Your Partner for Revenue-Generating Retail AI Engines.
We help Retail & Ecommerce SaaS (10M-125M ARR) platforms build the "Pillar Features" that reduce churn, justify premium tiers, and build an unshakeable competitive moat — through AI strategy & implementation
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The 3 Systemic Failures Affecting AI Roadmap in Retail SaaS
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#1: Building a "Copilot" When You Need…?
Your competitors are adding generic chatbots that answer questions. We build engines that take action.
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#2: Solving for "Cool Tech" Instead of Churn
That generative AI feature your team is excited about? If it doesn't directly impact your customer's LTV or your platform's retention, it's a distraction.
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#3: Falling for the "Build vs. Buy" Trap
You think your only options are a slow, expensive in-house team ($1.5M/year gamble) or a generic dev shop that builds blindly - both too slow.
Most of our clients also knew AI is critical but not knowing where to begin.
Conversational Engine: Turning Legacy Platforms into AI-Native Competitors
We architected a "Conversational Commerce" layer for a legacy shopping platform with 2M+ MAU, bypassing the need for a risky full-platform rewrite.
The challenge was a stagnating UX that was bleeding users to newer, faster competitors. We deployed an AI engine that navigates the platform's 150+ API routes and 100k+ SKUs to handle end-to-end shopping journeys in under a minute.
The SaaS Outcomes
- Defended the Moat: Stopped user migration to AI-native competitors by modernizing the UX without touching the core 9-year-old legacy codebase.
- Stickiness: Reduced search time by 80%, directly improving session retention metrics.
- Enterprise Compliance: Shipped under strict SOC2/GDPR constraints, allowing the client to retain enterprise contracts.
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Pumice: Transforming Catalog Management into a Premium Revenue Driver
Together with Width.ai, we built Pumice, a custom-LLM engine designed for PIM (Product Information Management) and Marketplace platforms.
Most PIMs are just dumb databases. We built a value-add engine that combines text and image embeddings (CLIP) to auto-classify products, detect duplicates, and generate SEO attributes at scale (~2M runs per day).
The SaaS Outcomes
- New Revenue Stream: Enabled the platform to launch an "Automated Enrichment" premium tier (upsell).
- Operational Moat: Replaced the merchant's need for offshore VAs, reallocating that budget directly to the SaaS subscription.
- 10x+ ROI on Development: The feature allows the platform to charge for compute-heavy tasks that were previously manual.
MicroSegments: Enabling a Premium Personalization Tier for SaaS
MicroSegments is a 4-layer reasoning engine we developed that allows SaaS platforms to offer "Agency-Level" strategy as a software feature.
Instead of giving merchants raw data dumps, this engine ingests transactional data (Shopify/Klaviyo) and uses an LLM reasoning layer to identify high-value micro-tribes (e.g., "Ingredient Researchers"). It then autonomously drafts the offers, bundles, and creative angles required to target them.
The SaaS Outcomes
- Justifies Premium Pricing: Allows the SaaS to sell a high-ticket "AI Personalization" module that directly competes with expensive marketing agencies.
- Increases Merchant LTV: By uncovering segments with 10–40% higher LTV, the SaaS becomes the primary driver of its customers' revenue.
- Differentiation: Moves the platform from a "reporting tool" to a "revenue-generating partner."
SupplierHQ: Monetizing Audience with a Recurring SaaS Asset
For a high-ticket ecommerce education company, we executed a rapid "SaaS-ification" of their proprietary data. We designed and deployed SupplierHQ, a platform that gives store owners instant cached access to vetted suppliers.
We didn't just build a database; we built a recurring revenue engine. We shipped the MVP in 8 weeks, scaled it to thousands of paying users, and successfully handed the codebase to an in-house team.
The SaaS Outcome
- Instant ROI: The client recovered the development cost in under 30 days via subscription revenue.
- Asset Creation: Turned a static list of data into a sellable SaaS asset with recurring MRR.
- Competitive Wedge: Created a proprietary tool that anchored students to their ecosystem, significantly reducing course refund rates.
Retention AI: Building the Churn Prevention Module SaaS Platforms Need
We developed an exploratory data analysis and churn modeling system for a global retail brand that now serves as the blueprint for "Retention AI" modules in subscription SaaS.
Our models identify high-risk customers 30–90 days before they churn and map them to specific behavioral interventions. We moved beyond simple "at-risk" flagging to prescriptive retention strategies—telling the merchant exactly which offer (fee waiver, education flow) will save the customer.
The SaaS Outcome
- Indispensability: Embeds the SaaS platform deeply into the merchant's financial health by directly saving them revenue.
- Prescriptive vs. Descriptive: Shifts the platform from showing churn charts (depressing) to preventing churn (valuable).
- High-Value Upsell: This functionality is the primary driver for merchants upgrading to "Pro" or "Enterprise" plans on subscription platforms.
Dispute Dine — Automating Operations to Unlock a New SaaS Revenue Stream
Dispute Dine automates the complex chargeback disputes for restaurants on platforms like Uber Eats and DoorDash. We transformed a labor-intensive consulting service into a fully automated SaaS.
We built an engine that ingests order data, applies template-driven dispute logic, generates evidence, and queues filings via background workers—removing the human operator entirely.
The SaaS Outcome
- Scalable Revenues: Enabled the client to switch from "hourly consulting billing" to "high-margin recurring software subscriptions."
- 100% Automation: The system handles the full loop, allowing the founders to scale from tens to hundreds of clients without increasing headcount.
- Net-New Reality: Created a brand new category of software for the client, opening a revenue stream that previously didn't exist.
We Only Solve Four Problems. Exceptionally Well.
Hyper-Personalization & Marketing
Problem
What We Build
Result
Merchandising & Catalog Management
Problem
What We Build
Result
Subscription & Recurring Revenue
Problem
What We Build
Result
Attribution & AdTech (coming soon)
Problem
What We Build
Result
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AI transformations delivering 10–20x efficiency and 5–10x ROI across 35+ platforms.
"Ionio...they clicked right away... Rohan was able to execute perfectly."
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"Ionio was very professional and provided excellent support throughout the entire process."
"Ionio came in and documented everything and then shared that over and then after that we started iterating on the code base."
"Ionio didn't disappear after the MVP was built. They continued to work with us to fix bugs and make improvements based on user feedback."
"Communication is free-flowing and Veerview can almost always get a hold of someone on Ionio’s team throughout the day."
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"Even though we are from the US and working with a company overseas, they made the experience very personable."
"Ionio...has the experience in the industry to take a generalized concept and turn it into something very specific."
"The communication was Flawless, like I've never had such a communication with the dev team."
"The MVP of the software was ready within eight weeks and the people who use it absolutely love it."
"Communication was transparent throughout the project, including pricing, process, and timeline."
"Rohan understands both technical and non-technical aspects, which was very important for me as I don't have a technical background."
"Ionio's team was understanding about this and did not get discouraged when new bugs were found."
"Rohan was exceptionally patient and helpful, he was able to explain complex technical concepts in simple way"
Most Mid-Market SaaS Platforms Face an Impossible Choice.
You know AI is the key to differentiation, but the path forward is unclear.
A $2M strategy deck with zero code shipped. They identify the problem, then leave execution up to you.
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They build exactly what you spec, lacking the market expertise to challenge assumptions or build what your customers truly need.
12-18 months to hire a team, another year to ship, and a 73% chance the project fails to deliver meaningful ROI.
Partner with Ionio.
We’ve spent the last 4 years building revenue-generating AI for retail & e-commerce SaaS. We are not generalists. We are a specialized force that embeds with your team to deliver a strategic AI moat in 90 days.
Former VPs of Product at $100M+ retail tech platforms
Retention strategists from Klaviyo, Recharge & competitors
PIM architects who’ve managed 500K+ SKU catalogs
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The Economics of Transformation
The $1.5M+ In-House Gamble
The Ionio Strategic Partnership
Team Cost:
$1.2M+ / year (3 ML Engineers, 2 Data Engineers, 1 PM)
World-class AI strategists & engineers, embedded with you
Timeline:
12-18 months to build, if you can hire the team.
90 days from audit to production deployment.
Risk:
High. 70% of in-house AI projects fail to deliver ROI.
We guarantee deployment & success fees based on actual ROI.
Total First-Year Cost:
$1.5M - $2M+
AI Strategic Audit:
Guesswork & going off "vibes"
$15-25K (~2 weeks)
Build & Deploy:
$100K-150K (10 weeks)
Total Cost
$1.5M - $2M+
Total: $100-200K
$100K-150K (10 weeks)
Ref: Client case studies with 10x-20x ROI in E.V
See Exactly What We Build, How It Performs, and What You Do Next
What the Blueprint Produces: Your Next Revenue Engine.
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The Prescriptive Commerce Engine
Autonomous Content Automation
The Omnichannel Attribution Engine
From Blueprint to Bottom Line: A Case Study.
Ready to Build Your Moat?
This isn't a sales call. It's a 45-minute strategic session with our founder to deconstruct your market and identify one potential "wedge" opportunity. You will walk away with a tangible, valuable insight, whether we work together or not.
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Research
Business Applications of Video Chat with LLMs
Have you ever wondered what it would be like to chat with language models as naturally as you video chat with friends? Imagine sharing every random thought or question just as it pops into your mind
How to Create an AI Agent to Manage Your Email Inbox and Reply to...
This is part 2 of creating an AI agent to manage and reply to your cold emails blog where we saw how to create an AI agent using Langchain which can classify and reply to your cold emails in your tone and style.
A Comprehensive Guide About Langgraph: Code Included
In this blog, we will explore how Langgraph can help us to automate complex and large workflows using its unique decision making and easy to understand architecture.
Demonstrating Virtual Clothing Try-on(VTON) using Hugging Face
With Virtual Try-On(VTON) technology, your business can help customers feel sure about their choices and enjoy shopping in a whole new way. Are you ready to see how this simple yet powerful tool can help your business.
What are Large Action Models (LAM) and How They Work?
In this article, we will discuss a new trend in the generative AI field that is large action models that can not only give instruction on how to perform any task but can take action on user's behalf.
LLMs in production with guardrails
For LLMs, guardrails are crucial safety measures that guide our models to avoid unintended harm. Implementing these guardrails not only prevents errors and ensures compliance with regulations, but also boosts customer trust and your company's reputation...
A Comprehensive Guide on Merging Language Models
Combining LLMs with techniques like SLERP, TIES, DARE, and MoE boosts capabilities without excessive computational burden. Uploading merged models to the Hugging Face Hub demonstrates the efficiency of this approach.
Fastest Token First: Benchmarking OpenLLMs by inference speed
Latency, especially in the context of Large Language Models LLMs), plays a crucial role in determining their practical utility, especially in real-time applications where responsiveness is paramount.
Business Applications of Video Chat with LLMs
Have you ever wondered what it would be like to chat with language models as naturally as you video chat with friends? Imagine sharing every random thought or question just as it pops into your mind
How to Create an AI Agent to Manage Your Email Inbox and Reply to...
This is part 2 of creating an AI agent to manage and reply to your cold emails blog where we saw how to create an AI agent using Langchain which can classify and reply to your cold emails in your tone and style.
A Comprehensive Guide About Langgraph: Code Included
In this blog, we will explore how Langgraph can help us to automate complex and large workflows using its unique decision making and easy to understand architecture.
Demonstrating Virtual Clothing Try-on(VTON) using Hugging Face
With Virtual Try-On(VTON) technology, your business can help customers feel sure about their choices and enjoy shopping in a whole new way. Are you ready to see how this simple yet powerful tool can help your business.
What are Large Action Models (LAM) and How They Work?
In this article, we will discuss a new trend in the generative AI field that is large action models that can not only give instruction on how to perform any task but can take action on user's behalf.
LLMs in production with guardrails
For LLMs, guardrails are crucial safety measures that guide our models to avoid unintended harm. Implementing these guardrails not only prevents errors and ensures compliance with regulations, but also boosts customer trust and your company's reputation...
Business Applications of Video Chat with LLMs
Have you ever wondered what it would be like to chat with language models as naturally as you video chat with friends? Imagine sharing every random thought or question just as it pops into your mind
How to Create an AI Agent to Manage Your Email Inbox and Reply to...
This is part 2 of creating an AI agent to manage and reply to your cold emails blog where we saw how to create an AI agent using Langchain which can classify and reply to your cold emails in your tone and style.
A Comprehensive Guide About Langgraph: Code Included
In this blog, we will explore how Langgraph can help us to automate complex and large workflows using its unique decision making and easy to understand architecture.
Demonstrating Virtual Clothing Try-on(VTON) using Hugging Face
With Virtual Try-On(VTON) technology, your business can help customers feel sure about their choices and enjoy shopping in a whole new way. Are you ready to see how this simple yet powerful tool can help your business.
What are Large Action Models (LAM) and How They Work?
In this article, we will discuss a new trend in the generative AI field that is large action models that can not only give instruction on how to perform any task but can take action on user's behalf.
LLMs in production with guardrails
For LLMs, guardrails are crucial safety measures that guide our models to avoid unintended harm. Implementing these guardrails not only prevents errors and ensures compliance with regulations, but also boosts customer trust and your company's reputation...
A Comprehensive Guide on Merging Language Models
Combining LLMs with techniques like SLERP, TIES, DARE, and MoE boosts capabilities without excessive computational burden. Uploading merged models to the Hugging Face Hub demonstrates the efficiency of this approach.
Fastest Token First: Benchmarking OpenLLMs by inference speed
Latency, especially in the context of Large Language Models LLMs), plays a crucial role in determining their practical utility, especially in real-time applications where responsiveness is paramount.
We Only Partner with Platforms Ready to Lead
Our Partnership Thrives When You Are:
- A Retail/E-com SaaS (5M−100M ARR) in a Competitive Squeeze. You see platform giants and agile startups threatening your position, and you know a bold move is necessary to secure your future.
- Aiming for Category Leadership. Your vision extends beyond the next feature release. You're ready to build a lasting competitive moat that makes your platform the undisputed choice for customers.
- Ready to Deploy AI Strategically.You've moved past experimentation with small projects and have executive buy-in to invest ($100k+) in a core AI engine that will generate revenue and solve fundamental customer problems.
- Sitting on a Goldmine of Data. With 500+ customers, you have the raw material. You need a partner with a proven methodology to transform that data from a simple record into a predictive growth engine.
Our Approach Is Purposefully Different
- We build for outcomes, not just features.You see platform giants and agile startups threatening your position, and you know a bold move is necessary to secure your future.
- We focus on transformation, not trends. We build core business value through an immersive process. We don't offer a quick or cheap way to simply add a "generative AI" label to your product — years ago we’ve tried that, it does not work
- We solve your customer's problem.Our framework makes your platform indispensable to your users. If you can't draw a straight line from your challenge to their pain, we can't help.
What's the ROI on a 15% Churn Reduction?
If your platform does $10M ARR, a 15% reduction in churn is $1.5M in saved annual revenue. Our AI engines are designed to deliver a 5-10x return on your investment, turning your product into an indispensable revenue-driver.
Still have questions? Let's talk. Book a 30-min strategy call
We are a very lean core team, so can only take on 3-5 partners at a time. The next step is a 30-minute AI Strategy Call where we'll map your market's biggest opportunity. You’ll meet me, Rohan Sawant, Founder.
Frequently Asked Questions
An engineering team builds what you spec. We bring a proven framework—market deconstruction, adoption playbooks, ROI dashboards—that ensures the feature wins. You're buying a 5x faster path to revenue, not just code.
That's exactly why we build moats, not features. Our architectures use your proprietary data and customer relationships—things Shopify can't replicate. We future-proof against commoditization.
Every engagement starts with a $25K AI Strategic Audit. You get the full battlefield map before committing to build. No blind bets.
Perfect. We embed with them, upskill them in our 90-day sprint, and ship 10x faster. You keep the IP; we keep the execution risk.
We build board-ready dashboards that track impact on churn, LTV, and upgrade rates. If we can't measure it, we don't ship it.
We are a very lean core team, so can only take on fewer than 3-5 partners at a time. We work better with companies who have flat hierarchies, lack of extended middle management, those who value relentless intensity & total ownership in execution over red tape, busy work & politics. We would not be a good fit for slow-moving organizations resistant to change, iteration & speed.
