A research agent that answers with evidence

From paper summariesto research design

Extract the core reasoning and evidence from the papers you care about. Rigorously test your ideas, discover new directions, and keep developing your research with an agent that retains the context you've built.

Extract the core reasoning from your papers and develop your research with an agent that retains your context.

DownloadFree

Until launch · Same limits as Pro

General-purpose AI isn't enough.Research needs a dedicated AI environment

01 · CONNECT

Keep using the AI models you already subscribe to

Bring the models and workflows you know—GPT, Claude, Gemini, and more—into Piuda. Then add research-specific capabilities such as evidence-linked papers, research design, and context that accumulates within each project.

Ask anything about your research
Send with Enter
02 · DESIGN

Built-in pipelines for research

Move step by step from paper summaries and analysis to new idea suggestions and validating your own ideas, using pipelines designed around how research actually gets done.

  1. Paper summary/analysisStructuring core logic and evidence
    01
  2. Idea recommendationDiscovering research gaps through cross-analysis
    02
  3. Idea verificationScoring evidence/verification predictions
    03
  4. Paper recommendationsRecommend papers needed to reinforce my ideas
    04
03 · BUILD

Turn research materials into a project structure

Piuda automatically archives the sources, project documents, and data you use with the agent by study. Structure scattered materials in one place and retrieve them when they matter.

Research project · Archive
Enterprise UI Research
Related papers3
Adaptive UI in EnterprisePDF
Human–AI CollaborationPDF
Data files5
survey_responsesCSV
Interview data2
interview_P01DOCX
Experimental data4
trial_metricsXLSX
Piuda pipeline

From scattered papers
to one research design

Add your papers and Piuda structures their research questions, variables, and methods. It cross-analyzes the gaps between them, so you can carry the context from idea validation through research design in one workspace.

Deep analysis display language

Research summary

Early conversational agents (CAs) focused on dyadic human–AI interaction between a person and a CA. Multiparty human–AI interaction, in which a CA mediates interaction among people, has since become more widespread. Research on multiparty CAs is scattered across fields, making existing knowledge difficult to identify, compare, and build upon. This study reviewed ACM publications to identify UX research and synthesized the effects of multiparty CAs across four dimensions: communication, engagement, connection, and relationship maintenance.

Study Claims

21 claims

Claims made directly in the paper are grouped by type. Open an item to inspect its evidence and source context.

Empirical

Claims supported by observational and statistical data

Theory

Claims regarding conceptual relationships and explanatory framework

YOUR LIBRARY · PAPER PROFILE
01 · Your Library

Add a paper and Piuda structures it automatically

Piuda reads and structures each PDF's research questions, theoretical background, independent and dependent variables, methods, and key findings. The analysis results are shown in the language selected by the user.

02 · Discovery

Cross-analysis surfaces research insights

Piuda finds meaningful combinations and research gaps across your papers, then scores and recommends research insights worth testing, together with their evidence and testable predictions.

03 · Validate & Enrich

Validate and develop ideas based on evidence

Cross-paper evidence is used to assess distinctiveness, strength of evidence, and feasibility—and to suggest concrete ways to sharpen the idea.

Talk with your agent

Lay out your validation results,
then discuss them with the AI you already use

Piuda does not write the paper for you. Keep using GPT or Claude, now with an agent that remembers the full pipeline output and helps you move the research forward.

01Idea analysis02Cross-analysis03Idea verification04Piuda suggestions

03. Idea verification

Referenced in conversation
3.1

Overall verdict

This idea is clearly differentiated within the current reference set, but the mechanism explaining why UX gains conflict with enterprise governance costs still rests largely on indirect evidence. The first priority is to define measurable operational indicators for how introducing an adaptive layer affects consistency, security, and review quality.

Referenced in conversation
3.2

Has this already been studied?

HighCan be strengthened with prior research · 29 papers found externally

The uploaded papers separately address adaptive UI's effects on conversion and engagement, enterprise security and legal constraints, and code-quality metrics. However, they do not extend to a study comparing a static design-to-code pipeline with an adaptive UI generation layer across multiple outcomes in an enterprise setting.

Closest existing researchEnhancing Enterprise Usability: Integrating Adaptive UI and Inclusive Design Strategies · 2025

Referenced in conversation
3.3

Is it theoretically supported?

MediumIndirect evidence

A plausible link exists: while adaptive UI improves contextual fit, data collection, runtime changes, and variation in generated code increase the burden of consistency reviews and security verification. The uploaded evidence for this trade-off remains indirect, however.

Task–technology fitUser-centered designSecurity-by-DesignCompliance-as-CodeModel-Driven Development

New evidence · Pilot testJust addedab_test_results.csv · The sprint that introduced the adaptive layer showed both longer review times and a lower defect-detection rate—direct evidence for the trade-off pathway.

Referenced in conversation
3.4

Can it actually be carried out?

MediumConditionally feasible
Data access

User satisfaction, engagement, and conversion rates can be collected through product logs and surveys. Access approval and anonymization are required for real repository and security-compliance data.

Research-design fit

A quasi-experiment can compare outcomes before and after introducing the adaptive layer, or exploit staggered adoption across teams or products.

Research-type fit

In enterprise contexts where random assignment is difficult, quasi-experiments using phased introduction or before-and-after comparisons are realistic options.

Data Privacy

Research data belongs to the researcher

Your research data is stored locally on your own computer by default. Original files and analysis results stay on the researcher's device.

Use Cases

How do you use it?

piuda can be utilized in various research contexts.

Explore New Research Topics

When you've read papers but aren't sure what to research. Cross-analysis automatically explores unexplored gaps and suggests promising research directions.

Application Areas
Thesis Topic ExplorationNew Research ProjectsLiterature-Based Discovery

Validate My Ideas

When you have a research idea but want to check how differentiated it is from existing research and whether evidence is sufficient. Evaluates differentiation, evidence strength, and feasibility.

Application Areas
Proposal ValidationGrant Pre-CheckAdvisor Meeting Prep

Systematic Literature Analysis

Connect the papers you care about and the agent structures their variables, methodology, and results, cross-analyzing how they relate and where the gaps are — so you see the prior-work landscape at a glance.

Application Areas
Systematic ReviewVariable ComparisonResearch Trends

Proposal Review & Refinement

When developing validated ideas into proposals, piuda identifies methodological gaps and logical leaps. Apply comments to progressively strengthen your draft.

Application Areas
Master's/PhD ProposalsNRF GrantsResearch Reports
Pricing

Every feature, with usage that fits your research

Start every core capability for free—from agent conversations and paper analysis to insight discovery and research design. Pro does not lock features away; it is for researchers who need more usage and a wider choice of AI models.

Free

Start using every research feature at no cost

₩0/ mo
  • Agent conversations and projects
  • Paper analysis and insight generation
  • Research design and draft analysis
  • Core AI models and included usage
Download

Business

A tailored research environment for teams and institutions

Contact us
  • Every feature in Free and Pro
  • Team and institution licensing
  • Usage and model mix tailored to your organization
  • SSO and implementation support available by arrangement
Inquire about adoption
FAQ

Have questions?

Does piuda write papers for me?

No. piuda is not a tool that writes papers for you — it's a workspace for designing and validating research. It deep-analyzes papers to structure their claims, evidence, and research gaps, then recommends and validates research ideas through cross-analysis. The final judgment and writing stay with the researcher.

What happens when I upload a paper?

When you attach a PDF or DOI, piuda extracts the text and metadata while preserving the original untouched, then structures (deep-analyzes) the research purpose, methods, data, claims, evidence, and research gaps. From this you can create a project and carry the work forward.

How does idea recommendation (Discovery) work?

It cross-analyzes the papers gathered in a project to find research gaps and recommends combinations of testable research ideas. Each idea comes with its evidence and predictions, and you can go on to get recommendations for the papers needed to reinforce it.

Which AI models does it use?

During a conversation you can pick from the latest models — Claude, GPT, Gemini, and more. All analysis runs only through ZDR (Zero Data Retention) contracted endpoints, so your data is never used for training.

Where is my research data stored?

Your research data is stored locally on your own computer by default. On paid plans you can turn on cloud sync to continue across devices, and sensitive materials can be designated as Local Vault to exclude them from sync.

Which research fields are supported?

It isn't tied to any single field. piuda deep-analyzes papers across HCI, social sciences, computer science, and more, and paper search queries five sources in parallel — OpenAlex, Semantic Scholar, CrossRef, PubMed, and DBpia.

Can it be adopted at a university or lab level?

Yes. The Business plan supports team and institutional adoption. Get in touch and we'll help with seats sized to your needs and custom onboarding.

Early Access

Focus on reading
and thinking deeply

Organizing, comparing, and validating — piuda handles that.
Spend your time thinking more deeply.