Ionio

I run Ionio - We are a GPT4 & LLM, AI consulting company. We specialize in training, integrating & deploying LLMs (GPT4, ChatGPT, Alpaca) and other generative AI technologies into existing products and services - enabling up to 20x increase in efficiency and up to 90% operational automation.

Our end-to-end solution covers everything from ideation, development to seamless integration in 6-10 weeks. Value-based billing. 10x ROI Guarantee.

• We built Pumice.ai for Width.ai, a GPT4-powered E-Commerce taxonomy SaaS built with Python - React, Celery, Django, Redis, AWS & Docker in ~10 weeks.

• We helped ClearlyTrained with the strategy, research, ideation & product design of their in-house GPT4-powered eLearning Content solution in 4 weeks. GPT4, Python & NodeJS.

• We built Revvyco.com, an AI-powered revenue optimization platform in for a real-estate consultancy in Utah. Revvy runs on Django, React, Celery, Redis, AWS & Docker and was built in ~16 weeks with a team of 3.

• We built HypeMail.io, an AI-powered cold emailing SaaS built with Python - Flask, React, Celery, Redis, Docker, and AWS in ~12 weeks with a team of 5.

• We built FIT24TW Platform, a fitness startup based in Taiwan - 2 iOS/Android apps & 3 websites based on NodeJS, Flutter, Webflow & Docker in ~12 weeks per entity with a team of 4.

Send me a DM to brainstorm your AI strategy! 🛠

Founder & CEO

SmartCow

• Lead architectural, database, AI/DL and UI/UX design on various Full-stack Applications, built with Python and JavaScript
• Contribute to engineering efforts from planning and organization to execution and delivery to solve complex engineering problems.
• Manage individual projects priorities, deadlines, and deliverables
• Enforcing and maintaining code quality, and limiting technical debt through code reviews.
• Promote a culture of learning within the company, by mentoring, training colleagues, creating learning paths - modular projects which can be used to learn, teach, sharpen skills and then be integrated into production.

Senior Software Engineer

• Architect, develop and optimize the middleware, UI and storage microservices for edge systems.
• Design, build, test, and deploy software for internal and external use
• Ensure the continuous availability and throttling the downtime of core internal frameworks. 
• Design and implement scalable APIs and web services 
• Build dependable test cases and robust CI pipelines for multiple codebases

Software Engineer

Pseudtech Technologies Pvt.Ltd .

• Head research on the entire software side of several pilot-projects.
• Work with Python's leading web frameworks - Django and Flask with Heroku, AWS - S3, RDS and Google Cloud's App Engine to create software products to drive growth
• Work on Computer Vision and Deep Learning projects using OpenCV, Tensorflow, and Keras.
• Design several embedded Linux products including a (Facial Recognition Doorbell) using the Raspberry Pis and Python
• Work with AWS, Google App Engine, Angular and S3 to bootstrap a simple web application to monitor employee productivity
• Creating Facial Recognition POVs utilizing Microsoft Azure's Cognitive Services
• Contacting vendors and suppliers to acquire the resources needed for projects
• Work with Motor Drivers, Servos and Relays to create IOT devices which integrate seamlessly with the company's internal infrastructure. - IOT SwitchBoard
• Creating numerous software POCs and beta projects to meet the requirements.

• Understanding the workflow and commonly faced business problems
• Maintaining existing web apps and routine maintenance.
• Developing small tools and utilities for the upper management to help them automate office tasks.

Junior Software Engineer

Self-Employed

• Developed a Java and JavaFX Desktop App to manage a database of Fixed Deposits of Customers, added SMS integrations, encryption, and FTP Backups

• Created a corporate website for an investment firm using BootStrap, HTML, CSS, configured DNS, CPanel, and optimized it for SEO

• Created a desktop automation software using AutoIT that automated a BPO job of 3 Shifts of unskilled workers working 24x7, saving the client 33% in expenses and increasing efficiency by 50%

• Developed a Python Application to preprocess data received over the network and display it on the screen using PySide and PyQT.

Helping non-tech businesses build software products in 8-12 weeks | DM me your app ideas | Owner - Ionio |

Hi, I'm Rohan! I run Ionio. We are an AI consulting company specialized in LLMs training & deploying, building AI agents & Generative AI. We publish research on our site. Google "Ionio Research"

Our end-to-end solution covers everything from ideation, development to seamless integration in 6-10 weeks.

We are currently doing exactly this for our clients in the SaaS, eLearning, real-estate, fitness & lead generation space. 

Here's what they have to say :)

“We’ve had the opportunity of working with best full stack software engineering company in the biz.  Ionio makes it happen”.
- Greg Warner - Founder - Revvy 🏢

"Working with Ionio has been a game-changer for my business. The way they can take my vision and turn it into a reality better than I imagined is second to none".
- Thomas Manwaring - Founder - HypeMail 📧

“The Ionio team is just fire. Want something done fast? “Yep Yep”. Want something cool and innovative? “We can do that”. Nothing is beyond their reach....”.
- Brooke Daye - Founder - FIT24 🏋🏽‍♂️

Case Studies 👇🏽

• We built a Revvy, a revenue optimization platform in 4 months with a team of 3 engineers for a real-estate consultant in Utah. Property management companies pay a monthly recurring fee to use it. Saved +7.5 hours/week/client, automated 85% of the service

• We built the entire technical infrastructure for FIT24, a fitness startup based in Taiwan - 2 iOS/Android mobile apps & 3 websites in 5 months with a team of 4. Automated away +25 hours/week at the reception desk, saved +40 hours/week total

• We built HypeMail, an email outreach tool for small digital agencies with our client Thomas Manwaring. Took us 5 months & a team of 3. Agencies now pay a monthly recurring fee to use it. Automated away 7.5 hours/week of sales

Drop me a DM if you'd like to brainstorm your AI strategy. 🤖

Previously, I worked as a Software Engineer at SmartCow, an AI company. Before, that I worked in Python for a startup in Mumbai. Before before that for family friends, building websites & apps in Java. Been at this since I was 15 :)

“Chatbots for ecommerce don't work.”
There I said it…

Since 2023, we have been paid tens of thousands of dollars to build exactly those. 

We’ve built, launched, and analyzed dozens of ecommerce chatbots for small DTC brands, retail giants, and marketplaces. 

After reading through thousands of real customer conversations, one brutal truth keeps surfacing, now in almost-2026.

But before you cancel your chatbot project, let me add the nuance: they can work—but almost never in the way we originally imagined.

The flawed assumption we all made

Back in 2024/25. We thought users would land on a merchant's site and ask the AI, "What's the best running shoe for me?" or "Help me find a gift under $50." 

We assumed they'd treat it like an unbiased shopping assistant.

Here's why that falls apart:
Trust is dead on arrival. Users aren't naive. 

They know a brand's chatbot will only recommend that brand's products. The "unbiased advisor" pitch was never believable.

Quality control is a nightmare. Even if you wanted unbiased advice, how do you verify the bot's answers?

One hallucination about material safety or sizing, and you're facing a PR disaster.

The mobile experience is terrible. 70% of traffic is mobile. 

Nobody wants to type full sentences into a tiny corner widget on their phone. It's clunky, slow, and frustrating.

ChatGPT already won. 
Why would I use your chatbot when I already have the ChatGPT app? 

It knows my style, my budget, my quirks—I talk to it for hours. Your bot doesn't know me and never will.

So where do chatbots actually work?

Here's the pivot: Discovery within a brand.

When a customer is already on your site—because they love your aesthetic or trust your quality—they don't need selling. They need navigating.

Example: "Build me a complete work-from-home outfit using only your new collection." Or, "Show me everything in muted olive green."

This is where conversational AI shines. Not as a shopping advisor, but as a personal catalog curator.

The implementation fix nobody's talking about - 
Stop putting chatbots in the bottom-right corner.

That's not where discovery happens. Discovery happens in the search bar.

We're working on a POC that moves the conversation into search. 

Users type naturally, the AI responds with products inline, and it feels like upgrading search—not adding another layer.

The lesson? Don't fight ChatGPT. Don't try to replicate it. Instead, make your own discovery experience better.

Bottom line: The future isn't a chatbot that talks. It's a search bar that listens.

Agree? Disagree? Have you seen chatbots work differently? Let me know in the comments.

#chatbots #ecommerce #ai

Rohan Sawant

CEO & Founder | AI, LLM & NLP Consulting | Training, Deploying & Scaling GenAI Applications

We predicted this. "Shopping Research" in ChatGPT just launched —an deep research experience that asks clarifying questions, scours the web, and delivers personalized buyer guides in minutes.

We mapped this exact trajectory months ago.

Our white paper predicted how foundation model companies would evolve from assistants to autonomous shopping agents, and why this signals the end of the SaaS dashboard as we know it.

In 24 months, your most engaged customers will be the ones who never log in at all.

The three-layer architecture we outlined—Agent-to-Site → Agent-to-Agent → Broker-Agent-to-Site—is unfolding in real-time. 

OpenAI just made Layer One consumer-facing.

Layer Two (Merchant APIs) and Layer Three (Broker Agents) are next.

For retail & e-commerce SaaS platforms, this isn't a feature update. It's an existential pivot:

• GEO (Generative Engine Optimization) becomes table stakes
• GXO (Generative Experience Optimization) decides who wins the transaction
• Your moat isn't UI/UX anymore—it's how easily AI agents can negotiate with your backend

We've spent 5 years building agent-ready architectures for 40+ platforms. The 2-year window we predicted is now a 2-year deadline.

Is your platform is architected to survive this & the dozen other upcoming launches shaping how we shop?

Like + comment anything on this post and I'll DM you our full strategic briefing. Drop me a DM if you want to discuss what this means for your roadmap.

You're running an e-commerce brand with 1,000+ SKUs.

Every time you launch a new product, someone has to manually figure out what bundles well with it.

Your merchandising team is buried in spreadsheets. Your agency doesn't have direct access to product data. 

And the marketing team is waiting on three approvals just to update a "Frequently Bought Together" section. Sound familiar?

The problem with manual bundling at scale:
→ Hundreds of possible combinations per product 
→ Multiple teams, zero coordination 
→ Legacy systems that don't talk to each other 
→ Incomplete product data everywhere

We built a dynamic bundling system that sits on top of your existing setup. No massive overhaul needed.

When someone views a coffee maker, the system thinks: "They're trying to make coffee. What else do they need?" 

Then it suggests coffee beans, filters, grinder. Contextual, not random.

Someone buying gym shoes and a fitness tracker? Gets tagged as fitness enthusiast. 

Now protein bars and organic foods show up in their bundles.

The system runs in the background. Adapts to inventory changes, seasonal shifts, new SKUs. 

Your team focuses on growing the business instead of managing bundle spreadsheets.

We've deployed this for mid-market e-commerce brands dealing with exactly this operational nightmare.

If you're juggling thousands of SKUs and bundling feels impossible to scale, let's talk.

#ecommerce #productbundling #aitools #retail #skumanagement

"If ChatGPT can answer product questions better than your site can, why would anyone visit your site first?"

ChatGPT Shopping dropped last week. (Something we predicted, 1st white paper)

Now, we are writing our 2nd predictions essay, "The Aftermath" - where the industry goes next and the ripple effects on marketplaces, brands, and the SaaS companies that serve them.

Fellow men & women leaders in retail & ecom.  Have you considered the 

-  EFFECT of the impending shopping assistants roll-out?

- IMPLICATION of chatgpt ads for your business?

- CUSTOMER STRATEGY for what will be the most demanding, inquisitive buyers of the melliania -  customers who now expect instant, perfect answers just like they get from their "AI".

Drop me a message, comment, start a conversation what do you think, where is the space going? First impressions?

"Where do you see this space going in the next 12 months?"

I will be picking the best insights to feature in the final essay.

it has burnt a hole in my retinas yes.

taplio

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taplio

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