You will get an AI automation that alerts you the moment it breaks | n8n, Make
Project details
I found and disclosed security bugs in the AI frameworks other people build these automations on top of. That is the short version of why I build the ones that keep working.
Your team is copy-pasting data between tools and answering the same questions all day, and the automations you have tried either never got built or broke where nobody could see. I come from backend engineering on a payments platform moving tens of millions a day, so every automation ships with retries, idempotency, dedupe, and failure alerts. I know how these systems fail, and I build around it.
What you get: fewer manual hours, faster lead response, and an alert the moment something breaks, so you hear it from the system and not from a lost customer. You own all code and accounts, with no lock-in.
Tools I work with: n8n, Make, OpenAI, LangChain, Supabase, webhooks, HubSpot, Stripe, Slack, and Twilio.
Your team is copy-pasting data between tools and answering the same questions all day, and the automations you have tried either never got built or broke where nobody could see. I come from backend engineering on a payments platform moving tens of millions a day, so every automation ships with retries, idempotency, dedupe, and failure alerts. I know how these systems fail, and I build around it.
What you get: fewer manual hours, faster lead response, and an alert the moment something breaks, so you hear it from the system and not from a lost customer. You own all code and accounts, with no lock-in.
Tools I work with: n8n, Make, OpenAI, LangChain, Supabase, webhooks, HubSpot, Stripe, Slack, and Twilio.
AI Algorithms
Large Language ModelAI Applications
AI Chatbot, Conversational AIAI Development Language
PythonAI Models
ChatGPT, GPT-4What's included
| Service Tiers |
Starter
$300
|
Standard
$550
|
Advanced
$1,500
|
|---|---|---|---|
| Delivery Time | 4 days | 10 days | 21 days |
Number of Revisions | 2 | 4 | 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 Aditya
AI & LLM Integration Engineer | Python, Go, Java | Secure APIs
Bengaluru, India - 5:33 am local time
My open source record is in AI infrastructure you may already use: merged fixes in Ollama (streamed tool-call parsing), KubeRay (CNCF) and Keep (YC W23, 4 merged PRs), with more under review in LiteLLM, LlamaIndex and mem0. All of it is public on GitHub under the username aditya-786. I have also done contract work for Mercor building SWE-bench style agent evaluation tasks: Dockerized environments, test suites and grading rubrics.
Day job: senior backend engineer at a fintech, where I built a payment platform processing 40M+ INR in daily transaction value, a fraud detection network over 10M+ data points, and the backend for an e-commerce store serving 1M+ users.
What I can do for you:
- Add LLM features to your product: OpenAI and Anthropic APIs, tool calling and agents, MCP servers, RAG with LlamaIndex, local inference with Ollama, and evals so you know it actually works.
- Build backend features, REST APIs and services in Python (FastAPI, Flask), Go and Java, with tests.
- Review your code for real security issues: broken access control, IDOR, SSRF, auth flaws. Recent responsibly disclosed findings include a full account takeover chain (CVSS 8.1) and an XXE in an ML model loader, reported through huntr and HackerOne.
I keep communication tight, deliver on scope, and hand back work that is tested and documented.
Steps for completing your project
After purchasing the project, send requirements so Aditya can start the project.
Delivery time starts when Aditya receives requirements from you.
Aditya works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery and fixed-scope quote
A short call to map where your process leaks time or breaks, then a fixed scope so you know the price before I build.
Build, test, and add the safeguards
I build the automation, then test it end to end with error handling, retries, idempotency on writes, and a failure alert. I run a deliberate double-fire and a forced failure so the safeguards are proven, not assumed.