You will get your vibe-coded MVP scaled to handle millions of requests
Project details
We audit end to end: application code, database schema and query paths, AI/LLM pipeline design, token economics, API layer, infrastructure and deployment topology. Then we rebuild the bottlenecks so your system serves thousands of concurrent users and scales from thousands to millions of report generations.
Deliverables: a severity-ranked architecture audit and scaling roadmap. In Standard and Premium, the implemented work — async job queues, autoscaling, caching and CDN, connection pooling, model routing and cost reduction, load testing to your target concurrency, monitoring, alerting, CI/CD.
Built by PGAGI, an AI engineering studio with systems in production today.
Tell us your stack and target load. We'll tell you what breaks first.
Deliverables: a severity-ranked architecture audit and scaling roadmap. In Standard and Premium, the implemented work — async job queues, autoscaling, caching and CDN, connection pooling, model routing and cost reduction, load testing to your target concurrency, monitoring, alerting, CI/CD.
Built by PGAGI, an AI engineering studio with systems in production today.
Tell us your stack and target load. We'll tell you what breaks first.
Programming Languages
Python, Java, GoCoding Expertise
Performance Optimization, Security, DesignWhat's included
| Service Tiers |
Starter
$1,000
|
Standard
$2,400
|
Advanced
$6,500
|
|---|---|---|---|
| Delivery Time | 7 days | 24 days | 45 days |
Number of Revisions | 0 | 2 | 2 |
Design Customization | - | - | |
Content Upload | - | ||
Responsive Design | - | ||
Source Code |
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Ali D.
May 20, 2025
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Very talented, smart, and dedicated ml engineer. he became part of our ML development team. We are very happing to have him in our team.
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Alina S.
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Gagat B.
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excellent result, talented, beyond what I expected, will contract him again
About Neelambar
Full Stack AI Developer | AI Automation | AI Integration | AI Engineer
100%
Job Success
Pune, India - 11:17 pm local time
I have noticed that most AI projects don't fail after the MVP stage. They fail when a user hits a long-running workflow, and the system freezes, or 100 other things that are not expected. I build the infrastructure that prevents such failures when moving from an AI demo to a system that handles real users, real data, and real consequences.
Right now, asking a coding agent to build an application is a commodity any beginner can experiment with. The engineers worth hiring are the ones who keep an agent running under real load, recover it when it crashes, and know exactly what it was doing when it did. The difference is exactly that between a sailing ship and a nuclear submarine.
Over the last few years, my team and I have built 80+ AI systems across industries. And these are real systems up and running right now with real users, real revenue, and a real business impact.
We specialize in building systems that are:
- Enterprise-grade
- Reliable while maintaining harness with non-deterministic LLM
- Scalable, handling unexpected load
- Secure, with complex vulnerabilities taken care of in-house
- Integrated into real businesses solving real problems
- Built for ROI
What I Build:
- AI SaaS MVPs and Production builds: Next.js/React.js + AI Agents + CRMs/Tools + Stripe
- Multi-Agent Systems: LangGraph + LLM pipelines, 80+ products in production
- Enterprise RAG: Context-aware engines across Notion, Slack, SQL & Drive (10K+ docs)
- AI Voice Agents: Livekit + Vapi + ElevenLabs for appointment booking, sales, support
- MCP Servers: Custom servers exposing your data to your agents
- n8n + LLM Workflows: End-to-end automations with CRM, email, voice, WhatsApp
- Multi-Channel Ops: Telegram, Shopify + Square + Wholesale unified in Google Sheets / QuickBooks
Specialized in:
- Claude Ecosystem: Claude Code, Cowork, MCP, Skills, Plugins, Agent SDK, OpenClaw (self-hosted)
- Voice AI Agents: Livekit, Retell, Vapi, Bland, Synthflow, ElevenLabs, Deepgram, Whisper, Custom pipeline
- AI Systems: RAG, GraphRAG, SQL Agents, Multi-Agent Systems, Computer Use, Browser Automation
- Frameworks: LangChain, LangGraph, CrewAI, Pydantic AI, OpenAI SDK, Claude Code, Deep Agents, Livekit
Technical Stack:
- AI & LLMs: Anthropic Claude, OpenAI, Gemini, DeepSeek, Llama / Ollama (local)
- Voice & Real-Time — LiveKit, WebRTC, SIP, RTP, Deepgram, ElevenLabs, OpenAI Realtime API.
- Orchestration: LangChain, LangGraph, CrewAI, Pydantic AI, MCP, Temporal, Langfuse
- Vector & Data: Qdrant, Pinecone, ChromaDB, Supabase, PostgreSQL, pgvector
- Backend: Python, FastAPI, Node.js, TypeScript, Docker, Kubernetes, AWS, Redis, Apache Kafka.
- Frontend: Next.js, React, Tailwind, Vercel AI SDK
- Automation: n8n, Claude Code, Webhooks, REST APIs, OpenClaw
What you can expect:
Systems-first delivery. Not scripts that work once in isolation. Services with logging, error handling, and observability built in from the start, because retrofitting these into a broken production system costs three times what it would have taken to include them.
Technical honesty. If your use case doesn't need a large language model, I'll tell you. If a specific model is overkill for your budget and use case, I'll say so. I have lost projects being upfront about this. The ones I keep tend to run long.
Clean handoff. Containerized, fully documented codebases built for your team to maintain, extend, and understand without me in the room.
Steps for completing your project
After purchasing the project, send requirements so Neelambar can start the project.
Delivery time starts when Neelambar receives requirements from you.
Neelambar works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & access setup
We review your repo, infra, and current load metrics. Kickoff call to align on target concurrency, request volume, latency SLOs, and budget ceiling for infra and model spend
Full architecture & code audit
Line-level code review, database and query profiling, AI pipeline and prompt analysis, infra topology review. Every bottleneck documented with severity, blast radius, and fix effort