{"id":1651,"date":"2026-01-13T11:30:25","date_gmt":"2026-01-13T11:30:25","guid":{"rendered":"https:\/\/www.vaayushop.com\/blog\/?p=1651"},"modified":"2026-01-15T06:02:17","modified_gmt":"2026-01-15T06:02:17","slug":"beyond-context-graphs-ai-reasoning-with-gtm-strategy-example","status":"publish","type":"post","link":"https:\/\/www.vaayushop.com\/blog\/beyond-context-graphs-ai-reasoning-with-gtm-strategy-example\/","title":{"rendered":"Beyond Context Graphs – AI Reasoning with GTM Strategy Example"},"content":{"rendered":"\n

Why now – A trillion dollar blog<\/h3>\n\n\n\n

When Foundation Capital<\/strong> article – AI\u2019s trillion-dollar opportunity: Context graphs<\/a><\/strong> & Dharmesh Shah<\/strong> wrote about its opportu<\/em><\/a>nity, it reinforced adjacent angle that we have been building & executing on. Context Graphs & AI Reasoning make interesting area where big inflexion points will happen.<\/p>\n\n\n\n

But context graphs are already red ocean. Yet not all is lost. Larger field of Cognitive Automation has 10s of other fields than context graphs. Some of them we work upon which aren’t context graphs.<\/p>\n\n\n\n

Big unlocks – Human Decisioning & Reasoning with AI<\/h3>\n\n\n\n

Advance AI fields everyone must know<\/h3>\n\n\n\n

Across AI Infrastructure, there’s a rush to mimic Human Decisioning & Reasoning<\/strong>. That’s the biggest unlock. Many a times called Cognitive Automation<\/strong>. <\/p>\n\n\n\n

All the fields under that are adjacant tech or subs stream. Eg Context Graph is just one of them.<\/strong> <\/p>\n\n\n\n

Eg. Decision Intelligence System, Autonomous Agents & Agency; Joint Embedding; World Models, Context Graphs; Thinking & more.<\/p>\n\n\n\n

Among those, Vaayu works at Decsion Intelligence Layer under Cognitive Automation the most closely. (See a simplistic example of our work below)<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

If you prefer more techncial style & depth – pls request here<\/a>.<\/h4>\n\n\n\n

Context Graph – not the only big thing & way<\/h2>\n\n\n\n

Context graphs have limitations & they are very hard to acquire plus some new technologies may compete with them. So it’s worth looking at adjacant technolgoies. Eg. Stream that we have been working on.<\/p>\n\n\n\n

Eg. Context graph can tell you why a particular decision was taken; But it can’t tell you what should I change now. nor what will be the impact on revenue if such decisions continue to be taken.<\/p>\n\n\n\n

AI Reasoning based on World Modelling & Latent State<\/h2>\n\n\n\n

World & decisions are not linear. This is why most theories fail. The end outcomes are based on various sub systems working together.<\/p>\n\n\n\n

Eg. Consider a GTM strategy of a company -> Impacts Top of funnel Traffic -> Impacts Operations -> Impacts load on Customer Support -> Service Ticket Loads -> Impacts brand value, Revenue, Payment recovery<\/p>\n\n\n\n

So GTM strategy doesn’t work in isolation. It has latent impact.<\/p>\n\n\n\n\n\n

What if we could automate above Decision loop with AI<\/h3>\n\n\n\n

Humans – C-suite reason as the world changes & take action.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Limitations of Context Graph – Example with GTM solved with Vaayu’s Decision Intelligence & AI reasoning<\/h2>\n\n\n\n

Let’s take an example of a company which is very aggresively preparing for IPO. It wants to own best in world tech, wants to improve metrics, launch to new markets, new segments.<\/p>\n\n\n\n

Company has so far deployed traditional Dealer network, offline marketing like trade shows. <\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

AI Reasoning for GTM – Context graphs can’t answer<\/h2>\n\n\n\n

Decision Intelligence -> Impact on Revenue<\/h3>\n\n\n\n

So management question is if we used AI – how will revenue improve. They chalk out a strategy to go AI & CRM first. There current channel distributions for AI model training or context are modelled above in pic. <\/p>\n\n\n\n

They want to make some changes, budget allocation, team redistribution, introduce AI & heavily use CRM features. This will trigger<\/p>\n\n\n\n

So context graphs stop being useful. It can tell you why it happened. But not – what will change.<\/p>\n\n\n\n

Context graphs<\/strong> answer:
\u2192 Why did this decision happen?<\/em>
\u2192 What context led to this outcome?<\/em><\/p>\n\n\n\n

They do not answer<\/strong>:
\u2192 If we change X, how will revenue change?<\/em>
\u2192 What should management do next?<\/em><\/p>\n\n\n\n

From Context Graphs to world models<\/h2>\n\n\n\n

A world model<\/strong> is an internal, learned representation of an environment that captures:<\/p>\n\n\n\n