Next.js · TypeScript · AI-native systems
AI web app development for teams that need fast launch speed and production reliability: full-stack Next.js builds, agent workflows, and model-backed product features.
An AI web app is a product whose core value comes from a model — a copilot, a retrieval-grounded search, an extraction surface, a recommendation feed — not a chatbot bolted onto CRUD. The build is different from a normal web app: the model UX, the data it retrieves, latency, spend, and safety are all part of the product surface. I build AI web apps where the front-end, the model layer, and the backend are designed as one system, and the page still renders crawlable HTML for search.
Copilots, summarization, extraction, classification, semantic search, and recommendations tied to a real user job — designed as the product, not a chat widget in the corner.
Retrieval over your own content and data with grounded, cited answers and a UI users trust. Vector store, chunking strategy, relevance evals, and the front-end that makes it usable.
App Router, TypeScript, Supabase, APIs, auth, and deployment built as one production system — not a frontend hunting for a backend after launch.
Token streaming, optimistic UI, background jobs, and graceful loading and error states so model latency feels fast instead of broken.
SSR/ISR, metadata, structured data, and crawl-safe rendering so model-powered pages are discoverable by Google and AI search — not invisible client-only output.
Rate limits, spend caps, retries, prompt-injection boundaries, and content guards so AI features stay predictable and affordable under real load.
A web app whose core value comes from a model: a copilot, a RAG search, an extraction pipeline with a UI, a recommendation surface. The test is simple — if the AI can be removed without changing what the product is for, it is a normal web app with a chatbot, and a much smaller build.
Buy when an off-the-shelf tool already covers the workflow and you don't need your own data or a bespoke UX. Build when the model touches proprietary data, the UX is the product, or per-seat vendor pricing won't scale. A short decision pass usually pays for itself.
Streaming, caching, optimistic UI, and moving slow work to background jobs. Perceived latency is a UX problem you design around — most of the speed users feel comes from the interface, not just a faster model.
Only if they server-render real HTML with metadata and structured data. Client-only model output is invisible to crawlers and to AI Overviews. SEO-safe rendering has to be designed in, not retrofitted after the app already ships as a blank SPA.
What the model does for the user, what data it needs, where it must not be wrong, and the smallest version worth shipping.
Retrieval sources, prompt and tool schema, spend and safety boundaries, and the eval that decides when output is good enough.
Next.js UI, streaming, loading and error states, backend, auth, and SEO-safe rendering implemented as one system.
Rate limits, spend caps, observability, and review on security and performance before users arrive.
50+
Real model-backed products with auth, data, and ongoing maintenance — not portfolio demos.
Next.js-native
Metadata and structured data baked in, so AI-powered pages are discoverable instead of blank SPAs.
3-5x
AI-assisted implementation with senior review keeps velocity high without shipping silent regressions.
Bring the user job, the data, and whether the AI is the product or a feature. I'll map the shortest path to a fast, reliable, search-visible AI web app.
Get a real, shippable model-backed web app in front of users in weeks, with the data and UX designed as the product from the start.
Book a build callBuild a copilot, search, or extraction surface into your existing app without a prototype rewrite or a stalled handoff.
Hire an AI developerShip model-powered pages that actually rank, with SSR, metadata, and structured data designed in.
See technical SEOAn AI web app is a product whose core value comes from a model — a copilot, a RAG search, an extraction surface — not a chatbot bolted onto CRUD.
If the AI can be removed without changing what the product is for, it is a normal web app with a chatbot, and a much smaller build.
Model-backed pages only rank in search when they render real HTML with metadata and structured data; client-only output is invisible to crawlers.
Perceived latency in an AI app is a UX problem solved with streaming, caching, and optimistic UI — not just a faster model.
Build an AI web feature in-house when the model touches proprietary data or the UX is the product; buy when an off-the-shelf tool already covers the workflow.
A production AI web app needs spend caps, rate limits, retries, and safety boundaries before launch, or cost and reliability become the first incident.
Web + AI Integration Depth
I build both the product surface and the system underneath it, so shipping does not stall at handoff boundaries.
Production Tooling
Typed contracts, CI checks, and telemetry from day one keep iteration velocity high without silent regressions.
Operator-Led Delivery
One accountable owner from scope to production rollout.
Bring your product idea or existing app. I'll map the shortest path to a reliable, shippable AI-enabled version.
Get in touchAI web app development combines standard web engineering (frontend, backend, data, auth, deployment) with model-driven functionality such as extraction, generation, recommendations, retrieval-grounded search, or agent workflows — where the model feature is the core value of the product, not a bolt-on.
In an AI web app, the model is the product: a copilot, a RAG search, an extraction surface. Remove it and the product no longer does its job. A normal web app with a chatbot works fine without the chatbot — it's a smaller, cheaper build with very different reliability and UX requirements.
A focused MVP typically takes 3–6 weeks. More complex multi-role SaaS builds with retrieval, integrations, and reliability requirements usually take 6–12 weeks.
Only if they server-render real HTML. With Next.js SSR/ISR, metadata, and structured data, model-powered pages are fully crawlable and eligible for rich results and AI Overviews. A client-only React app that renders content after load is often nearly invisible to search — which is why SEO-safe rendering has to be part of the build, not an afterthought.
Spend caps and rate limits per user and per endpoint, response caching, model routing (cheaper models for easy calls), prompt caching, and batching where latency allows. Cost-per-task is engineered in from the start so it stays predictable as usage grows.
Primarily Next.js App Router, TypeScript, Supabase, and model APIs through disciplined service layers, plus MCP when agent workflows need external tool access, and a vector store when the app needs retrieval over your own data.