Outfindo

Outfindo turns sourced consumer research into product page content, interactive buying guides and structured data that AI shopping agents can reason with. Built for retailers and brands in considered-purchase categories where shoppers need help choosing between many similar SKUs.

Outfindo is a consumer research and commerce content platform for retailers and brands. It builds what the company calls a reasoning layer: a structured body of knowledge about how shoppers in a given category actually decide. That layer is assembled by sweeping articles, customer reviews and forum discussions for a chosen category, cleaning the noise out in stages, grouping the remaining voices into recurring themes, and ranking roughly 200 insights across four categories with a strength score computed against the full corpus. Every number in a report links back to the review, forum post or article it came from, so claims can be audited rather than taken on trust. The research is then applied on three surfaces. Product Data writes decision-grade copy for each SKU in a catalogue, explaining what a product is for, who it fits and how it compares, in a form shoppers can read and AI agents can parse. Product Guides are short interactive questionnaires on the retailer's own site that ask the questions that actually split a category and narrow a large grid to a few fitting products. The research itself is also sold standalone to teams working on assortment, filters, category trees and messaging, and the company cites Allwyn as an enterprise buyer. The pitch targets the shift toward AI-assisted shopping. A model asked for a recommendation will produce one whether or not it has good data, and Outfindo's argument is that the retailer's own catalogue is often too thin to be the source. Content grounded in sourced consumer language is the counter. The vendor reports roughly one month payback on its largest deployment, 2x or better conversion in deliberation categories such as white goods, electronics and bikes, and claims of 30x faster and 5x cheaper research than traditional methods in a head to head benchmark. Who it suits: mid to large e-commerce operations with deep catalogues where buyers struggle to self-serve, especially considered purchases with real trade-offs. It sits closer to research agencies and PIM/content enrichment than to a chatbot builder, and buyers should expect a sales-led onboarding rather than self-serve signup at scale.