Two years ago, “we use AI” meant something. Today it means almost nothing. Frontier models are commoditizing fast — every serious ecommerce tool now has one bolted on, and the differences between them keep shrinking.
So the question isn’t which model. It’s what’s underneath it.
Most ecommerce analytics tools sit on data models built for something else — generic event tracking, marketing dashboards, raw session replays. Bolt an LLM onto that and you get summaries. Generic ones. The kind your team scrolls past.
Noibu has spent years building an ecommerce-native data model: errors, conversion funnels, cart and checkout micro-stages, page behavior, custom attributes, session replays, heatmaps — all normalized, all linked. That’s the moat.
AI-native SaaS isn’t a feature anymore. It’s a baseline expectation. We’re building toward it on two anchors: Explorations, where you ask Noibu directly, and the Noibu MCP, where you bring Noibu’s data into C
Explorations is agentic AI built natively into the Noibu console. Plain-language business questions in. Data-backed answers — with charts, drill-downs, and follow-ups — out. Investigations that used to take a week of analyst time now take minutes.
A working example: in an internal test in 2025, we ran a root-cause investigation that had taken our data team four working weeks. Explorations reproduced the answer off the same data in minutes.
“This would take like a full day to scrape through. It is a life changer.”
— Don Hejny, Nerdwax
Custom dashboards, custom events, and additional AI agents (marketing campaigns, product performance, A/B testing) are on the roadmap for H2.
Most comparable products added an AI layer on top of UX or behavioral data. We took the opposite path: we built an AI analytics product on an ecommerce-native data model from the ground up. Different starting point, different ceiling.
Currently in early access with select Noibu design partners. Open beta planned for later this quarter. We’re talking to ecommerce teams now about joining the design cohort.
Most ecommerce teams aren’t waiting for a new AI tool. They’ve already picked one — Claude, ChatGPT, Gemini, whatever the team has standardized on. The bottleneck isn’t the model. It’s that none of those models know anything about your store.
The Noibu MCP fixes that. MCP — Model Context Protocol — is the open standard for connecting structured data sources to LLMs. With the Noibu MCP, your store’s full Noibu data layer plugs directly into C. No console, no separate seat, no context-switching.
Your marketing manager opens Claude and asks “what’s killing mobile conversion this week?” Claude reasons across Noibu’s actual session, funnel, page-performance, and error data — and answers. With charts. With sources. With deep-links back to the Noibu console for the underlying replay or heatmap.
CarsnCards, a Toronto-area ecommerce brand, ran one of the earliest Noibu MCP POCs. Their team went straight to Claude, pulled bounced-at-cart data through the MCP, and used what they found to support a real business decision — a Toronto market expansion. The setup wasn’t perfect (we learned a lot from that POC), but the wow moment landed: their team kept reaching for Claude unprompted, because Claude finally had their actual data.
You don’t have to bet on the AI tool. Your Noibu data shows up d.
Every ecommerce team we talk to is already using AI for something — ad copy, forecasting, product descriptions, support drafts. And almost every team hits the same two walls:
Wall 1: setup. Their data lives across Shopify, GA4, Klaviyo, Meta, Google, Gorgias, error monitoring, replay tools, ad platforms. Connecting any of it to an LLM is a multi-week project that requires engineering they don’t have.
Wall 2: relevance. Even when something gets connected, the LLM gives them generic answers. It doesn’t know their store, their funnel, their customer segments. So the output reads like a search result, not analysis.
Both walls are data problems disguised as AI problems. That’s why our anchors — Explorations and the MCP — are designed around the data model, not the model layer. AI-native SaaS isn’t a feature we shipped this quarter. It’s the baseline expectation we’re rebuilding the platform around.
A few things are in motion across Q2 and Q3 that extend the same thesis:
Quick answers about Explorations, the Noibu MCP, and how we’re thinking about AI.
Explorations is agentic AI built into the Noibu console. You ask plain-language business questions (“why did add-to-cart drop last week?”) and Explorations reasons across Noibu’s ecommerce-native data — traffic, funnel, cart, checkout, page behavior, errors, custom attributes — to return a data-backed answer with charts and follow-ups. It’s currently in closed beta with select design partners.
The Noibu MCP is a Model Context Protocol connector that brings your full Noibu data layer into the LLM your team already uses — Claude, ChatGPT, Gemini, or any LLM that supports MCP. Instead of switching tools to investigate something, your team reasons across Noibu’s data in their existing AI workflow.
Those products added an AI layer on top of session replay or behavioral data that was originally built for a different job. Noibu took the opposite path: we built our AI products — Explorations and the MCP — on an ecommerce-native data model from the ground up. The data model is the moat.
Any LLM that supports the Model Context Protocol standard. That includes Claude, and a growing list of clients and IDEs that have adopted MCP. Practically: if your team uses Claude, ChatGPT, or another MCP-compatible client, the Noibu MCP plugs in.
Errors and priority issues, sessions analytics, page visit metrics (click rate, conversion, revenue per session), heatmap and scroll map data, session replay deep-links, and domain management. Effectively: everything Noibu already captures about your store, queryable in your LLM.
Explorations is in early access with select Noibu design partners. Open beta is planned for later this quarter. We’re not committing to a hard public availability date until we’re confident in the experience. Teams interested in joining the design cohort can request access on this page.
The Noibu MCP V1 is launching in mid-May 2026. Existing Noibu customers can talk to their account team about setup; new customers can reach out through the contact form on this page.
It means our data schema was designed specifically for ecommerce reasoning — the entities are funnels, carts, checkouts, sessions, products, errors, page behavior — not generic events. When an LLM reasons across that data, it’s already speaking the language of ecommerce.
No. The MCP is an additional surface, not a replacement. Some teams will prefer the console (especially for visual work — replays, heatmaps). Some will prefer their LLM. Most will use both. The MCP also deep-links into the console for things like specific session replays.
Noibu One is our self-serve offering for Shopify SMBs ($1M–$5M GMV) — it bundles the Noibu MCP plus connectors for Meta, Google, Klaviyo, Shopify, and Gorgias into a Claude-native workflow at SMB pricing. The standalone Noibu MCP described on this page is for our enterprise customers who want to bring their Noibu data into an LLM they already operate.
It’s our argument that AI-native ecommerce analytics will be won on the underlying data model, not on the LLM. Frontier models are commoditizing — every serious tool will have AI. The lasting differentiation is whether the data underneath the AI was built for ecommerce reasoning. Ours was.
The fastest signal is Kailin Noivo’s LinkedIn. For long-form, The Ecommerce Toolbox: AI in Retail podcast covers what AI looks like inside actual ecommerce teams. We’re also publishing a Q2 research report on AI in ecommerce around Shoptalk Barcelona.
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