Sighted helps retailers become the products AI chooses

AI-led shopping creates a new problem for ecommerce teams. A shopper can now describe an outcome rather than search a category: the right dress for a destination wedding, a beginner-friendly golf set, a waterproof coat that packs small, a gift for a child who loves art.

A retailer may stock exactly the right product and still lose the recommendation if the information available to the shopping system does not make that fit clear. Sighted finds that gap and helps you close it.

Measure

Sighted benchmarks how your products perform across a defined set of AI shopping queries and the platform signals that matter.

Diagnose

It separates “not visible” from the cause underneath: missing product information, weak intent coverage, inconsistent source data, channel eligibility, variant issues or another measurable gap.

Enrich

It structures and adds validated product information that answers real shopper questions. The objective is better product intelligence, not more copy.

Distribute

Approved enrichment goes back into your existing stack and destinations where appropriate: product pages, structured product data, Merchant Center and product feeds, or supported commerce integrations.

Prove

Sighted reruns the same recommendation tests and connects the result to visibility, traffic, conversion and revenue where measurement allows.

Sighted grew out of SO-ME, a consumer AI shopping product from the same team, and today Sighted is the technology that powers SO-ME (so-me-app.com). Both are products of SO-ME App Ltd. That is a different starting point from a generic feed tool or an AI rank tracker: SO-ME shows how people actually describe what they want, which product facts resolve that intent, what gets recommended and, where the data allows, what gets bought.

Those learnings go into improving a retailer's product information, and the effect is then tested. SO-ME's data is not treated as a black box: as the dataset grows, Sighted will publish its methodology and aggregated findings through the UK AI Shopping Demand Index.

In one six-week retail pilot, visibility across the agreed AI shopping prompt set increased from 0% to 32%, and measured agentic-sourced sales increased 309% from the baseline period after product-data changes. It is an early case study, not a promise of future results.

Retailers and ecommerce brands that want answers to three practical questions:

1. Where are your products being selected or missed in AI shopping?
2. What product information is responsible for the gap?
3. Does fixing it improve recommendation and commercial performance?

Pilots are designed to answer those questions with a defined range, baseline, prompt set and measurement framework before a retailer commits to a larger rollout.

Molly Sishton, co-founder and CEO

Molly leads Sighted's commercial strategy, retailer partnerships, B2B sales, audits and pilot strategy. Her background is in digital marketing, SEO and ecommerce growth, and her work focuses on how AI-led product discovery changes visibility, product information and measurement for retailers.

Connie Sishton, co-founder and CMO

Connie leads brand, consumer growth and community across Sighted and SO-ME. She brings the shopper's perspective into the business, turning product-data and AI technology into experiences, content and propositions that make sense to retailers and consumers.

Together the founding team combines retailer growth and commercial strategy, consumer shopping behaviour, brand and community, and the technical infrastructure that makes product data usable by AI shopping systems.