AI is becoming a new sales channel in commerce

Win product recommendations in AI and turn them into purchases

See why AI recommends competitors and which onpage, offpage, and MCP/API actions make your products easier to recommend and buy.

Presence show up
Preference get recommended
Conversion be buyable
Free analysis • Takes ~2 minutes • No credit card required
Book a demo

⚡ Free analysis • Takes 2 minutes • No credit card required

Sales preview
Decision Intelligence Snapshot
Live analysis
AI demand signal
Relevant sources
  • Reviews and test pages
  • Buying guides and comparison pages
  • Reddit and community signals
Priority actions
  • Prioritize comparison FAQs
  • Strengthen offpage sources
  • Launch MCP/API layer for buy paths
Top influence Reviews + guides
Commerce layer MCP/API recommended
Works with any shop system

Why AI revenue gets lost

AI is already shaping decisions. Most brands and shops are not prepared for it.

Today, companies lose demand to competitors that are explained better, referenced better, and easier to buy through AI.

01
AI names other products first

Shoppers ask AI for recommendations. Competitors get named because they are stronger across reviews, guides, and comparisons.

02
The reasons stay invisible

Most teams cannot see which sources, content gaps, or product attributes are actually driving AI recommendations.

03
Even visible products lose conversion

Without clean variant logic, compatibility checks, and buy paths, AI-driven demand does not turn into a smooth purchase flow.

From signal to action

Gencko turns AI demand signals into a sales-ready action system

We show what AI sees, why competitors win, and what your team should change next.

Signal -> explanation -> action
01
Track Prompts, models, competitors
02
Explain Sources, gaps, reasons
03
Win Actions, MCP, buy paths
How Gencko works
Gencko translates signals into prioritized actions
Signals
External sources Reviews, guides, comparisons, communities
Shop and product data PDPs, variants, availability, buy paths
Gencko interprets
Gencko engine prioritizes signals, explains the reasons, and derives concrete actions
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Recommended actions
Onpage Content, FAQs, and structure
Offpage Media, reviews, and communities
AI commerce layer MCP, APIs, and buyable flows
🔍
Track presence and preference

See where your products show up, where competitors win, and which prompts actually matter.

🧠
Explain the decision

Understand which reviews, guides, FAQs, and product attributes influence AI recommendations.

⚡
Deliver the next actions

Get prioritized tasks for onpage content, offpage representation, and AI-facing product infrastructure.

🛒
Make products buyable

Expose structured product data, variant logic, and checkout-ready flows through MCP and APIs.

Concrete actions

Three focused levers to win more AI recommendations

Gencko does not stop at reporting. It tells your team what to change on the site, off the site, and inside the AI commerce layer.

Onpage
Fix what AI cannot understand on your site

Turn missing buying context into stronger PDPs, categories, and landing pages.

  • Content and comparison gaps
  • FAQs buyers actually ask
  • Technical structure for AI readability
Offpage
Strengthen the external sources AI already trusts

Build a sharper external footprint where AI already gets confidence signals.

  • Media and guide opportunities
  • Review and Reddit signals
  • Corrections to weak external representation
AI commerce layer
Make products usable for LLMs and agents

Connect recommendation to structured product access and a real buy flow.

  • MCP and API endpoints
  • Variant and compatibility logic
  • Cart and checkout readiness

How prioritization works

Every recommendation is tied to a real signal

Gencko does not work from rigid templates. The platform detects signal patterns and maps them to the right onpage, offpage, or AI-layer workstream.

Content and comparison signals
Typical signal

External reviews, guides, or comparison pages explain buying criteria more clearly than your own PDPs, category pages, or landing pages.

→
Prioritized workstream

Prioritize onpage work: comparison content, FAQs, decision criteria, use cases, and structured product presentation.

Trust and source signals
Typical signal

Communities, Reddit, forums, review pages, or media sources make competitors look more credible and relevant than your brand.

→
Prioritized workstream

Prioritize offpage work: strengthen relevant sources, improve mentions, correct weak representations, and expand social proof.

Commerce and buy-path signals
Typical signal

AI cannot reliably resolve products, choose the right variant, or initiate a clear transaction flow.

→
Prioritized workstream

Prioritize the AI commerce layer: MCP/API, variant logic, compatibility checks, and checkout-ready buy paths.

Ready to stop losing AI-driven demand?

Start with an analysis or book a demo to see where competitors win and what Gencko would change first.

Book a demo
Free analysis • Takes ~2 minutes • No credit card required

FAQ

Questions, answered

Gencko covers the full AI commerce funnel: presence, preference, and conversion monitoring, source and competitor analysis, prioritized recommendations for onpage and offpage work, plus an AI commerce layer for structured product data, variant logic, and buy paths. That means you do not just see whether you are mentioned. You understand why competitors win, which actions matter most, and how AI demand can become a buyable flow.

Gencko analyzes which sources, content patterns, product attributes, and competitors appear inside AI answers and which signals support those recommendations. It then turns that evidence into prioritized actions for specific pages, external sources, and AI-facing interfaces. The result is not a generic checklist, but an action backlog that explains what to change, why it matters, and where the strongest leverage sits.

Typical onpage recommendations include missing comparison content, buyer objections, FAQs, use-case coverage, product attributes, internal linking, and technical clarity for AI readability. Gencko does not stop at broad themes. It helps prioritize which PDPs, category pages, or landing pages should be updated first and which missing content is weakening recommendation quality for both AI systems and buyers.

Gencko shows which review sites, buying guides, media pages, Reddit threads, forums, and other external sources influence competitor recommendations. From there, it prioritizes where stronger representation, corrections, or additional credibility would have the biggest impact. That makes offpage work much more concrete than a vague PR or awareness exercise.

It is the layer that exposes structured product data, variant resolution, compatibility checks, and buy paths through MCP and APIs so AI systems can recommend, resolve, and transact more reliably. This becomes critical when you want AI demand to move beyond visibility and into dependable conversion and agentic commerce flows.

A first report is usually available within minutes. From there, monitoring, source analysis, and action planning can deepen step by step. That gives teams fast initial visibility while still building toward the more structural improvements that drive durable recommendation wins and cleaner AI-led conversion.

No. Gencko does not blindly rewrite your site. It provides prioritized recommendations, explains the signals behind them, and can expose structured product data through the AI commerce layer. Your team decides what to implement on the site and offsite, which keeps the platform usable across marketing, SEO, product, and commerce workflows.

Gencko works best when ecommerce, SEO, content, product, and growth teams need a shared view of why AI recommendations are won or lost and what to do next. The platform creates a common operating model: monitoring shows the gap, source and competitor analysis explain the cause, and onpage, offpage, plus AI-layer recommendations turn insight into an executable backlog.