Systematic Review Automation
PRISMA 2020 Pipeline with I-Category Agents
Conversation-driven systematic literature review automation with AI-assisted screening, automated PDF retrieval, and RAG-powered analysis.
What is Systematic Review Automation?
Diverga's I-category agents provide a 7-stage automated systematic literature review pipeline following PRISMA 2020 guidelines. They combine conversation-driven workflow with AI-assisted screening and RAG technology.
PRISMA 2020 compliant workflow
AI-assisted screening with Groq LLM
Automated PDF retrieval (5 databases)
RAG-powered literature analysis
7-Stage Pipeline
Each stage builds on the previous, ensuring systematic and reproducible research.
Research Domain Setup
15-20 minDefine research question, scope, and constraints
Query Strategy Design
20-30 minDesign search queries with keywords and operators
PRISMA Configuration
15-25 minSet inclusion/exclusion criteria and thresholds
Database Search
10-20 minFetch papers from Semantic Scholar, OpenAlex, arXiv
Screening & Selection
30-60 minAI-assisted relevance screening with configurable LLM
RAG System Building
20-40 minCreate vector database for semantic search
Analysis & Synthesis
OngoingQuery literature and generate PRISMA diagram
Two Project Types
Choose the workflow that matches your research scope:
Knowledge Repository
Broad exploration, topic discovery, RAG-first workflow
Systematic Review
Rigorous PRISMA 2020 compliance, publication-ready
Project Structure
Diverga creates a dual-directory structure separating system files from researcher-facing documentation:
Created by natural language project init or /diverga:setup
.research/ # System files (hidden) ├── baselines/ │ ├── literature/ │ ├── methodology/ │ └── framework/ ├── changes/ │ ├── current/ │ └── archive/ ├── sessions/ ├── project-state.yaml # Research configuration ├── decision-log.yaml # Checkpoint decisions ├── checkpoints.yaml # Checkpoint states └── hud-state.json # HUD display state docs/ # Researcher-facing (auto-generated) ├── PROJECT_STATUS.md # Progress tracking ├── DECISION_LOG.md # Decision audit trail ├── RESEARCH_AUDIT.md # IRB/reproducibility audit ├── METHODOLOGY.md # Research design summary ├── TIMELINE.md # Milestones and deadlines ├── REFERENCES.md # Bibliography tracking └── README.md # Project overview (editable)
Additional structure created when running systematic review pipeline
data/ ├── raw/ # Downloaded PDFs │ ├── semantic_scholar/ │ ├── openalex/ │ └── arxiv/ ├── processed/ │ ├── deduplicated.json # After deduplication │ ├── screened.json # After AI screening │ └── included.json # Final included papers ├── vectordb/ # ChromaDB vector database │ └── chroma/ ├── reports/ │ ├── prisma_flow.png # PRISMA 2020 diagram │ └── screening_report.md # Screening statistics └── config.yaml # Pipeline configuration
Supported Databases
Three databases chosen for API access and PDF availability:
Semantic Scholar
~40% open access
OpenAlex
~50% open access
arXiv
100% PDF access
Cost Efficiency
Minimize API costs while maintaining quality:
500-paper review
~$0.07
Screening time
30-60 min
PDF retrieval
50-60%
- Groq LLM (default): $0.01 per 100 papers
- Local embeddings: Zero cost (sentence-transformers)
- No institutional subscriptions required
Key Features
AI-Assisted Screening
Groq LLM (llama-3.3-70b) for relevance scoring with configurable thresholds
Conversation-Driven
Stage-by-stage prompts guide researchers through PRISMA workflow
Automated PDF Retrieval
Retry logic, fallback chains, and progress tracking for 50-60% success rate
RAG-Powered Analysis
ChromaDB vector database for semantic search and synthesis
PRISMA Diagram
Auto-generate PRISMA 2020 flow diagram with exclusion tracking
Quality Validation
Checkpoint integration ensures reproducibility and transparency
Learn More
Explore detailed documentation for each component:
Ready to Automate Your Systematic Review?
Start with Diverga's I-category agents to conduct PRISMA 2020 compliant systematic reviews in hours, not weeks.