Prototypes

In development (2026)

EverCurious AI develops and tests "Reflective-AI" interface variants. By engineering thin mediation layers and orchestration flows, we aim to move AI systems from "black box" answer engines toward inquiry-driven partners that support human agency.

1. Reflective-AI Mediation Layers

• Prototype output: Functional middleware for tool orchestration, retrieval grounding, and multi-model comparison.

• What it tests: How architectural-level "second-opinion" flows—presenting multiple perspectives rather than a single authoritative answer—affect user deference.

• Rationale: To determine if shaping AI input and output at the orchestration layer can measurably reduce "premature closure" before it reaches the UI.

2. A/B/C Experimental Variants

• Prototype output: A series of "reflective" interface variants compared against standard "answer-first" baseline flows.

• What it tests: The impact of specific interaction patterns—such as Socratic scaffolding and verbalized uncertainty—on verification behavior and calibrated confidence.

• Rationale: Using micro-pilots and the CDAI metric set as the primary outcome, these tests isolate which patterns strengthen independent judgment without adding prohibitive friction.

3. Prototype Guidance & Pattern Sets (v1.0)

• Prototype output: A tested catalog of Reflective-AI patterns and "anti-patterns" for AI product teams.

• What it includes: Implementation notes, scoring rubrics, and prototype concepts across both UX and technical mediation layers.

• Rationale: To provide AI labs and tech firms with actionable, field-grounded guidance for building systems that support human inquiry rather than quietly suppressing it.

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