Forecast1
Forecasted Share of Recommendation
A probability range for whether AI systems will recommend the brand by topic, segment, and competitor context.
AI Market Forecasting
Know whether AI will recommend your brand.
AmpliRank estimates Forecasted Share of Recommendation, explains the drivers, recommends the next move, and proves what changed with attribution and experiment evidence.
amplirank.com/dashboard
Forecasted Share of Recommendation
46%
likely range 37-55%
medium confidence
What holds you back
Thin review evidence
AI answers cite competitors more often in decision prompts.
Next move
Publish comparison proof
Expected FSR lift: 4-8 points with measurable attribution.
Operating loop
One clear path from signal to proof.
Explain2
Driver decomposition
A ranked readout of what helps or hurts recommendation probability: evidence, authority, relevance, consensus, risk.
Act3
One measurable action plan
Prioritized work with expected lift, effort, proof requirements, and measurement plan attached.
Prove4
Impact proof
Action measurements and simple experiments connect FSR movement to AI-sourced attribution and revenue signals.
Built for interpretation
Not another pile of AI polling charts.
The product hides the modeling complexity under a simple executive workflow: forecast, diagnose, improve, measure, and publish structured truth for AI systems.
Audience
Generated buyer map, pains, objections, preferred proof, and journey risk.
Sources
Evidence graph showing which domains, citations, and third-party mentions matter.
Actions
A ranked execution board built from the forecast drivers, not a static task list.
Agent Experience
Reviewable fact sheets, source bundles, llms.txt, and structured feeds for AI systems.
UAT ready
Load a demo brand, run the workflow, inspect the proof.
New workspaces can seed a realistic brand in-app so the full AmpliRank loop is testable before real customer data is connected.