The core technical hurdle was evaluating "good communication," which is inherently subjective and couldn't rely on standard test sets. To overcome this, we designed a sophisticated, multi-LLM evaluation pipeline. One powerful LLM synthesized the initial flagging test set, a second acted as a judge for precision and recall, and a third continuously auto-tuned the prompt for the flagger LLM, resulting in high confidence in our system's accuracy. Furthermore, we built a dedicated agent to simulate Clarity operating in various synthetic workspaces—featuring diverse industries, sectors, and personas in conflict-prone situations—to further validate its performance.
We intentionally launched Clarity as an individual-focused product to lower the barrier to entry, with the strategic goal of establishing usage before transitioning to a team-based model (M1). We are now seeking community feedback on the product before expanding our reach.
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