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LearningNemo

First application

Deep Medical Research report generation

The first product composes the platform's modules into an end-to-end research pipeline, coordinated by the CEO and executed by a panel of specialist agents — intake → plan → gather → analyze → compose → gate → render.

Proof of concept complete

The pipeline is generating full reports today

The Deep Medical Research Report Generation proof of concept is complete. The swarm is producing full, detailed medical reports end-to-end — 40–70 page Deep Insight, hypothesis-generating reports — coordinated by the CEO and a panel of specialist agents.

40–70
pages per report
Deep Insight
hypothesis-generating

The pipeline, step by step

  1. 1

    Intake & classification

    CEO (M004)

    The request is classified (direct response, tool-first, or research handoff) and a structured research task is built — requested sections, chart and citation floors, quality thresholds.

  2. 2

    Planning

    Research planner

    A planner agent decomposes the work and produces an evidence-search contract.

  3. 3

    Evidence gathering ("BigBrain")

    Deep-research specialist

    A multi-round, tool-using retrieval loop over biomedical sources produces structured, cited claims.

  4. 4

    Analysis

    Domain analyst panel

    Analysts critique each claim with structured verdicts (supports / contradicts / methodological concern / insufficient evidence), confidence, and evidence strength. A rubber-stamp detector guards against shallow agreement.

  5. 5

    Composition

    Report composer

    The composer renders the deliverable from claims, analyst critiques, and the weighted memory graph — validated against a strict schema.

  6. 6

    Quality gating

    Deterministic evaluator

    Block- and warn-severity rules are enforced: missing sections, thin sections, minimum chart and citation counts, citation completeness, and more.

  7. 7

    Charts & rendering

    Chart planner + renderers

    A value-ranked chart planner selects visualizations anchored to retrievable identifiers; Markdown, HTML, and PDF renderers produce the final outputs.

Domain-flexible by design

Built for medicine, designed to travel

The same intake → plan → gather → analyze → compose → gate → render structure is deliberately domain-flexible — intended to be re-pointed at other research domains without re-architecting.

Learning Nemo is a research-support platform. Its medical research application produces evidence-cited research reports for review by qualified professionals and does not constitute medical advice, diagnosis, or treatment.