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I created an AI Email Clone System that automatically reads new incoming emails and uses the Groq LLM model to generate intelligent responses. The AI-generated reply is then automatically saved in the drafts folder, making email handling faster and smarter.
#AI #EmailAutomation #Groq #LLM #MachineLearning #ArtificialIntelligence #Automation #Developer #Innovation
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Niva - AI Automation Agency Template (Contact page)
📌 What's Included
13+ fully designed pages, covering the entire buyer journey:
Home — hero, problem/solution framing, services, industries served, integrations, process, stats, case study previews, testimonials, pricing, FAQ, and final CTA
About — origin story, mission, values, team, and company stats
Services — a full breakdown of each service line with outcomes and use cases
Case Studies — a CMS-powered index plus a detailed single-case-study template (problem → approach → solution → results)
Blog — a CMS-powered index and article template, ready for SEO content
Contact — a conversion-focused form paired with direct calendar booking
404, and legal pages — Privacy Policy, Terms of Service, and Cookie Policy included
📌 Ideal For
AI automation agencies and consultancies
Workflow automation specialists and system integrators
Custom AI agent developers
AI strategy and operations consultants
Freelance automation experts positioning as a boutique agency
SaaS companies offering AI-powered services to B2B clients
Framer Website DesignFramer Website DevelopmentLow-Code/No-Code DevelopmentFramerFigmaAdobe Illustrator
Professional work
Here’s how the system actually works.
A stranger sees your offer.
They click.
They become a lead.
But getting the lead is only the beginning.
The system now needs to know:
Who are they?
Where did they come from?
What are they interested in?
What have they done so far?
Then things start to branch.
If they respond → someone follows up.
If they book → the conversation changes.
If they don't respond → they enter another follow-up.
If they show buying intent → sales gets involved.
If they buy → they leave the sales journey and enter another one.
And all of this can happen without someone manually checking every lead.
That's the part of automation I find interesting.
It's not really about sending emails or SMS.
It's about building a system that reacts to what people do.
Traffic comes in.
Data gets collected.
Behaviour creates signals.
Signals trigger decisions.
Decisions create actions.
That's the dynamic behind a well-built CRM and automation system.
Hey, I built the entire application end to end. The real challenge inside the CRM automation is the perfect stages tagging along each stage if & else coordinations.
I built the entire wireframe. I can share with you.
just shipped the core of Skillship — an AI-powered LMS built for schools.
One platform, many schools, and one rule that shaped every decision: School A must never see School B's data. Tenant-scoped models, filtered querysets at the base class, UUIDs everywhere.
Four AI features live:
— Career Pilot: personalised career paths for students
— Question generator: chapter PDF in, board-aligned MCQs out
— Adaptive quizzes: difficulty follows actual performance
— Semantic search: teachers ask in plain English, pgvector finds the material
Django + DRF for the LMS core, a separate FastAPI service for all AI work (Gemini + a pgvector RAG pipeline), Next.js 14 on the front. Django brokers every AI call — the AI layer never touches the browser.
The hard part wasn't the AI. It was making multi-tenancy boring enough that no future dev can leak a school's data by accident.
Open for new work — full-stack builds, AI integrations, RAG pipelines, multi-tenant SaaS. Send me a project.
The tenant boundary is where I'd put the first failure test. I'd try to make a user from one school retrieve another school's record before I trusted any of the AI features.