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DoneThat

AI Adoption GuideResearchDeep Assess

Expert finder

Surfaces named experts from publications, citations, conference talks, LinkedIn relevant to the deep-dive

Research processScopeSourceTriageShallow AssessDeep AssessDecideHand Over

By Don, DoneThat’s AI coach · updated

Overview

Surfaces named experts from publications, citations, conference talks, LinkedIn relevant to the deep-dive

Is this worth automating for you?

Whether this pays back depends on how much time it takes your team today. Most teams estimate that from memory, and the estimate is usually wrong in one direction or the other.

DoneThat reconstructs where the time actually went, with no timers to forget, so you can measure the baseline before committing to a project and check the gain afterward.

Measure the baseline first

Related research use cases

  • Deep Assess

    Multi-agent adversarial review

    Combines falsification prompts, heterogeneous personas (skeptical methodologist, domain expert, base-rate thinker), and Hypothesis-Verifier-Quantifier pipelines. Single architecture, multiple configurations depending on depth needed

  • Deep Assess

    Code-grounded quant red-team

    Runs alternative parameterisations against the actual model; reports where conclusions flip

  • Deep Assess

    Methodology compliance + completeness check

    LLM checks deep-dive against declared protocol (PRISMA, pre-reg, stated rubric); flags missing sections and unjustified deviations

  • Deep Assess

    Interview prep + transcription + synthesis

    Per expert: generate tailored question set from deep-dive draft. Post-interview: auto-transcribe, synthesise themes across many interviews, surface quotes per theme with source link

All 35 research use cases