Make better use of a council of AI models.

Rate a completed decision, record how it affected your thinking, and download an auditable report.

Turn a vague concern into a concrete question that an AI council can analyze from several perspectives.

Select a useful mix of OpenAI, Gemini, Claude, Grok, and DeepSeek models without adding diversity for its own sake.

A practical walkthrough from entering a question to reading the synthesis and token receipt.

Read distinct considerations, supporting evidence, materiality, disagreement, and verification status after a council run.

Update a council recommendation when you learn new facts without starting the reasoning from scratch.

Understand free estimates, prepaid credits, retry-aware reservations, final token charges, and result release.

Analyze compensation, meaning, advancement, commute, risk, family effects, and reversibility before changing jobs.

Use deliberation to weigh authority, compensation, service burden, politics, staff quality, and long-term career effects.

Organize emotions, recurring patterns, needs, safety, repair attempts, and future compatibility without pretending AI replaces human support.

Compare work, housing, commute, community, family, climate, schools, and reversibility in a relocation decision.

Evaluate educational choices using career value, learning goals, debt, time, completion risk, and alternative paths.

Use a council to test affordability, alternatives, total ownership cost, timing, and downside risk before a major purchase.

Ask the Council to retrieve current sources before deliberating and preserve the source list with the run.

Compare strategic options using objectives, evidence, capabilities, competitors, implementation constraints, and explicit uncertainty.

Structure job-related evidence and tradeoffs while keeping humans accountable and avoiding sensitive or protected information.

Compare product initiatives using customer value, evidence, effort, strategic fit, risk, and sequencing dependencies.

Ask an AI council to imagine a failed plan, identify plausible causes, and convert them into mitigations and stop rules.

Use models to represent competing positions, uncover shared assumptions, and identify evidence that could move a dispute.

Examine policy goals, mechanisms, affected groups, implementation, evidence, unintended effects, and alternatives.

Copy a complete design safely, test one benchmark item end to end, and scale only after the path passes.

Move from an interesting council demonstration to a reproducible comparison with explicit conditions and outcomes.

Use the Outcome Builder to define what will be measured, when, at what unit, and with which scoring rule.

Control model rosters, turns, information sharing, order, roles, prompts, parameters, aggregation, and failure handling.

Design the key comparison between models exchanging information and repeated same-model reasoning under matched exposure.

Collect several model answers without peer exposure, then aggregate them by vote, confidence, judge, or no aggregation.

Set assignment units, reproducible seeds, repeated trials, blocking, and fair compute constraints across conditions.

Randomly assign people to deliberative or non-deliberative AI interactions and measure what changes afterward.

Measure baseline beliefs and later outcomes without losing the transcript or confusing participants at the transition.

Match response controls to the construct being measured and produce outputs that remain interpretable.

Distinguish whether participants received, noticed, and understood the assigned interaction without hiding exclusions.

Use complete summaries and structured exports for outcomes, change scores, checks, assignments, and transcripts.

Arrange consent, baseline measurement, stimuli, assigned interaction, process tasks, final outcomes, and debriefing in a clear participant journey.

Measure whether people retain or revise an answer after solo AI, council, hybrid, or human advice.

Collect interpretable timing and task-performance diagnostics without invasive surveillance.

Insert live human contributors into an experimental conversation and compare human deliberation with AI conditions.

Place one or more people between model turns to study mixed human–AI reasoning and influence.

Show participants standardized communication material before assigning solo-AI, council, human–AI, or human interaction conditions.

Compare solo, Council, human, human–AI, scripted-feedback, and no-tutor conditions using immediate, transfer, and delayed learning outcomes.

Use measurement packs, a transparent sample-size approximation, a pre-launch audit, and a machine-readable variable codebook.

Use the experiment activity dashboard to detect drop-off, failed calls, retention loss, and budget pressure while a study is live.

Configure established strategic-interaction games for human, model, and mixed groups with auditable payoffs.

Study unilateral allocation choices between human or model players with explicit endowments and recipients.

Study offers and acceptance decisions when rejection eliminates the proposed split.

Measure investment and return behavior after a transferred amount is multiplied.

Compare cooperation and defection under a payoff matrix that preserves the game’s strategic ordering.

Study contributions to a shared pool among two to eight human, model, or mixed participants.

Study coordination between a high-value joint outcome and a safer individual action.

Set experimental units, dollar displays, payoff parameters, repeated rounds, and information visibility without changing the intended mechanism accidentally.

Distribute one-time seat links safely, record consent, monitor progress, and handle incomplete sessions responsibly.

Choose item counts, categories, reasoning levels, jurisdictions, sampling methods, and seeds without changing source questions.

Use MMLU-Pro categories to test difficult multiple-choice reasoning under controlled council conditions.

Study policy understanding and application using the preserved U.S. PolicyBench configuration and structured facets.

Test beliefs, intentions, emotions, knowledge states, deception, and perspective-taking with canonical or five-variant scoring.

Turn selected outcomes into editable whisker plots and interpret estimates, uncertainty, titles, notes, and labels responsibly.

Invite collaborators, keep owner billing intact, lock manifests, preserve source revisions, and retain an auditable run history.

Compare human and model language using automatic VADER sentiment and transparent descriptive counts.

Place two released answers, consideration maps, disagreements, token totals, and costs side by side without another model call.