You will get a custom AI agent to automate your business workflow
Project details
Most AI agent projects don't fail at the prompt. They fail at everything around it, the API that times out, the model that returns nonsense on one weird input, the step nobody decided who owns. That's where I spend the engineering.
Before any code, you get a written design: every step mapped, which use a model, which run as plain code, where a human approves, and what the agent will never do. Usually only two or three steps genuinely need an LLM. Putting one on the rest adds cost and unpredictability for nothing.
The agent runs against your real systems: CRM, email, database, internal APIs with auth, rate limits and retries handled properly. Failure behaviour is defined per integration, not left to chance. Every run keeps a full trace, so you can see why it did what it did.
Behind that: 20 years of production software: systems code at Nokia, VP Engineering twice, founder. Recently an AI shopping-agent MVP delivered in five months to six pilot clients.
Deployed on your infrastructure, under your keys, full source at handover.
Not sure what's automatable? Describe the workflow and I'll tell you honestly, including if part of it shouldn't be an agent.
Before any code, you get a written design: every step mapped, which use a model, which run as plain code, where a human approves, and what the agent will never do. Usually only two or three steps genuinely need an LLM. Putting one on the rest adds cost and unpredictability for nothing.
The agent runs against your real systems: CRM, email, database, internal APIs with auth, rate limits and retries handled properly. Failure behaviour is defined per integration, not left to chance. Every run keeps a full trace, so you can see why it did what it did.
Behind that: 20 years of production software: systems code at Nokia, VP Engineering twice, founder. Recently an AI shopping-agent MVP delivered in five months to six pilot clients.
Deployed on your infrastructure, under your keys, full source at handover.
Not sure what's automatable? Describe the workflow and I'll tell you honestly, including if part of it shouldn't be an agent.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI-Enhanced Classification, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Azure OpenAI, Gradio, Hugging FaceAI Models
ChatGPT, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$1,500
|
Standard
$3,000
|
Advanced
$7,000
|
|---|---|---|---|
| Delivery Time | 16 days | 28 days | 45 days |
Number of Revisions | 1 | 2 | 3 |
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
+$150
Additional Integration
(+ 2 Days)
+$350
Additional Workflow
(+ 4 Days)
+$800Frequently asked questions
About Saravanan
AI Agent & RAG Engineer | LLM Apps in Production | 20 Yrs Software
Bangalore, India - 9:13 am 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.
Workflow mapping and written design
A 45-minute call to walk through the process as it happens today. Within three working days you get a written design: every step, which ones use a model, which run as plain code, where the approval gates sit, and what is explicitly out of scope.
Integrations and access
I wire up the systems the workflow touches: CRM, email, database, internal APIs, Slack. Auth, token refresh, rate limits and retries handled properly. This is where most agent projects quietly break, so it gets its own stage.