Two architectures for LLM in GTM
Aditya Lahiri
CTO & Co-Founder @ OpenFunnel
The Slopper: LLM as a generation engine.
Search space = entire TAM.
Blast out.
Rely on replies to surface intent.
More output, no reasoning.
Knowledge of intent is only obtained when someone replies.
The Contextualizer: LLM as a search space compressor engine.
Query traces. Parse unstructured sources. Monitor state changes.
Estimate probability of intent. Compress search space.
Output is a list of ranked leads with context.
Humans or downstream agents handle decision-making and action.
One architecture treats LLM outputs as the product.
The other treats LLM outputs as inputs to a decision layer.
The goal isn't more generation. It's faster time-to-knowledge.
