From question to verified paperimplementation specproduction systemmarket launch

ApolloBot is the open-source autonomous research engine. It discovers, translates, implements, and commercializes — with full provenance at every step.

# Install and run your first session
$ pip install apollobot
$ apollo discover "your research question"
# Or run the full pipeline
$ apollo pipeline "your research question"

Read the Docs View on GitHub →

Complete

✓

Planning

Decomposed into 3 hypotheses, 4 data sources

6-phase research plan generated

✓

Literature Review

Synthesized 127 papers via PubMed + Semantic Scholar

23 directly relevant · 7 methodological

✓

Data Acquisition

Retrieved 3 methylation datasets from GEO

GSE198234 · GSE201456 · GSE187901 · checksums verified

✓

Analysis

847 differentially methylated regions identified

FDR < 0.05 · |Δβ| > 0.1 · 14 scripts executed

✓

Statistical Testing

H1: supported (d=0.72) · H2: supported (d=0.54) · H3: not supported

Bonferroni corrected · effect sizes with 95% CIs

✓

Manuscript & Self-Review

Draft complete — 0 critical, 2 minor issues

Translation potential: 8.4/10 → candidate flagged

Output

manuscript.pdfprovenance/replication_kit/figures/data/

↓ Translation candidate detected — apollo translate →

✓

Commercial Assessment

Epigenetic biomarker panel for environmental toxicology

Market: $2.4B environmental diagnostics · Feasibility: 8.1/10

✓

Prior Art & IP Landscape

12 relevant patents identified, freedom-to-operate confirmed

3 white space opportunities · No blocking patents

✓

Implementation Specification

Methylation array-based diagnostic panel (47 CpG sites)

Target: Illumina EPIC platform · Validation: 200 samples required

✓

Feasibility Validation

In-silico validation: 94.1% sensitivity, 91.8% specificity

Estimated dev cost: $340K · Timeline: 14 months to LDT

✓

Translation Report

Complete spec: diagnostic panel, validation plan, regulatory pathway

FDA 510(k) pathway identified · 3 CRO partners recommended

Output

translation_report.pdfip_landscape.pdfimplementation_spec.yamlfeasibility/

↓ Spec approved at checkpoint — apollo implement →

✓

Scaffold

Project structure, dependencies, CI/CD pipeline configured

Nextflow workflow · R/Bioconductor environment · Docker container

✓

Build

Diagnostic pipeline: preprocessing → normalization → classification

47-CpG panel extraction · Random forest + logistic regression ensemble

✓

Test

Cross-validation AUC 0.943 on held-out data

28 unit tests · 4 integration tests · all passing

✓

Document

API docs, clinical user guide, validation protocol

LIMS integration guide · SOPs for lab technicians

✓

Package

Docker image, Nextflow pipeline, published to PyPI

Procurement list: 12 reagents · Equipment checklist generated

✓

Validate

Implementation traces back to all 847 DMRs from Discover

Provenance chain verified · Original findings faithfully represented

Output

pipeline/Dockerfiledocs/tests/validation_report.pdf

↓ Implementation validated — apollo commercialize →

✓

Market Analysis

Environmental diagnostics TAM: $2.4B, growing 12% CAGR

Target segment: aquaculture monitoring · 340 potential customers ID'd

✓

IP Strategy

Provisional patent recommended for 47-CpG panel composition

Draft application generated · Trade secret for ensemble model weights

✓

Go-to-Market

LDT launch via CLIA-certified partner labs → 510(k) in year 2

Pricing: $450/test · Break-even at 756 tests/month

Output

market_analysis.pdfip_strategy.pdfgo_to_market.pdfpatent_draft/

✦ Full provenance chain: question → finding → spec → system → market

The Apollo Pipeline

Four modes, one continuous provenance chain

Each mode feeds the next. Every step is logged. A production system traces all the way back to the original research question.

🔬

● Live

Discover

Research question → verified paper with full provenance, ready for Frontier Journal.

apollo discover→

🔄

◯ Coming Q3 2026

Translate

Verified finding → implementation specification with feasibility assessment and IP analysis.

apollo translate→

⚙️

◯ Roadmap

Implement

Technical spec → working code, tested pipelines, deployment-ready packages.

apollo implement→

💎

◯ Roadmap

Commercialize

Working system → market analysis, IP strategy, go-to-market plan.

apollo commercialize

Built for rigorous, reproducible research — and everything that comes after

ApolloBot maintains scientific rigor across all four modes. Every claim is sourced, every estimate has confidence intervals, every artifact links back to evidence.

📜

Continuous Provenance

Unbroken chain from research question to deployed system. Every decision logged across all modes.

🔌

20+ MCP Servers

PubMed, GEO, FRED, HuggingFace, Materials Project, patent databases, market data, and more.

🔄

One-Click Replication

Every Discover session produces a replication kit. Every Implement build includes a full test suite.

📊

Statistical Rigor

Anti-confirmation bias, automatic corrections, effect sizes with CIs, self-review, and statistical audit.

🛡️

Human Checkpoints

Mode transitions require human approval. Configurable: strict (every phase) or relaxed (mode boundaries).

🧩

Domain Packs

Pre-configured data sources, statistical frameworks, and output templates for each scientific domain.

Full provenance from question to production

Every ApolloBot session builds a continuous provenance chain. By the time a finding reaches commercialization, every claim traces back to evidence, every transformation is logged, and every decision is auditable.

✓ Publication-ready manuscript with figures
✓ Complete execution log with every decision
✓ Data lineage with SHA256 checksums
✓ IP landscape and freedom-to-operate analysis
✓ Implementation specification with feasibility scores
✓ Tested code and deployment packages
✓ Market analysis and go-to-market plan

session-001/
├── discover/
│   ├── manuscript.pdf # Journal-formatted paper
│   ├── provenance/ # Execution + data lineage
│   ├── replication_kit/ # One-command verify
│   └── figures/
├── translate/
│   ├── translation_report.pdf
│   ├── ip_landscape.pdf # Patents + white space
│   ├── implementation_spec.yaml
│   └── feasibility/
├── implement/
│   ├── pipeline/ # Production code
│   ├── tests/ # Full test suite
│   ├── docs/ # User + API docs
│   ├── Dockerfile
│   └── validation_report.pdf
└── commercialize/
    ├── market_analysis.pdf
    ├── ip_strategy.pdf
    └── go_to_market.pdf

From question to verified paperimplementation specproduction systemmarket launch

Planning

Literature Review

Data Acquisition

Analysis

Statistical Testing

Manuscript & Self-Review

Commercial Assessment

Prior Art & IP Landscape

Implementation Specification

Feasibility Validation

Translation Report

Scaffold

Build

Test

Document

Package

Validate

Market Analysis

IP Strategy

Go-to-Market

Four modes, one continuous provenance chain

Discover

Translate

Implement

Commercialize

Built for rigorous, reproducible research — and everything that comes after

Continuous Provenance

20+ MCP Servers

One-Click Replication

Statistical Rigor

Human Checkpoints

Domain Packs

Five domains, full pipeline

Bioinformatics

Comp. Physics

ML / CS

Comp. Chemistry

Quant. Economics

Full provenance from question to production

Published with ApolloBot

Epigenetic clock acceleration in marine organisms under microplastic exposure

Sparse attention reduces transformer inference cost 40% without accuracy loss

Ab initio prediction of high-Tc superconductivity in La₃BH₁₀

Built for Frontier Science