Web Apps With AI Built In — By One Engineer
Most teams split the people who build the product from the people who add the AI. I do both. I own the database, API, and UI andthe LLM layer — so the AI feature is genuinely wired into a production app, with evals, guardrails, and a bill that doesn't surprise you.
- RAG assistants & chatbots grounded in your docs — with citations
- Tool-using AI agents with guardrails and human-in-the-loop
- AI features inside a full Next.js + Node.js + PostgreSQL app
- Evals, prompt-injection defenses & cost control by default
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Full-stack
App and AI in one pair of hands — schema, API, UI, and the LLM layer, no hand-offs
4+
Years shipping production web apps for FinTech, SaaS, and enterprise teams
Eval-gated
AI features ship behind a golden dataset and automated evals — not vibes
60%
Typical token-cost reduction via model routing, caching, and right-sizing
What does a full-stack AI developer do?
A full-stack AI developer builds the entire web application and the AI capability inside it — database, backend API, frontend, and the LLM layer (prompts, retrieval-augmented generation, agents, and evaluations). Smit Parekh is a full-stack AI developer with 4+ years of production experience who builds AI chatbots, agents, and AI-native SaaS using Next.js, Node.js, and PostgreSQL together with OpenAI, Anthropic Claude, and open-source models — shipping AI features that are grounded, evaluated, guard-railed, and cost-controlled, not just demoable.
AI, End to End
What I Build With AI
From a grounded support chatbot to an AI-native SaaS product — the AI capability and the application around it, owned by one engineer.
AI Chat & RAG Assistants
Support bots and on-site assistants grounded in your docs — with citations, streaming, and honest 'I don't know' instead of confident hallucination.
AI Agents & Automation
Tool-using agents that run real workflows against your systems, with guardrails, human-in-the-loop checkpoints, and step/cost limits that keep them safe.
AI Features Inside Your Product
Summarisation, classification, extraction, semantic search, and generation wired into your existing app with structured outputs and proper error handling.
AI-Native SaaS, End to End
The whole product — multi-tenant Next.js app, Stripe billing, auth, admin tools — with the AI capability as a first-class, metered, monitored part of it.
AI-Ready Backends & APIs
Typed Node.js APIs, vector stores, queues for async generation, webhook handlers, and usage metering — the plumbing that makes an AI feature reliable at scale.
Evals, Guardrails & Cost Control
Golden datasets, automated evals on every change, prompt-injection defenses, and cost dashboards — so your AI stays accurate, safe, and affordable in production.
Why Hire Me
Why a Full-Stack AI Developer Beats a Split Team
When the same engineer owns the product and the AI layer, the feature actually ships — grounded, evaluated, and affordable.
I build the app and the AI inside it
Most 'AI consultants' can prototype a prompt but can't ship the product around it. I own the database, the API, the UI, and the LLM layer — so the AI feature is actually wired into a real, deployed application, not a Streamlit demo.
Production discipline, not demo magic
An AI demo takes an afternoon. An AI feature that doesn't hallucinate on edge cases, doesn't leak prompts, and doesn't surprise you on the bill is an engineering project. I bring evals, guardrails, retries, and fallbacks by default.
Cost-aware by design
Smaller models like Claude Haiku or GPT-4o-mini for routine work, premium models reserved for hard reasoning, prompt caching, and context discipline. Your AI bill should be predictable, not a monthly surprise.
Model-agnostic, future-proof
OpenAI, Anthropic Claude, or open-source — chosen per task and benchmarked on your data. The orchestration layer is built so you can switch models as the frontier moves, without a rewrite.
Related Services
Explore the AI Work in Detail
Each capability has a dedicated service page with scope, process, and pricing.
AI Integration
OpenAI, Claude, RAG & evals wired into your app
Learn moreAI Agent Development
Tool-using autonomous agents with guardrails
Learn moreAI Chatbot Development
RAG assistants grounded in your docs, with citations
Learn moreFull-Stack Developer
The non-AI full-stack work — React, Next.js, Node.js
Learn moreCommon Questions
Full-Stack AI Development, Answered
The questions clients ask before adding AI to their product.
What is a full-stack AI developer?
A full-stack AI developer builds the complete web application and the AI capability inside it — database, API, frontend, and the LLM layer (prompts, RAG, agents, evals). Instead of one person prototyping a prompt and a separate team shipping the app, one engineer owns both, so the AI feature is actually integrated into a production product.
Which AI models and tools do you work with?
OpenAI (GPT-4o and mini), Anthropic Claude, and open-source models like Llama and Mistral, plus RAG stacks (pgvector, Pinecone), orchestration (LangGraph, the Vercel AI SDK, the OpenAI and Claude agent SDKs), and eval tooling (Promptfoo, LangSmith). I pick per task and benchmark on your data rather than defaulting to one vendor.
How do you stop AI features from hallucinating or leaking data?
Grounding answers in your real content with RAG and citations, enforcing structured outputs, filtering inputs and outputs, defending against prompt injection, and gating every change behind a golden-dataset eval suite. High-risk actions get human-in-the-loop approval. The goal is an AI feature that's incapable of the worst outcomes, not just discouraged from them.
Will running AI in production be expensive?
Usually far less than people fear. I default to smaller models for routine turns, reserve premium models for hard reasoning, cache aggressively, and keep context tight — typically a 60% cost reduction versus a naive GPT-4-everywhere build. You get a realistic monthly token estimate before we start.
Can you add AI to my existing app instead of building from scratch?
Yes — that's common. Share the repo and I'll assess where AI genuinely helps (and where it doesn't), then wire the feature into your existing stack with the same evals, guardrails, and cost controls I'd use on a greenfield build.
Do you also handle the non-AI parts of the product?
Yes. I'm a full-stack developer first — see /full-stack-developer for the core web work. The advantage of one engineer is that the AI layer and the product around it are designed together, not bolted on afterward.
Ready to add AI to your product — properly?
Send your brief. I'll reply within 24 hours with a written proposal — scope, timeline, model recommendation, and a realistic cost estimate. No discovery calls until you've seen the numbers.