Three services.
Zero filler.
We focus on what early-stage startups actually need: fast mobile apps, solid web products, and AI features that ship.
React Native + LLM integrations, shipped fast.
AI-Powered Mobile Apps
We build cross-platform mobile apps in React Native with AI features baked in from the start. OpenAI, Anthropic, Gemini — we pick the right model for your use case and integrate it cleanly.
- iOS & Android from one codebase
- LLM-powered features (chat, summarization, recommendations)
- Expo or bare React Native — we work with both
- Real-time features with WebSockets
- App Store & Play Store submission handled
- Post-launch support included
Tech Stack
// AI-powered chat in React Native
const response = await openai.chat.completions.create({
model: "gpt-4o",
messages: [
{ role: "system", content: systemPrompt },
...conversationHistory,
],
stream: true,
})Next.js + Node.js — production-grade from day one.
Full-Stack Web Development
Need a web app, dashboard, or API? We build it with Next.js on the frontend and Node.js on the backend. Fast, scalable, and SEO-friendly. We don't do WordPress.
- Next.js App Router with server components
- REST or GraphQL APIs with Node.js
- PostgreSQL / Supabase / PlanetScale
- Auth with Clerk, NextAuth, or custom JWT
- Deployed to Vercel, Railway, or AWS
- Built-in analytics and error tracking
Tech Stack
// Type-safe API route in Next.js
export async function POST(req: Request) {
const { userId, prompt } = await req.json()
const result = await generateAIResponse(userId, prompt)
return Response.json({ result })
}Add AI to your existing product — no rewrite needed.
LLM Feature Integration
You already have a product. You want AI features. We add them without blowing up your codebase. Chat interfaces, semantic search, document Q&A, AI agents — we've built them all.
- RAG pipelines (retrieval-augmented generation)
- Semantic search with vector databases
- Document Q&A and summarization
- AI agents with tool use
- Streaming responses for snappy UX
- Prompt engineering and model optimization
Tech Stack
// RAG pipeline for document Q&A
const docs = await vectorStore.similaritySearch(query, 5)
const context = docs.map((d) => d.pageContent).join("\n")
const answer = await llm.invoke(
buildPrompt({ context, question: query })
)