You will get a custom MCP server connecting Claude to your API, database, or tools
Project details
Most teams already have an AI assistant. What they don't have is a way for it to touch their own systems - the API, the database, the internal tool where the real work happens. An MCP server closes that gap.
I build custom MCP servers that give your assistant real, typed access to your systems: look up a record, create a ticket, run a query, search your documents. Not a chatbot bolted on the side - tools it can call directly and reliably.
You get production code, not a demo. Typed input schemas so bad arguments fail cleanly. Secrets kept out of source. Retries and structured errors so the assistant recovers instead of dead-ending. Documentation your team can follow, and a verified install before I call it done.
I'm a frontend architect who has spent the last stretch deep in agentic AI and the Model Context Protocol, so I care about the boring parts - auth, error states, what happens on the third retry. Those are what break in week two.
Tell me the system you want connected and I'll tell you honestly whether MCP is the right tool for it.
I build custom MCP servers that give your assistant real, typed access to your systems: look up a record, create a ticket, run a query, search your documents. Not a chatbot bolted on the side - tools it can call directly and reliably.
You get production code, not a demo. Typed input schemas so bad arguments fail cleanly. Secrets kept out of source. Retries and structured errors so the assistant recovers instead of dead-ending. Documentation your team can follow, and a verified install before I call it done.
I'm a frontend architect who has spent the last stretch deep in agentic AI and the Model Context Protocol, so I care about the boring parts - auth, error states, what happens on the third retry. Those are what break in week two.
Tell me the system you want connected and I'll tell you honestly whether MCP is the right tool for it.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI-Generated Code, AIOps, Conversational AI, Natural Language UnderstandingAI Development Language
PythonAI Models
ChatGPT, GPT-4What's included
| Service Tiers |
Starter
$79
|
Standard
$299
|
Advanced
$749
|
|---|---|---|---|
| Delivery Time | 2 days | 5 days | 10 days |
Number of Revisions | 2 | 3 | 5 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | |||
Detailed Code Comments | |||
Image Upscaling | - | - | - |
MLOps | - | - | |
Model Deployment | - | ||
Model Documentation | |||
Model Monitoring | - | - | |
Model Testing & Optimization | - | - | |
Model Tuning | - | - | - |
Natural Language Processing | - | - | - |
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | |||
Source Code |
Frequently asked questions
About Aayush
Frontend Architect | React & Next.js Expert | Agentic AI & MCP
Ghaziabad, India - 8:04 am local time
I set technical direction for large React and TypeScript platforms: multi-brand, multi-locale, 1M+ users, regulated domains. Recently I've been building the layer where that expertise meets agentic AI — MCP servers, tool-calling interfaces, and LLM-powered product surfaces that hold up in production.
AI SOLUTIONS
• MCP servers — I built one exposing a production design system to Claude Code and Cursor, so AI assistants generate on-brand, spec-compliant components instead of hallucinating markup
• Agentic workflows — tool definitions, multi-step agent orchestration, human-in-the-loop review surfaces
• LLM application engineering — streaming chat UIs, RAG-backed search, OpenAI/Anthropic API integration, prompt architecture
• AI-native developer tooling — making your codebase, design system and internal APIs legible to coding agents
FRONTEND ARCHITECTURE
• NX monorepo platforms — architected one serving 5 brands across 12 locales: CSRF protection, GraphQL proxy for server-side credentials, React Query SSR with HydrationBoundary
• Next.js 15 / React 19 — App Router, SSR and hydration strategy, end-to-end auth domains, i18n
• Design systems — built two from scratch, both adopted as the shared vocabulary across feature teams
• Micro-frontends — including a bidirectional Vue-to-Three.js interoperability layer for a real-time 3D platform
• CI/CD and build architecture — unified core and per-partner repos into one Vite pipeline; a partner deploy now triggers the core pipeline automatically
• WCAG 2.1 AA with zero audit violations across an enterprise platform; TDD with Jest and RTL
WHERE I'VE DONE IT
Goodyear US retail e-commerce via Publicis Sapient — led a delivery pod within weeks of joining. SigFig digital wealth management — 1M+ end users across banking partners. Foyr Neo — browser-based 3D design platform used by 100K+ designers in 30+ countries.
STACK
TypeScript, React 19, Next.js 15, Vue/Nuxt, Zustand, Redux, React Query, GraphQL, NX, Vite, Node, Tailwind, Three.js, OpenAI and Anthropic APIs, Model Context Protocol.
I work best when you bring me the problem rather than the ticket. I ship in one- to two-week increments with something demoable at each step, and I'll tell you straight if I'm not the right fit.
Steps for completing your project
After purchasing the project, send requirements so Aayush can start the project.
Delivery time starts when Aayush receives requirements from you.
Aayush works on your project following the steps below.
Revisions may occur after the delivery date.
Scope and tool design
We agree exactly which tools the server exposes, what each one takes as input, and what it returns. You approve the list before any code is written.
Build and connect
I build the server, wire it to your system, and handle the parts that usually break: authentication, pagination, rate limits, and clean error responses.