AI Integration for Your App — ChatGPT, Claude & RAG

Your product already works. The goal here is to make it smarter, deflect repetitive support, turn your own content and data into answers, and automate the manual steps, without rebuilding from scratch. I add deep, production-grade AI features to an existing website, SaaS platform or web app, wired directly into your data, workflows and business logic rather than dropped in as a generic widget.

What gets added

• Support chatbot grounded in your content — answers from your docs, FAQs and knowledge base instead of generic replies.
• Document Q&A with RAG — users ask questions of your PDFs, manuals and files; answers cite the source material.
• Content generation — drafts for product descriptions, blog posts and email copy on demand.
• Semantic / natural-language search — meaning-based results, not just keyword matching.
• AI-driven workflows — lead scoring, routing, summarisation, content moderation and alerts.
• Speech & image features — transcription, text-to-speech, image generation and processing where they fit your use case.

How it's delivered

1. You share access to the current site or app and describe the feature you want.
2. I review the codebase and propose an integration approach that avoids unnecessary rewrites, with a cost and token-usage estimate up front.
3. I build, test and deploy the feature with proper error handling, rate limiting, fallbacks and cost controls.
4. You receive the full source code, deployment, and a walkthrough of how it works.

Tech stack

AI: OpenAI (GPT-4 / GPT-4o), Anthropic Claude, Hugging Face models, LangChain, with Pinecone or ChromaDB for vector search and RAG. Backend: Python (FastAPI, Django), Node.js / Express, Laravel / PHP. Frontend: React, Next.js, Vue, TypeScript, Tailwind CSS. Data: PostgreSQL, MongoDB, Redis, Supabase, Firebase. Infrastructure: AWS (EC2, S3, Lambda), Vercel, Docker, GitHub Actions.

On "training the AI on your data"

For most use cases, the model is grounded in your data using RAG, embeddings and carefully tuned system prompts and few-shot examples — this gives brand-consistent, accurate answers without the cost and brittleness of full model fine-tuning. Where genuine fine-tuning is the right call for your data and budget, I'll say so and scope it explicitly rather than promise it by default.

Scope, timeline and revisions

A single, well-defined AI feature typically takes about a week; broader multi-feature or RAG-pipeline work runs longer and is scoped before we start. Revisions cover the agreed feature set until it works as specified. Ongoing API usage costs are billed by your AI provider directly — I optimise calls and tokens to keep them low and give you an estimate beforehand.

What's not included

Building a new SaaS product from scratch (that's a separate service), large-scale data labelling, and the monthly AI provider API fees after launch.

Background

I'm a full-stack developer (AWS Certified) who has shipped AI products end to end — including Tube2Blog.ai, Replix, RevSignal AI and BrandFlow AI. I handle both the AI integration and the surrounding web architecture, so the feature is reliable in production, not just a demo.