You will get an AI content and monitoring pipeline in n8n with LLM drafting
Top Rated
Top Rated
Project details
Watching what people say about your brand and replying to it by hand does not scale. You add a second brand and it doubles. You add a third and things start slipping.
I build a pipeline that does the watching for you. It scans conversations for every brand you care about, filters each hit against that brand's own rules, and sends only the ones worth answering to an LLM prompted in that brand's voice.
Every draft lands in Notion or Slack for a person to approve, edit or skip. Nothing posts on its own. That matters, because an AI posting unsupervised on your brand account is how you end up apologising.
I built this for a growth agency that went from a handful of brands to more than seven on one system with no extra headcount. Adding a brand became a config change. That case study is in my portfolio.
It is built to stay up. Failures raise a Slack alert in seconds, a watchdog catches a dead API key before it costs you a day, and every workflow is backed up nightly.
Tell me which brands and which sources and I will tell you what is realistic.
I build a pipeline that does the watching for you. It scans conversations for every brand you care about, filters each hit against that brand's own rules, and sends only the ones worth answering to an LLM prompted in that brand's voice.
Every draft lands in Notion or Slack for a person to approve, edit or skip. Nothing posts on its own. That matters, because an AI posting unsupervised on your brand account is how you end up apologising.
I built this for a growth agency that went from a handful of brands to more than seven on one system with no extra headcount. Adding a brand became a config change. That case study is in my portfolio.
It is built to stay up. Failures raise a Slack alert in seconds, a watchdog catches a dead API key before it costs you a day, and every workflow is backed up nightly.
Tell me which brands and which sources and I will tell you what is realistic.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Content Creation, Natural Language Generation, Natural Language Understanding, Sentiment AnalysisAI Development Language
PythonAI Tools
Azure OpenAIAI Models
ChatGPT, GPT-4What's included
| Service Tiers |
Starter
$300
|
Standard
$900
|
Advanced
$2,500
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 20 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 |
Frequently asked questions
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HA
Howard A.
Jul 7, 2026
Test engineer for end to end automated testing of entire SaaS Application (with some manual testing)
Indu has worked for us on a full-time basis for ~7 years and I have nothing but great things to say about her. She has been dedicated, hard-working and super diligent on all the automated testing we have thrown her way. She worked hand in hand with the development team and customer support team and our software was all the better for it. The team had a wonderful relationship with her; she is friendly, supportive, inquisitive, experienced, knowledgeable and what everyone would want in a contractor. Unfortunately, we have transferred our customer base to a third party company and we have had to terminate her contract (and all others) with us. I would not hesitate one second to re-contract with Indu should the opportunity arise again. I wish her all the best, and we will miss her:(
HA
Hadi A.
Apr 8, 2026
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Wolfgang B.
Feb 9, 2026
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Hadi A.
Aug 5, 2024
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PS
Phil S.
Apr 20, 2023
Automation Help
About Indumathy
AI Automation & AI App Developer | RAG, LLM Agents, Make/n8n, APIs
100%
Job Success
Chennai, India - 11:56 pm local time
Top Rated · 100% Job Success · 7,000+ hours · $100K+ earned.
Most of my clients already use CRMs, ERPs, spreadsheets, or support tools but still rely on people to move data, read documents, draft replies, and chase follow-ups. That's where I come in: I turn those manual, repetitive tasks into AI-powered systems that are accurate, monitored, and cost-controlled.
What I build
• AI applications - document extraction (OCR + GPT-4o/Claude), per-field confidence scoring, human-in-the-loop review
• RAG pipelines - vector search (pgvector / HNSW), embeddings, and knowledge bases grounded in your data
• AI agents & MCP tools - custom agents and copilots for ops, finance, and support teams
• Workflow automation - Make & n8n, multi-step, error-handled, and monitored
• Enterprise API integrations - ERP/CRM/finance systems (Sage Intacct, HubSpot, SharePoint), REST, webhooks, SQL
Recent work & results
• AI invoice-processing app - PDF → GPT-4o extraction + RAG over 50,000+ historical documents, auto-coding invoices to the right accounts with confidence scoring → eliminated hours of manual AP coding, with a full audit trail.
• AP payments → ERP suite - 30+ orchestrated Make scenarios syncing payments, bank feeds, and reconciliation into Sage Intacct + SQL across multiple entities → a near-hands-off finance back office.
• AI content & monitoring engine - n8n + LLMs drafting on-brand content and intelligence across 7+ brands, synced to Notion/Slack → hours of manual work turned into a reviewable AI pipeline.
• Custom AI agents & MCP tooling - agentic tools that connect LLMs to live data sources for non-technical teams.
Why teams keep rehiring me?
I run automations like software - Git-backed versions, error alerting, credential monitoring, and clear documentation for handover. I'll also tell you honestly what's worth automating and what isn't.
Tools I work with
AI/LLMs: OpenAI, Claude, LangChain, RAG, AI Agents, MCP
Automation: Make, n8n, Power Automate, Zapier
Data/Integration: REST APIs, Webhooks, PostgreSQL, pgvector, SQL, Python
Platforms: HubSpot, Airtable, Notion, Slack, Google Workspace, SharePoint/Office 365
Let's talk if you want automation that actually works, measurable business impact and someone who can own it end-to-end. Message me with what you're trying to automate. I'll give you a clear, honest recommendation.
Steps for completing your project
After purchasing the project, send requirements so Indumathy can start the project.
Delivery time starts when Indumathy receives requirements from you.
Indumathy works on your project following the steps below.
Revisions may occur after the delivery date.
Client purchases the project and sends requirements.