Hey PH, we're Deborah & Alina, co-founders of botanu. Before we were at McKinsey, Verizon and in AI startups and spent years helping companies figure out where their money was going. Now we're doing the same thing for AI. Here's the problem: the whole industry is moving to outcome-based pricing: Intercom charges per resolution, Zendesk charges per resolution, everyone's going there. But when you ask these teams what a resolution actually costs them? They don't know. They check their OpenAI dashboard, maybe Helicone or Langfuse for per-call metrics, and try to piece it together across 5 different bills. That's not cost per outcome. That's cost per API call. Very different thing. botanu connects your full cost stack; models, multi-agent pipelines, RAG, vector DBs, cloud infra; and maps it to business outcomes. So instead of "we spent $7K on OpenAI this month" you get "our Voice Agent costs $1.98 per resolution with a high margin, our Chatbot costs $0.58 per resolution, and our Escalation flow is experimental at $0.83." You see which customers are driving cost, which vendors map to which features, where your RAG pipeline is leaking money, and whether your cost is trending up faster than your adoption. Nobody else shows cost per outcome, margin bands by feature, or vendor-to-feature mapping. Existing tools stop at the API layer. We go from infrastructure all the way up to the business. We're looking for design partners who are shipping AI features and need this visibility. Check out the live demo at venturedemo.botanu.ai and DM me if this is a problem you're feeling.

botanu

The complete cost engine for AI builders

The complete cost engine for AI builders