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 — a strategic AI consultancy for Retail & Ecom SaaS. We help mid-market platforms ($5M-100M ARR) build AI features that reduce churn, justify premium pricing, and turn their product into something merchants can't live without.

Over the past 5 years, we built 40+ products and helped founders go from zero to seven figures multiple times. Same playbook, over and over: take an idea, ship it fast, make it work, scale it.

After doing that tens of times, we wanted to take everything we learned and bring that energy to companies at the next stage. That's how we ended up focused on mid-market Retail & Ecom SaaS.

I'm genuinely passionate about this space. Generative AI lets us do something that was never possible before: personalize every single customer interaction. Not segments. Not cohorts. Actual one-on-one conversations at scale. Retail and ecom is where that has the biggest impact.

I've been working in AI since 2016 — back when that meant computer vision, not ChatGPT. Smart cameras, facial recognition, number plate recognition. That foundation matters when you're building production systems that actually need to work.

Previously: From 18 to 20, Sr. Software Engineer at SmartCow (Deep Tech AI company in Malta). At 17, Python work for a Mumbai startup, who are YET to pay me. At 15, building websites and apps for family friends in Java. It's been over a decade.

I started Ionio as a college junior, from my parent's bedroom, while hiring someone to do my assignments & a chauffeur to take me to college. Now it's a little less grunge than that.

In another life: rapper, musician, and cinematographer. Now I just build AI for retail... :(

What our clients say:

"Working with Ionio felt like having an extension of my own team — professionals who genuinely cared about delivering results, not just collecting a check."
— Stefan Georgi, Founder, 9-figure ecom founder

"Fastest I've ever launched a polished product. From wireframes to +1000 paying users in <8 weeks."
— Brad Milne, CTO, SupplierHQ

"Ionio has the character to navigate challenges and the technical depth to turn our 'AI-first' vision into reality."
— Sallyann Della Casa, CEO, Gleac

Read 35+ case studies on our site.

I post about: AI strategy for retail, technical deep dives, and behind-the-scenes of building Ionio.

If you're leading product, engineering, or strategy at a Retail & Ecom SaaS company, or a founder in any space — DM me, I always reply. If you're in Mumbai, I'll buy you a coffee.

Rohan Sawant

The $100K truth about enterprise SAAS that everyone forgets!

Last year, I helped a nine-figure retail brand evaluate data analytics platforms, they wanted to understand & visualize an frontend A/B test. 

Standard buying process—shortlist vendors, book demos, compare features. 

Here's what actually happened: I joined every call as their technical advisor. 

Not to hear the AE's pitch. To strip it down. 

To see what the product actually did versus what the marketing claimed it did. 

Got a list of 5-6 platforms from Chatgpt. Booked calls. 

Removed all their positioning, all their messaging, all their sales theater. Just laid the core products side by side. 

Made the decision based on capability, differentiation, ROI & cost. That's it. This is the actual buying journey for most of SaaS right now. 

In most sophisticated markets. 

And this experience crystallized something I've been seeing across dozens of conversations with SaaS companies over the past few weeks: 

All the marketing gimmicks, all the positioning frameworks, all the clever messaging—they work exactly once. 

They get you shortlisted in the discovery phase. But when an enterprise buyer is actually deploying a $100K budget? 

None of that matters anymore. 

What matters is: 
1. Does your product actually do what you claim? 🤔 
2. How differentiated is the core capability? ⚡
3. What's the measurable ROI? 💰

The mid-market knows this. Enterprise buyers definitely know this. 

Yet somehow, SaaS companies keep optimizing for discovery metrics instead of decision metrics. 

As we move into AI-native products, this gap is going to become even more obvious. 

Because AI products are easier to test, easier to compare, and harder to hide behind marketing. 

The companies that win won't be the ones with the best positioning decks. 

They'll be the ones that built something meaningfully better. 

For companies wondering how their products stack up in an AI-native future (esp. ones who just rolled their eyes at that) you should definitely drop me a DM, saying "AI"

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

Help businesses to generate more revenue by taking them online. Co-Founder, Full-stack Dev, Indie-hacker.

My tech journey started with a curious 19-year-old tinkering with code at hackathons. 

Fast forward a few years, and that curiosity has turned into CubikTech, a company born from a simple idea: technology should make business easier, not harder.

We've seen firsthand how the right digital tools can transform a struggling business into a thriving one. That's what gets us out of bed in the morning - the chance to be part of someone else's success story.

At CubikTech, we're not about flashy tech for the sake of it. We're about building software, and digital strategies that bring real results - more customers, smoother operations, and yes, more revenue.

We've worked with startups and established businesses alike. Each project teaches us something new, and we bring all of that experience to the table for you.

Looking to grow your business but not sure how technology fits into that? Let's chat. 

We're always up for a good conversation about turning business challenges into digital opportunities.

Drop me a line at nilesh@cubiktech.com

Who knows? Your business might be our next favourite success story.

Nilesh Aggarwal

Traditional data handling isn't slow - your decision-making framework is.

Most teams blame their data systems for delays. But the real bottleneck? Static frameworks that can't adapt to evolving patterns.

Recursive Language Models (RLM) shift this entirely. Instead of waiting for manual analysis, RLM processes data dynamically - identifying patterns, surfacing insights, and enabling real-time decisions.

The difference:
→ Traditional systems store and retrieve
→ RLM analyzes, adapts, and informs

Companies that moved to AI-driven data management saw this shift firsthand. Real-time pattern recognition replaced static reports. Decision cycles compressed. Operational efficiency improved - not because data moved faster, but because the framework became responsive.

The lesson: efficient data management isn't about speed. It's about building systems that learn, adapt, and support better decisions as your data evolves.

If your team is still waiting on reports to act, you're not solving a data problem. You're solving a decision architecture problem.

Helping D2C teams build AI team to handle Ops | Co-founder at Cubiktech

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