What problems is Profound solving and how is that benefiting you?
Profound solves the problem of not knowing where or why a brand wins or loses visibility in LLM‑driven answers. Before using it, AI search felt opaque — we could see outputs in tools like ChatGPT, but we couldn’t systematically identify the gaps behind those answers or prioritise what to fix first.
What Profound does well is break this down prompt by prompt and surface the specific reason for underperformance — whether it’s an authority gap, content structure gap, narrative gap, category mis‑framing, or purchase‑path issue. That clarity allowed us to move away from generic content creation and instead map a focused strategy to fill those gaps.
The biggest benefit has been efficiency. By analysing prompts across topics, Profound helped us identify “jewel wins”, where one action (for example, strengthening owned explanatory content, clarifying category language, or consolidating purchase pathways) unlocks multiple prompts at once. This made it much easier to prioritise workstreams and be realistic about what we could improve quickly versus what required longer‑term brand repositioning.
Overall, Profound enabled us to design a strategy that is LLM‑efficient: creating content and signals that directly answer how AI systems reason, rather than guessing or over‑producing content. It turned AI visibility from a reactive exercise into a structured, defensible plan that we could clearly explain and align on with the Knorr team. Review collected by and hosted on G2.com.