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Rather than launching popups at everyone, it detects the exact moment a visitor hesitates and only then offers help.
ENTRY ANGLES
AI-powered intent inference from behavioral signals to surface relevant products without explicit user requests · Reduce friction in discovery by matching visitors to offerings based on actual intent rather than search queries · Minimize interruptions while maximizing relevance in commercial discovery experiences
VERTICALS
CAPABILITIES
AI/ML for behavioral signal analysis and intent inference, Product matching and recommendation algorithms, Integration with large product catalogs and discovery systems
NEOCOM FOUNDER
“Are you looking for something lightweight or heavier?”
Neocom helps online shoppers find what they're looking for faster and with less friction. The knock-on benefit for store owners: meaningfully better conversion from visitor to buyer.
The clever angle isn't what Neocom does when visitors arrive – it's when it decides to act. Rather than bombarding everyone with chatbots, popups, and banners the moment they land, the platform waits. It detects the specific moment when a visitor shows genuine signs of confusion or hesitation, and only then makes contact.
By the time it does, it already has a model of what the visitor has been looking for and where they got stuck – so the interaction is specific and contextually relevant, not generic.
The platform's "digital advisors" step in at these moments with targeted interventions. If someone is paralyzed by the blanket selection, an advisor might ask: "Do you usually sleep hot or cold?" or "Are you looking for something lightweight or heavier?" If a visitor is comparing bicycle frames, an advisor can explain the actual differences between aluminum and carbon fiber – and help them make the right call.
The goal isn't to push a sale. It's to help visitors find exactly what fits their needs and budget.
Under the hood is an AI engine that does three things: (a) infers hidden intent from how visitors navigate the site, (b) hypothesizes why they're stuck at each point, and (c) formulates the question or piece of advice most likely to help them move forward.
The advisor widgets can be embedded in any site and render cleanly across devices – from phone to desktop. Their visual style can be matched to the store's design, and the tone of voice can be configured to suit the brand's personality.
Store owners can monitor and tune performance through analytics on advisor interactions, adjust the triggers that activate an advisor, modify question sets, and run A/B tests to find more effective configurations.
The results speak for themselves. Stores using Neocom have seen visitor-to-buyer conversion more than double, average order value rise 5%, and overall revenue increase 11%.
Neocom offers three pricing tiers; exact figures require a demo booking.
The startup has real customers, including well-known brands like Miele and Paulmann. It just raised its first significant round of $4.29M.
Two other startups are approaching the same problem from different angles.
Session AI – [covered previously](/review/prodavat-mozhno-dazhe-neizvestno-komu) when it was still called ZineOne – built a platform that can infer what an anonymous visitor intends to buy within just five clicks on the site, even with zero prior data on that person. It has raised $43M.
The anonymity challenge is acute: roughly 90% of online shoppers are unknown to the retailer – they've never registered, or their login session expired long ago. Without a profile or purchase history, stores can't make personalized offers – which is exactly what platforms like Session AI address.
Wyng, [covered previously](/review/inogda-proshhe-sprosit) last summer, tackles the problem from the preference side. It built a platform for asking visitors engaging, visually appealing questions to build out user profiles – which then powers product recommendations tailored to each shopper. Wyng has raised $37M.
The shared problem across all three is fundamental: on virtually any e-commerce site, the right product for a given visitor almost certainly exists – but with large catalogs and limited navigation tools, most shoppers give up after a few clicks. The store loses a potential sale that was there for the taking.
This isn't a niche failure mode. Standard e-commerce conversion rates rarely exceed a few percent – meaning more than 90% of visitors leave without buying anything.
If you can understand what someone is looking for – and help even a fraction of those abandoning visitors find it – conversion can double. The real-world numbers from these startups suggest this isn't aspirational: it's what actually happens.
The meta-lesson here is worth stating plainly. Most advice about improving sales amounts to "sell harder." What actually works is "understand better." Figure out what the person in front of you actually wants – even when they haven't articulated it – and help them get there.
This applies beyond retail. Y Combinator's motto is "Make something people want" – not "get better at selling what you made"
Understanding others is genuinely hard. Humans approach it through a thick filter of their own assumptions, biases, and mental models – all of which distort perception of what's actually in front of them.
AI carries far less of that baggage. Which makes it a natural fit for inferring real intent from behavioral signals – in e-commerce and well beyond.
The direction here is clear: platforms that use AI to identify what visitors on a commercial site are actually looking for, and then surface the product or service most likely to match – without waiting for the visitor to figure out how to ask.
Despite how obvious this sounds, it's still far from standard practice. And the technology has only recently become capable enough to do it well – minimizing annoying interruptions while maximizing relevance. The timing to build in this space is good.
Neocom, Session AI, and Wyng are all credible starting points for inspiration. But the application space extends well beyond retail. The same gap – potential customers unable to find what they need, not because the product doesn't exist but because no one is helping them get there – appears in professional services, healthcare, enterprise software, and anywhere else a large catalog meets a time-constrained buyer. The vertical with the sharpest mismatch between catalog breadth and discovery quality is usually where the next version of this plays out first.