-modal capabilities run $20,000–$60,000. LLM API costs are billed directly to your account and are separate from development fees.
MVP
From $5,000
3–7 business days
Full Build
From $15,000
2–4 weeks
All projects include full code ownership, two revision rounds, Vercel deployment, and one week of post-launch support. No hidden fees.
Frequently Asked Questions
Which LLM provider do you use for AI web apps?
We default to the Anthropic Claude API for most production applications — Claude's instruction following, structured output reliability, and context window size are best for the RAG and tool-use patterns we build most often. We use OpenAI's API for applications that need specific models like DALL-E for image generation. We help you choose based on your specific requirements, not vendor preference.
What is RAG and why does it matter for AI web apps?
Retrieval-Augmented Generation means giving the LLM access to your specific documents or data at query time, rather than relying only on its training data. RAG is what makes AI web apps answer questions about your specific product, policies, or knowledge base accurately — instead of hallucinating plausible-sounding but wrong answers. Almost every production AI feature that works with proprietary information uses RAG.
How do you prevent the AI from hallucinating?
Hallucination is reduced but not eliminated by grounding the LLM with retrieved context (RAG), using structured output prompts that constrain the response format, validating outputs against Zod schemas, and monitoring validation failure rates in production. We also include explicit instructions in the system prompt to cite retrieved context and indicate uncertainty rather than fabricating confident answers.
Can you build a chatbot on our documentation or knowledge base?
Yes. This is the most common AI web app we build. We ingest your documentation, chunk it into semantically coherent pieces, generate embeddings, store them in pgvector, and build a retrieval pipeline that finds the most relevant chunks for each user query. The LLM generates answers grounded in your actual documentation — not general knowledge.
How do you handle AI API costs at scale?
We implement per-user token usage tracking from day one. Usage is logged in PostgreSQL with daily aggregations and configurable cost alerts. We configure rate limiting per user tier — free users get fewer AI calls than paid users. We also implement response caching for frequently repeated queries, which can reduce API costs by 40–60% for knowledge base chatbots.
How long does AI-powered web app development take?
A basic AI feature — a chatbot or content generator using RAG on a static knowledge base — takes 1–2 weeks. A full AI-powered web app with custom pipelines, user-specific knowledge bases, streaming UI, and production monitoring takes 3–6 weeks. AI development is slower than standard web development because evaluation and iteration on prompts and retrieval accuracy requires more testing cycles.
Can you fine-tune an LLM for our use case?
For most use cases, prompt engineering and RAG outperform fine-tuning at a fraction of the cost and complexity. Fine-tuning makes sense for very specific output styles, specialized domains where general models underperform, or extremely high-volume applications where reducing token usage matters. We advise on whether fine-tuning is justified after evaluating your specific requirements.
How do you monitor AI features in production?
We configure Langfuse or a similar LLM observability tool to log every prompt, completion, retrieval result, and validation outcome. This gives you a searchable history of every AI interaction, which is essential for identifying systematic prompt failures, measuring accuracy improvements, and debugging edge cases that only emerge in production with real user queries.
Ready to ship?
Ready to build your AI-powered web app?
Start Your ProjectOr reach us directly at hello@greta.agency
Written by the Greta Agency team · Last updated April 2025