Pricing
Plans for decision intelligence and AI commerce enablement
For teams that want to measure AI visibility, derive concrete actions, and operationalize the new sales channel.
Monitoring
understand signals and competitors
Actions
onpage, offpage, and AI layer
Enablement
roll out with clear next steps
Starter
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For teams starting with AI signals, competitor analysis, and their first action backlog.
- 1 Workspace
- 2,400 prompt runs / month
- Recommendations for 20 key pages / month
- Deep tracking for 2 competitors
- Visibility and recommendation monitoring
- First onpage and offpage action backlog
- AI-commerce readiness overview
- E-Mail Support
Growth
Most popular
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For teams systematically building onpage, offpage, and AI-commerce capabilities.
- 1 Workspace
- 9,000 prompt runs / month
- Recommendations for 80 key pages / month
- Deep tracking for 5 competitors
- Prioritized backlog by workstream
- Onpage, offpage, and AI-layer backlogs
- Category and catalog-level monitoring
- Readiness for MCP/API and buyable flows
- E-Mail Support
Scale
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For ambitious brands, merchants, and multi-market teams rolling out AI commerce.
- Multiple Workspaces
- 27,000 prompt runs / month
- Recommendations for 150 key pages / month
- Deep tracking for 10 competitors
- Multi-market and multilingual rollouts
- Advanced API, feed, and AI-layer support
- Monitoring across larger catalogs and regions
- Dedicated Account Manager
- MCP/API and AI layer rollout support
Agency
For agencies running AI visibility, action backlogs, and AI-commerce rollouts across multiple clients.
- Multiple Workspaces
- Dedicated Agency Features
- Create Preview Accounts for potential Clients
- White-label reporting
- Separate client setups and priorities
- Run multiple client rollouts in parallel
- Built for AI-visibility and commerce retainers
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What's included
What every plan includes
All plans combine monitoring, source-driven actions, and commerce enablement.
Presence and preference monitoring
Track prompts, models, sources, and competitors.
Onpage and offpage actions
Get concrete guidance for content, FAQs, technical structure, and external representation.
AI commerce layer
Be ready for structured product data, MCP/API, and buy paths.
Revenue-oriented prioritization
Work first on the actions with the biggest commercial upside.
Add-ons
Additional modules for rollout, support, and execution
For teams that need more guidance, more markets, or deeper implementation support.
Recommended for Growth and Scale
Optional success manager for ongoing support
If you want to build an operating process, not just adopt a tool, a dedicated success manager supports rollout and monthly prioritization.
On request
- Monthly status and prioritization call
- Support for setup, rollout, and team adoption
- Sparring on onpage, offpage, and AI-layer actions
- Ongoing support between reporting cycles
Onboarding
Setup and initial rollout
A guided start for prompt sets, source focus, priority pages, and reporting structure.
On request
- Kickoff and goal alignment
- Tracking and priority setup
- Initial action-backlog structure
Reporting
Monthly executive reviews
Condensed reporting for leads, stakeholders, and market owners.
On request
- Monthly results review
- Management-ready summary
- Priorities for the next cycle
Markets
Additional markets and regions
Extra countries, languages, or storefronts within a broader rollout.
On request
- Multilingual prompt sets
- Regional competitors and sources
- Market-specific reporting
Integrations
Extended AI-layer integration
Additional support for MCP, APIs, variant resolution, and buyable flows.
On request
- API and feed coordination
- Commerce and data-model support
- Enablement for agentic flows
Need a tailored rollout?
Talk with sales to customize limits, onboarding, and integrations.
FAQ
Frequently asked questions
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.