Cosolvent Design Plan

Cosolvent is designed to be help diverse and remote users to identify one another and, ultimately, to connect and do exchanges with one another. The exchanges can vary greatly to accomodate the wide variety of thin markets that need help. For example, the exchages may involve sales contracts, knowledge sharing, personal collaborations, as well as other constructive interactions. The conceptual model for all of these exchanges involves a series of common-sense steps that must be completed before meaningful exchange can take place. The model for this process is shown below:

The model assumes that the core of any exchange between unrelated parties will need to progress through at least 3 levels of trust building. No one will waste time, let alone money or effort, if they don’t trust the counterparty. Hence, the primary objective of any attempt to build a thin market exchange must focus on building that trust:

• Trust to Explore – People are busy. They don’t have time to waste on frivolous exchanges or questionable counterparties. This is especially true in thin markets, where the participants will generally be unfamiliar with their opposite number. Under these conditions, participants may be unwilling to even entertain an advance or to reach out and make an approach of their own.
• Trust to Engage – People may become interested in some of the other participants or their offerings. That is still not enough to spur someone to reach out and make contact. Every market has lurkers and looky-loos and it takes a targeted incentive to draw lurking users to reach out and make contact.
• Trust to Accept – Assume that two participants discover each other and enter into a dialog. What then? When do they conclude that they know enough to be willing to sign a contract or remit funds? In familiar thick markets like Amazon, the decision may be spontaneous. In thin markets, however, participants will have more questions and need more concrete answers.

Deeperpoint believes that conventional software systems can only seldom achieve these levels of trust due to cost, practical, and technical reasons. However, what if AI technology were added to the design? What if they could be harnessed to make exploration easier, first contact more targeted, and quickly assemble all of the information needed to reassure the participants that their acceptance was justified? That is the design question and the design roadmap that DeeperPoint is trying to follow.

Discovery

The Discover Phase is where Cosolvent can make it easier for potential participants to join and participate. As such, it is the first part of the design challenge that Deeperpoint is attempting to build.

The core idea is to use AI to do two things:

1.  Allow individual users to upload their own collection of unstructured materials (text, pdfs, images, even videos) to tell  potential counterparties who they are and what they do. With AI, it is possible to look at a folder of miscellaneous documents and ask the AI to craft a summary profile according to general guidelines. A key part of the process is a sequential approval system that allows users to label specific assets as public, visible to registered users, available only with approveal, or private. Other categorizations can be added for specific thin market situations. The goal is to make easy for users to “fill out” their profile, while giving them control over which users see what material.
2. If a user wants to view other users’ published profiles, they can use AI to conduct sophisticated and very specific searches. Eventually , if a grain buyer wanted to know which farmers use John Deere equipment, an AI directed search might give them a set of prospects that match that oddball criterion.

Cosolvent’s discovery features do other, more mundane, functions. For example, it allows administrators to craft structured prompts to generate and format complex or obscure searches. It can also integrate contextual information such as the questioner’s own profile and industry context information about a product or service to generate the kind of multi-factor answers that would be a big challenge to program with conventional code.

Communication

This first phase of Cosolvent development also focuses on provision of key administrative utilities. A playground is provided so administrators can play with different LLM models and embedding tools, as well as try out different types of structured prompts to get nuanced answers from the data that will eventually populate the system.

If participants can find and assess one another, the next task is to establish communication. The participants in many thin markets will be unused to talking directly with distant counterparties and there will be a lot of resistance to starting or accepting the conversation. 

Negotiation