Surfacing the topics where your brand is missing from AI answers
Knowledge Gaps runs deep research across multiple AI models (GPT-5, Perplexity, Gemini) to identify topics in your category where your brand is absent, weak, or underrepresented. Each gap comes with an explanation, a why it matters rationale, a checklist of action items, and the option to start tracking it as a prompt set.
The feature is a qualitative guide, and is not based on your prompt monitoring results.
Why blind spots are more dangerous than weak rankings
You cannot fix what you cannot see. Visibility tools tell you how you perform on the prompts you already track, but the gaps that hurt most are the ones you have not thought to track yet. Knowledge Gaps surfaces those blind spots before competitors lock them in.
How multi model research uncovers and structures each gap
Click Start Research to kick off the analysis (typically 30 to 60 seconds). ALLMO queries multiple models in parallel and reports per model success or partial failures so you know what made it into the result set.
Each gap is stored with a topic, details, "why it matters" rationale, and a list of action items split into:
Core Optimizations: the structural moves.
AI Generated Tips: model suggested specific actions.
You can check off action items individually. ALLMO records who completed what and when.
You can also use the Tag associated with each knowledge gap, to highlight prompts related to the knowledge gap, to view how information improves over time.
Once tracked, each gap shows a Brand Visibility block with average mention % and citation % from web search models that cite your domain.
Running your first research cycle and tracking the gaps that matter
Open Recommendations › Knowledge Gaps for the report.
If empty, hit Start Research and wait around 30 to 60 seconds.
Read each gap row. Expand it to see the full action checklist and rationale.
For gaps that matter, click Generate Prompt Suggestions, pick language and location, and add the prompts that fit. This automatically marks the gap as tracked, applies the tag, and starts weekly monitoring.
Work through the action items, checking them off as you complete them.
Re run Start Research periodically (e.g. monthly). New gaps emerge as your category and the models evolve.
Closing gaps and measuring the lift over time
High "why it matters" plus low effort action items: start here. Quickest path to closing visibility gaps.
A gap with strong overlap to your product: treat it as a content brief. Build a dedicated landing page, a blog explainer, and external assets (Reddit thread, YouTube video, third party mentions) around it.
Gaps where competitors dominate: reverse engineer their cited content. What format, length and angle is the model rewarding?
Track the brand visibility block over time: for tracked gaps, the mention % and citation % are your scoreboard. Set a target (e.g. raise mention % from 15% to 40% in 90 days) and revisit weekly.
Apply common sense when reviewing gaps: the recommendations are AI generated. The algorithm works well, but occasionally, especially when the information situation on your brand is already strong, only non critical gaps will surface (because the AI will always find something). Treat the list as input, not gospel.
Watch for missing key decision criteria: this view is especially useful for spotting when information on core buying signals is missing from AI answers, for example a pricing page, comparison content, or integrations overview. Those are the gaps that cost you deals.