Designing a tool that uses LLMs to negotiate SaaS prices. Looking for feedback on the architecture.
One of the easiest ways to make an AI application difficult to use is to turn every capability into another screen. While building DealMind, we wanted to avoid that. The product has memory, evidence, strategies, what-if analysis, counteroffer guidance, customer history, and learning. But the user should not feel like they are navigating seven different applications. The solution was to organize…
DealMind aims to simplify AI-powered SaaS pricing negotiations by streamlining the user experience. Rather than overwhelming users with multiple screens for each feature, the product organizes functionality around a single negotiation object. The workflow starts at the Dashboard, then progresses through New Negotiation, Analysis, Recommendations, Strategies, What-if Scenarios, Counteroffers, Evidence, Customer History, Recording Outcomes, and Learning.
Upon initiating a new negotiation, users enter key deal details like customer, industry, segment, deal value, initial offer, and counteroffers. The system then analyzes the negotiation, presenting an Overview, Strategies, What-if Analysis, Counteroffer, Evidence, and Customer tabs to provide context. This contextual approach keeps all tools relevant to the ongoing negotiation.
Memory is made visible by showing users how the recommendation connects to historical experiences. Identifiers link the recommendation to specific memories, helping users understand why that knowledge was relevant. The interface also clearly communicates when historical data is unavailable, to manage user expectations.
Uncertainty is handled transparently; if the system lacks certain information, that limitation is made clear to the user. A 60-second demo walks users through the full negotiation loop, from loading a deal to recording the outcome and updating the learning model. This concise demonstration shows how the memory loop works in practice.
Technical details like API health and memory configuration are isolated in separate Settings sections, preventing them from distracting from the core negotiation experience. The product ultimately aims to be a supportive tool for salespeople, providing insights and guidance rather than replacing human decision-making. As users record outcomes, the system builds organizational knowledge that enhances future negotiations.
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