FAILURE MODE 01
The Passive "Copilot" Trap
Your competitors are adding generic chatbots that just answer questions. It creates noise, not utility. Users don't want to chat; they want results.
Your Partner for Revenue-Generating Retail AI Engines.
We help Retail & Ecommerce SaaS (5M-100M 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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Trusted by 35+ public companies to bootstrapped founders, since 2021
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Most of our clients also knew
AI was critical
but didn't know where to begin.
Here's what happened next
Conversational Engine: Turning Legacy Platforms into AI-Native Competitors
Modernizing a 9-year-old Monolith to Defend Against AI-Native Startups
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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Confidential retail platform
Pumice: Transforming Catalog Management into a Premium Revenue Driver
Transforming a "Passive Database" into a Premium Automation Tier
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.
Pumice
MicroSegments: Enabling a Premium Personalization Tier for SaaS
Enabling a "Enterprise-Grade" Personalization Tier for Mid-Market 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."
MicroSegments (Ionio IP)
SupplierHQ: Monetizing Audience with a Recurring SaaS Asset
From "Idea" to "Cash-Flowing SaaS Asset" in 8 Weeks
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.
SupplierHQ
Retention AI: Building the Churn Prevention Module SaaS Platforms Need
Building the "Churn Prevention" Feature Your Merchants Are Begging For
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.
Confidential global retailer
Dispute Dine — Automating Operations to Unlock a New SaaS Revenue Stream
Automating a Manual Consulting Workflow into a High-Margin SaaS
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.
Dispute Dine
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AI transformations delivering 10–20x efficiency and 5–10x ROI across 35+ platforms.
Deep expertise in the AI features that actually move the needle for retail SaaS platforms. Some examples of what we typically build.
"Ionio...they clicked right away... Rohan was able to execute perfectly."
Thomas Manwaring - Founder & CEO
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"Ionio was very professional and provided excellent support throughout the entire process."
Brad Milne - Co-Founder
"Ionio came in and documented everything and then shared that over and then after that we started iterating on the code base."
Shaun Brown - Founder & CEO
"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."
Ryan Samii - Founder & CEO
"Communication is free-flowing and Veerview can almost always get a hold of someone on Ionio’s team throughout the day."
TJ Gottfried - Founder
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"Even though we are from the US and working with a company overseas, they made the experience very personable."
Reid Chong - Founder & CEO
"Ionio...has the experience in the industry to take a generalized concept and turn it into something very specific."
Eric Bort - Founder & CEO
"The communication was Flawless, like I've never had such a communication with the dev team."
Dean Sloves - Founder
"The MVP of the software was ready within eight weeks and the people who use it absolutely love it."
Greg Warner - Founder & CEO
"Communication was transparent throughout the project, including pricing, process, and timeline."
Jake Valentine - Founder & Growth Consultant
"Rohan understands both technical and non-technical aspects, which was very important for me as I don't have a technical background."
Eanna Mac Gearlit - Founder & CEO
"Ionio's team was understanding about this and did not get discouraged when new bugs were found."
Michael Guimarin - Founder
"Rohan was exceptionally patient and helpful, he was able to explain complex technical concepts in simple way"
Brittany McDaniel - CEO
They said “best decision we made.” Your turn?
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.
BOOK A CALL
Research
Read about our research work in different domains written by our team of AI researchers, not content writers
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
Garima Saroj
May 24, 2024
Read
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.
Shivam Danawale
May 24, 2024
Read
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.
Shivam Danwale
May 13, 2024
Read
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.
Garima Saroj
May 2, 2024
Read
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.
Shivam Danawale
May 2, 2024
Read
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...
Garima Saroj
April 30, 2024
Read
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.
Shivam Mitter
April 1, 2024
Read
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.
Srihari Unnikrishnan
March 14, 2024
Read
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
Garima Saroj
May 24, 2024
Read
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.
Shivam Danawale
May 24, 2024
Read
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.
Shivam Danwale
May 13, 2024
Read
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.
Garima Saroj
May 2, 2024
Read
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.
Shivam Danawale
May 2, 2024
Read
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...
Garima Saroj
April 30, 2024
Read
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
Garima Saroj
May 24, 2024
Read
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.
Shivam Danawale
May 24, 2024
Read
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.
Shivam Danwale
May 13, 2024
Read
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.
Garima Saroj
May 2, 2024
Read
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.
Shivam Danawale
May 2, 2024
Read
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...
Garima Saroj
April 30, 2024
Read
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.
Shivam Mitter
April 1, 2024
Read
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.
Srihari Unnikrishnan
March 14, 2024
Read
Stop Guessing. Start Growing.
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.
Your #1 Opportunity Identified
Competitive Intelligence Shared
Founder-to-Founder
BOOK A CALL
Frequently Asked Questions
"How is this different from hiring 3 ML engineers?"
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.
"What if Shopify launches this feature tomorrow?"
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.
"Can we start small to prove ROI?"
Every engagement starts with a $5-25K AI Strategic Audit. You get the full battlefield map before committing to build. No blind bets.
"Our in-house team is already experimenting with AI."
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.
"How do you measure success?"
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.
"What's the catch?"
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.
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