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"
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.
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
Technical deep dives written by our team of AI researchers, not content writers.
What are Large Action Models (LAM) and How They Work?
We discuss a new trend in the generative AI field: Large Action Models that can not only give instructions but take action on a user's behalf.
LLMs in production with guardrails
Guardrails are crucial safety measures that guide models to avoid unintended harm. Implementing them boosts customer trust and reputation.
A Comprehensive Guide on Merging Language Models
Combining LLMs with techniques like SLERP, TIES, DARE, and MoE boosts capabilities without excessive computational burden.
Fastest Token First: Benchmarking OpenLLMs
Latency plays a crucial role in determining practical utility. We benchmark open source models specifically for real-time applications.
Research & Intelligence
We don't just use APIs; we optimize the architecture. Explore our technical deep dives into the next generation of AI.
Large Action Models (LAM): Beyond Text Generation
Analyzing the shift from "Chat" to "Action." How LAMs autonomously navigate interfaces to perform complex user tasks.
Production Guardrails & Trust Layers
A technical framework for preventing hallucinations and enforcing compliance in regulated industries (Fintech/Health).
Merging LLMs: SLERP, TIES, and MoE Techniques
How we combine specialized models to boost capabilities without doubling inference costs or latency.
Fastest Token First: OpenLLM Latency Study
A comparative analysis of inference speeds across Llama 3, Mixtral, and proprietary models for real-time use cases.
R&D and Technical Expertise
We don't just implement APIs. Our engineering team researches novel architectures to solve fundamental platform challenges.
Beyond Chatbots: Large Action Models (LAMs)
Analyzing the new trend of models that can take direct action on a user's behalf, enabling complex workflow automation within your platform.
Production LLMs with Deterministic Guardrails
Implementing crucial safety measures that sit outside the model to mathematically guarantee avoidance of unintended harm or hallucinations.
Advanced Techniques in Merging Language Models
A technical breakdown of combining LLMs using SLERP, TIES, and Mixture of Experts (MoE) to create specialized, efficient models.
Fastest Token First: Benchmarking OpenLLMs
Latency is the defining factor for UX. We benchmark open source models specifically for time-to-first-token in production environments.
R&D and Technical Expertise
We don't just implement APIs. Our engineering team researches novel architectures to solve fundamental platform challenges.
Beyond Chatbots: Mannan Action Models (LAMs)
Analyzing the new trend of models that can take direct action on a user's behalf, enabling complex workflow automation within your platform.
Pranav Is Sleeping
Implementing crucial safety measures that sit outside the model to mathematically guarantee avoidance of unintended harm or hallucinations.
Advanced Techniques in Merging Language Models
A technical breakdown of combining LLMs using SLERP, TIES, and Mixture of Experts (MoE) to create specialized, efficient models.
Fastest Token First: Benchmarking OpenLLMs
Latency is the defining factor for UX. We benchmark open source models specifically for time-to-first-token in production environments.
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.
