AI Automation for Research Teams

AI Automation for Research Teams

AI automation can help research teams reduce repetitive searching, screening, extraction, documentation, and reporting work.

It can support academic research, evidence synthesis, market research, policy research, scientific review, and internal research operations.

A practical research workflow may look like:

Research Question
→ Define Search and Inclusion Rules
→ Collect Sources
→ Screen and Extract Evidence
→ Prepare a Synthesis
→ Researcher Review

AI handles variable language, document interpretation, classification, comparison, and first-draft preparation.

Deterministic workflow steps should handle identifiers, dates, inclusion rules, duplicate detection, calculations, data transformations, and record keeping.

Researchers remain responsible for the research question, methodology, source selection, interpretation, statistical analysis, citations, authorship, and publication decisions.

The safest starting point is a narrow workflow that prepares reviewable evidence without deciding study inclusion, fabricating citations, or writing conclusions independently.

Where AI automation fits in research

AI is useful when research work contains repeated reading, classification, extraction, comparison, or documentation.

Suitable examples include:

• research-request intake;
• question and protocol preparation;
• literature-search assistance;
• title and abstract screening support;
• full-text extraction;
• evidence-table preparation;
• study comparison;
• qualitative coding drafts;
• research-note summaries;
• data-analysis explanations;
• citation checking;
• manuscript-outline preparation;
• peer-review assistance; and
• recurring literature monitoring.

Some tasks should remain under direct researcher authority.

These include:

• defining the final methodology;
• deciding study inclusion;
• assessing risk of bias;
• interpreting ambiguous evidence;
• selecting statistical methods;
• drawing final conclusions;
• approving quotations and citations;
• determining authorship;
• submitting a manuscript; and
• making claims that affect policy, medicine, safety, or public understanding.

AI can organise evidence and propose language.

It should not become the final authority for consequential research decisions.

Begin with one repeated task whose output can be checked against the source, such as metadata extraction, evidence-table preparation, or a recurring research digest.

Research questions, protocols, and search preparation

AI can help turn a broad topic into a structured research brief.

A workflow may extract or prepare:

• research objective;
• population or subject;
• intervention or exposure;
• comparator;
• outcomes;
• context;
• date range;
• jurisdictions;
• study designs;
• inclusion criteria;
• exclusion criteria;
• key concepts;
• synonyms; and
• unresolved questions.

The result should remain a draft.

AI may omit an important concept, broaden the question too far, or introduce a criterion that changes the intended study.

Researchers should approve the question, protocol, databases, search fields, filters, and stopping rules.

Search strings should be tested against known relevant sources where possible.

Protocol changes should be documented with dates, reasons, and responsible reviewers.

A fluent protocol is not evidence that the method is valid.

Literature discovery and source collection

AI can help researchers identify search terms, organise query variants, and prepare source lists.

A controlled workflow may:

1. receive the approved research question;
2. generate candidate search concepts;
3. search approved databases or sources;
4. collect source metadata;
5. preserve identifiers and links;
6. remove exact duplicates;
7. record retrieval dates; and
8. return the result for researcher review.

Search coverage depends on database selection, indexing, language, date limits, access, and query design.

General web search should not replace appropriate scholarly, legal, technical, or specialist databases when the research question requires them.

AI-generated references may not exist.

Verify titles, authors, publication dates, identifiers, and source availability before a reference enters the research record.

Preserve failed searches and no-result queries because they are part of the method.

Screening and study-selection support

AI can help screen titles, abstracts, or full texts against approved criteria.

A screening workflow may return:

• source identifier;
• likely include;
• likely exclude;
• unclear;
• criterion matched;
• exclusion reason proposed;
• relevant excerpt; and
• reviewer note.

Use a fixed list of exclusion reasons.

Include an Unclear route so the model does not force every record into a binary decision.

AI can reduce repetitive review effort, especially when eligibility criteria are explicit.

It can also miss unusual terminology, misunderstand study design, or invent evidence absent from the source.

A researcher should review uncertain cases and retain responsibility for the final inclusion decision.

For higher-stakes evidence synthesis, use independent review, discrepancy resolution, and documented reasons for exclusion.

Data extraction and evidence tables

AI can extract defined fields from papers, reports, interviews, or technical documents.

A research evidence table may include:

• citation;
• source identifier;
• research question;
• study design;
• population or sample;
• setting;
• methods;
• intervention or exposure;
• comparator;
• outcomes;
• findings;
• limitations;
• funding;
• conflicts stated;
• source location; and
• missing information.

Use Not reported when a field is absent.

Do not let the model infer sample size, methods, effect direction, or limitations from general context.

Preserve page, section, table, or figure references where possible.

Structured output makes comparison easier, but a valid schema does not prove extraction accuracy.

Researchers should verify important fields against the full source and reconcile disagreements.

Evidence synthesis and qualitative analysis

AI can help organise extracted evidence into themes, comparisons, and draft summaries.

A synthesis workflow may:

• group studies by design or topic;
• compare findings;
• identify agreement and disagreement;
• summarise methods;
• organise limitations;
• identify evidence gaps;
• prepare a chronology;
• draft thematic codes; and
• list questions requiring expert interpretation.

Separate source findings from AI-generated synthesis.

A repeated finding does not automatically establish causality, quality, or generalisability.

AI may flatten important differences between populations, measures, settings, and methods.

Researchers should assess heterogeneity, risk of bias, study quality, applicability, and the weight given to each source.

For qualitative work, coding decisions, reflexivity, context, and deviant cases should remain visible.

Data analysis and research computation

AI can assist with analysis planning, code drafts, data dictionaries, diagnostic questions, and explanations of approved results.

It may prepare:

• analysis-plan drafts;
• variable descriptions;
• code scaffolding;
• data-cleaning questions;
• test ideas;
• visualisation suggestions;
• model-assumption checklists;
• interpretation prompts; and
• reproducibility notes.

Authoritative calculations should come from deterministic statistical or analytical tools.

Generated code should be reviewed and tested.

AI may use the wrong statistical method, leak information between training and test data, mishandle missing values, ignore clustering, or overstate significance.

Researchers should verify data provenance, transformations, assumptions, diagnostics, uncertainty, sensitivity analyses, and reproducibility.

Keep observed results separate from AI-generated explanations.

Writing, citations, and publication preparation

AI can prepare outlines and drafts from approved notes and verified evidence.

Suitable outputs include:

• literature-review outlines;
• method-section drafts;
• evidence summaries;
• table descriptions;
• plain-language summaries;
• presentation notes;
• reviewer-response drafts; and
• internal research briefs.

A focused workflow should define the audience, purpose, source set, required structure, citation format, and prohibited claims.

AI should not invent references, quotations, results, methods, or author contributions.

Verify every citation and quotation against the original source.

Researchers remain responsible for originality, accurate attribution, disclosure of AI use where required, authorship, conflicts, and final submission.

Publication volume is not a substitute for research quality.

The workflow should support careful scholarship rather than accelerate unsupported output.

Peer review, quality control, and reproducibility

AI can help prepare a checklist-based review of a manuscript, protocol, report, or analysis package.

A review workflow may flag:

• missing sections;
• unsupported claims;
• inconsistent terminology;
• unexplained exclusions;
• citation gaps;
• unclear methods;
• missing limitations;
• figure-label problems;
• data and code availability gaps; and
• differences between registered and reported methods.

AI-generated critique can sound convincing while missing methodological problems.

It should not replace subject-matter peer review.

Preserve:

• source documents;
• search records;
• screening decisions;
• extraction versions;
• analysis code;
• model and prompt versions;
• corrections;
• reviewer decisions; and
• final outputs.

Reproducibility requires more than keeping the final text.

The complete path from source to result should remain understandable.

Privacy, ethics, and sensitive research data

Research workflows may process participant data, interviews, unpublished findings, intellectual property, confidential documents, health information, or commercially sensitive material.

Before using automation, identify:

• which model receives the data;
• whether processing is local or cloud-based;
• which tools receive information;
• where outputs and activity records are stored;
• who can access them;
• which consent or approval applies;
• which external destinations are reachable; and
• how long information is retained.

Apply data minimisation, de-identification where appropriate, role-based access, and least privilege.

Research ethics, consent, data-use agreements, institutional policy, and applicable law may limit how AI can be used.

Treat documents, websites, and tool responses as untrusted content because they may contain instructions aimed at the model.

A local model can keep its model step on the computer, but the complete workflow is only local when every source, tool, storage location, and destination also remains local.

Build a research workflow in Feluda

Feluda is a desktop application for building and running visual AI workflows.

Begin in Workbench with public, synthetic, or appropriately redacted research material.

For example:

Read the research paper.

Return:
1. citation details stated;
2. research question;
3. study design;
4. population or sample;
5. methods;
6. main findings;
7. limitations stated;
8. source section for each field; and
9. missing information.

Use only the source.
Write "Not reported" when a value is absent.
Do not invent citations, methods, results, or limitations.

Compare every extracted field with the original paper.

Once the task is dependable, build the process in Studio.

A practical flow may use:

Research Source
→ LLM Label Study Type
→ LLM Extract Evidence
→ Expression Validate Required Fields
→ LLM Prepare Evidence Summary
→ Output for Researcher Review

Use LLM Label for approved study or topic categories, LLM Extract for named fields, LLM for summaries and drafts, Expression for exact rules and routing, Emit for selected intermediate output, and Output for review, clarification, partial, success, or error states.

Feluda models, tools, permissions, and testing

Feluda can connect to supported cloud providers and compatible local model applications such as Ollama and LM Studio.

A local model may suit unpublished manuscripts, confidential interviews, or repeated private extraction when it performs reliably.

A cloud model may support longer inputs, images, or more demanding analysis.

Compare models using the same approved examples and review extraction accuracy, groundedness, citation quality, privacy, speed, context length, cost, tool support, and hardware requirements.

Genes can add tools, prompts, flows, and resources.

MCP connections can expose additional approved tools.

Before enabling a research tool, check what sources and files it can read, what it can create or change, which credentials it uses, whether it connects externally, whether its action is reversible, and how completion is confirmed.

Store private values in Secrets.

Use flow permissions to control allowed or denied URLs, IP addresses, file paths, and ports.

Apply least privilege and separate discovery, extraction, analysis, writing, and publication actions.

Use RunFlows with normal, incomplete, conflicting, confidential, adversarial, and failing cases.

Confirm that the workflow preserves source evidence, avoids invented citations or findings, exposes missing information, displays failures, and prevents uncontrolled external actions.

Scheduling and measurement

Feluda's Schedule Manager supports once, daily, weekdays, weekly, and monthly schedules in paid plans.

Suitable scheduled workflows may include:

• a weekly literature digest;
• a recurring database search;
• a monthly evidence-gap report;
• a source-update monitor;
• a research-project status brief; or
• a scheduled extraction review.

Scheduling runs on the desktop, so Feluda and required local services must be available.

Schedule only after dependable manual runs.

Preserve researcher review, prevent duplicate records, monitor run history and conflict warnings, and assign an owner.

Useful success measures include retrieval precision, screening agreement, extraction accuracy, citation accuracy, review time, correction rate, evidence-table completeness, synthesis acceptance, tool failure rate, review burden, cost per approved result, and high-impact error rate.

Do not measure success only by papers processed, summaries generated, or words written.

An efficient workflow is not successful when it weakens validity, transparency, reproducibility, or research integrity.

Common research-automation mistakes

Avoid:

• treating AI-generated citations as verified;
• letting AI decide final study inclusion;
• extracting findings without source references;
• hiding Unclear or Not reported cases;
• summarising only abstracts when full text is required;
• treating repeated findings as proof of quality;
• using generated code without statistical review;
• writing conclusions broader than the evidence;
• sending sensitive data to unsuitable providers or tools;
• giving broad file or publication permissions;
• measuring output volume instead of research quality; and
• scaling before methodology, review, and provenance are clear.

Start with one reviewable workflow.

Define the question, source set, output, exact controls, ethical limits, review process, and owner.

Keep study selection, methodological judgement, analysis, interpretation, citations, authorship, and publication under qualified researcher control.

AI automation is most useful for research teams when it reduces repetitive processing while strengthening evidence organisation, transparency, and reproducibility.