AI for research, literature review, and analysis
For researchers, analysts, and consultants working through large document corpora, mixed data sources, and multi-stage synthesis tasks that exceed a single model's context window.
What this looks like in practice
Literature review across corpora
Ingest a folder of papers, reports, or filings into a retrieval index. An agent runs structured passes over each document for methodology, findings, and limitations, then synthesizes a comparison matrix. Use long-context models (e.g. Claude Opus) for cross-document reasoning, with citations linked to source paragraphs.
Multi-source data synthesis
Combine PDFs, transcripts, CSV exports, and web sources into one workflow. Function calling produces structured output, JSON tables, ranked lists, extracted entities, that downstream tools or notebooks consume. Switch models per stage: a reasoning model plans the analysis, a long-context model reads, a smaller model formats.
Autonomous investigation agents
Define a research question and let an agent decompose it into subtasks: search, retrieve, read, cross-check, summarize. The agent invokes tools through the plugin system, web fetches, internal databases, calculation steps, and returns a report with reasoning traces and source links you can audit.
Why yno.ai for Research & Analysis
- Long-context document ingestion with chunking, embeddings, and retrieval over your private corpus
- Per-stage model routing so reasoning, reading, and formatting each use the appropriate model
- Function calling for structured output: tables, citations, extracted fields, ranked findings
Recommended models
Key concepts
No credit card required
Frequently asked
- Which AI model is best for long-document analysis?
- Long-context models like Claude Opus and Gemini 2.5 Pro handle hundreds of pages in a single prompt, which suits cross-document synthesis. For step-by-step reasoning over extracted evidence, GPT-5.5 Thinking or O3-class reasoning models often perform better. yno.ai lets you route each stage to the model that fits, rather than committing to one.
- How can AI help with literature review across hundreds of papers?
- Index the corpus with embeddings, then run an agent that reads each paper against a fixed schema, methodology, dataset, findings, limitations, and writes structured records. A second pass synthesizes across records into themes or comparison tables. Citations stay anchored to source paragraphs so you can verify any claim before quoting it.
- Can AI agents run autonomous research workflows end-to-end?
- Yes, within scoped boundaries. Agents handle decomposition, retrieval, reading, and drafting, calling tools through the plugin system for searches, database queries, or calculations. For consequential outputs you should keep a human review step. Reasoning traces and tool-call logs make it practical to audit what the agent did and why.
- How does yno.ai handle citations and source attribution?
- Retrieval returns source chunks with document IDs and offsets, which agents are prompted to cite inline. Structured output via function calling enforces a citation field on every claim, so synthesis steps preserve attribution rather than paraphrasing it away. You can render footnotes, link back to source PDFs, or export the citation graph.