You will get I will build your AI product MVP end to end and ship it to real users
Project details
Most AI MVPs fail the same way: everything gets built except the thing that proves whether anyone wants it. Nine months later there's an admin dashboard, a permissions system, and no users.
So the first week is spent deciding what not to build. You get a written scoping document: architecture, the feature list split into ships-now and deferred, a realistic timeline, and the monthly running cost as well as the build cost. That week usually saves more money than any other part of the project.
Then I build the whole thing: backend, frontend, the AI layer, infrastructure, deployment. One senior engineer, so there's no coordination overhead and nothing falls between two people. Weekly demos throughout, you see working software, not a status report.
Behind that: 20 years of production software, systems code at Nokia, VP Engineering twice, founder. Most recently an AI shopping-agent MVP delivered in five months to six pilot clients.
Everything runs in your cloud from the first commit. Full source, infrastructure and documentation at handover.
Not sure the scope is right? Tell me the idea and I'll say honestly what I'd cut.
So the first week is spent deciding what not to build. You get a written scoping document: architecture, the feature list split into ships-now and deferred, a realistic timeline, and the monthly running cost as well as the build cost. That week usually saves more money than any other part of the project.
Then I build the whole thing: backend, frontend, the AI layer, infrastructure, deployment. One senior engineer, so there's no coordination overhead and nothing falls between two people. Weekly demos throughout, you see working software, not a status report.
Behind that: 20 years of production software, systems code at Nokia, VP Engineering twice, founder. Most recently an AI shopping-agent MVP delivered in five months to six pilot clients.
Everything runs in your cloud from the first commit. Full source, infrastructure and documentation at handover.
Not sure the scope is right? Tell me the idea and I'll say honestly what I'd cut.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI Mobile App Development, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Azure OpenAI, Gradio, Hugging Face, StreamlitAI Models
ChatGPT, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$1,000
|
Standard
$10,000
|
Advanced
$18,000
|
|---|---|---|---|
| Delivery Time | 10 days | 40 days | 70 days |
Number of Revisions | 2 | 3 | 4 |
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 | - |
Optional add-ons
You can add these on the next page.
Additional Revision
+$250
Extra feature (scoped at discovery)
+$1,500
30 days post-launch support
+$1,200Frequently asked questions
About Saravanan
AI Agent & RAG Engineer | LLM Apps in Production | 20 Yrs Software
Bangalore, India - 9:13 pm local time
20 years shipping software (Nokia, VP Engineering roles, founder). The last few years: LLM products, agent orchestration, and retrieval pipelines, hands-on in the code.
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WHAT I BUILD
▸ AI agents & automation
Multi-step agents with LangChain and CrewAI that call your real APIs, handle failure, and don't hallucinate their way through a workflow. Tool use, orchestration, guardrails, evals.
▸ RAG / chat over your own data
End-to-end retrieval pipelines: ingestion, chunking, embeddings, vector search (Weaviate, pgvector), reranking, and the evaluation harness that tells you when retrieval quality drops. Chat over documents, support knowledge bases, internal wikis.
▸ Full LLM product & MVP builds
You describe the product; I deliver the working thing. Backend, frontend, infra, deployment. Recent: an AI shopping-agent MVP delivered in 5 months and onboarded to 6 pilot clients. Currently building an AI marketing-intelligence platform for SMB merchants, retrieval pipeline, agent orchestration, and full stack, solo.
▸ Cost & privacy engineering
Local LLM inference with Ollama and Qwen3 when your data can't leave your infrastructure, or when API bills are eating the margin. Model selection and evaluation so you're not paying frontier prices for a task a small model handles.
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WHY A 20-YEAR ENGINEER FOR AI WORK
Most LLM projects don't fail at the prompt. They fail at everything around it: auth, rate limits, retries, queues, data modelling, cost control, deployment, the boring parts that decide whether your agent works on a Tuesday afternoon under load.
I've been doing the boring parts since 2005. Symbian systems code at Nokia. VP Engineering twice. Built a SaaS platform adopted by 25 enterprise clients. Founded a company. I use agentic coding workflows daily, which is why I ship at a pace that normally takes a team.
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STACK
LLM/AI: LangChain, CrewAI, RAG pipelines, Weaviate, Ollama, Qwen3, OpenAI / Anthropic / Vertex AI APIs, prompt design & evaluation
Backend: Python, FastAPI, Node.js, TypeScript, REST/API-first
Frontend: React, Angular, JavaScript, TypeScript
Data: PostgreSQL, MongoDB, Redis, MySQL
Cloud: AWS, GCP, Azure, Docker, Railway, Cloudflare R2, CI/CD
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HOW I WORK
- I tell you when your idea needs a database query instead of an LLM. Cheaper for you, and it's the answer that keeps clients.
- Written scope before code. You always know what "done" means.
- Based in India (IST) — I overlap US mornings and full EU hours. Daily written updates, no chasing.
- You own the code and the infrastructure. No lock-in to me.
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GOOD FIT IF
You need an AI feature or product built properly the first time, by one senior person rather than a coordination problem.
NOT A FIT IF
You want the cheapest hourly rate, or a wrapper around a single API call with no thought about what happens next.
Message me with what you're trying to build and I'll tell you honestly whether it's a two-week job or a two-month one, before you spend anything.
Steps for completing your project
After purchasing the project, send requirements so Saravanan can start the project.
Delivery time starts when Saravanan receives requirements from you.
Saravanan works on your project following the steps below.
Revisions may occur after the delivery date.
Scoping week and written architecture
A working session on what v1 must contain. Within five working days you get a scoping document: architecture, the feature list split into ships-now and deferred, a realistic timeline, and both the build cost and the monthly running cost.
Foundation: backend, data and deployment
Backend, database, auth and the deployment pipeline, in your own cloud account from the first commit. The unglamorous layer everything else sits on, built before anything user-facing so the last weeks are polish rather than panic.