Executive summaries, Academy training, AI Preference, Share of Voice, and graph actions are now stronger for agency teams.

What changed in the app

This public summary focuses on the improvements agencies can use in client work: executive reporting, prompt-level proof, practitioner training, competitive AI preference, target-specific visibility, graph remediation, and reporting clarity.

• Intelligence summaries now consolidate LLM Perception, AI Preference, GEO, Web Perception, Share of Voice, and knowledge graph findings into one executive view for client reviews.
• Executive summary evidence cards expose the audited prompt, AI answer, source context, target, audit date, confidence signal, and missing-proof gaps behind each recommendation.
• Evidence drilldowns separate exact, fallback, supporting, and missing evidence so agencies can defend what is proven and prioritize the proof clients still need.
• Audit pages now surface summary prompts and answer evidence directly inside LLM Perception, AI Preference, GEO, Web Perception, Share of Voice, and the Intelligence summary.
• VectorGap Academy has been expanded with deeper practitioner tracks for GEO analytics, AI agents, local AI optimization, crisis management, 404 recovery, citation authority, verticals, multimedia authority, and private-data optimization.
• Academy lessons now surface richer exercises, checklists, templates, and certification-style assessments so teams can turn training into client deliverables.
• AI Preference audit history refreshes more reliably after launch and keeps provider-level recommendation results, competitive win/loss context, prompt evidence, and trendable scores ready for agency reviews.
• Competitive head-to-head drilldowns show which competitors are preferred, what sources support the answer, and where a client needs stronger proof.
• Share of Voice and Share of Model views are target-aware: Global and each audited country are separated across overview cards, exports, trends, and competitor rankings.
• The Intelligence overview explains AI Preference, LLM Perception, GEO, Web, Share of Voice, and graph drivers with clearer entry points for each audit type.
• Query Explorer is easier to inspect with audit grouping, provider context, prompt text, status, and cleaner drilldowns for client-facing evidence.
• Knowledge graph clusters are wider, labels are more readable, hover behavior is steadier, and export output better matches the visible graph.
• Graph actions open as focused action details and create visible remediation state instead of leaving teams with passive diagnostic panels.
• Strategic pillar health and brand knowledge signals use cleaner source-backed inputs, so weak evidence, category gaps, and negative trends are easier to explain.
• Security, API access, webhook handling, and protected dashboard states have been hardened so users see real product data, safer actions, and fewer confusing placeholders.