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R&D: AI Automation for Literature Reviews, Experiment Design, and Patent Research

Research teams spend more time on paperwork than on research. Here's how the R&D starter pack automates literature reviews, proposal writing, and experiment documentation so scientists can focus on discovery.

JT
JieGou Team
· · 5 min read

Researchers have a paperwork problem. For every hour of actual research — designing experiments, analyzing data, drawing conclusions — there are multiple hours of supporting documentation. Literature reviews that take days to compile. Grant applications with rigid formatting requirements. Lab notebooks that fall behind because writing up results is less urgent than running the next experiment.

The R&D starter pack provides 10 AI recipes and 4 workflows that handle the documentation burden of research. The AI does the drafting and formatting; you provide the scientific judgment.

What’s in the starter pack

10 Recipes

These cover the documents researchers produce most frequently:

  • Literature Review Summary — Generates a structured literature review from a research topic, synthesizing key findings, methodological approaches, gaps, and future directions
  • Research Proposal Writer — Produces a complete research proposal with background, objectives, methodology, expected outcomes, and timeline
  • Experiment Design Doc — Creates a detailed experiment design document with hypothesis, variables, controls, sample size justification, and statistical analysis plan
  • Lab Notebook Entry — Generates a formatted lab notebook entry from experiment notes, including procedure, observations, results, and next steps
  • Patent Prior Art Search — Produces a structured prior art analysis from an invention description, identifying relevant existing patents and differentiating features
  • Technical Paper Abstract — Drafts a publication-ready abstract from research findings, following the structured abstract format (background, methods, results, conclusions)
  • Research Grant Application — Generates a grant application narrative from research context, formatted for common funding body requirements
  • Peer Review Feedback — Produces structured peer review feedback from a manuscript, covering methodology, results, writing quality, and actionable suggestions
  • Data Analysis Narrative — Creates a written narrative from statistical results, translating numbers into clear, publication-ready prose
  • Innovation Brief — Drafts an innovation brief connecting research findings to potential commercial applications and strategic opportunities

4 Workflows

The workflows chain recipes together into end-to-end research processes:

  1. Research Pipeline — Literature review → research proposal → experiment design → grant application. Takes a research question and produces everything needed to start a funded research project.
  2. Experiment to Publication — Lab notebook entry → data analysis narrative → technical paper abstract → peer review preparation. Streamlines the path from experiment results to publication-ready materials.
  3. Patent Filing Workflow — Prior art search → invention description → patent claims drafting → filing checklist. Guides researchers through the patent documentation process with structured outputs at each stage.
  4. Innovation Sprint — Innovation brief → feasibility assessment → prototype spec → stakeholder pitch. Converts research discoveries into business-ready proposals.

Example: Research Pipeline workflow

Here’s how the flagship workflow takes a research question from concept to funded project.

Input: Research question, target domain, relevant background, and funding body (e.g., NSF, NIH, internal R&D budget).

Step 1: Literature Review. The Literature Review Summary recipe scans your research topic and produces a structured synthesis. Not just a list of papers — a narrative that identifies the key findings in the field, the methodological trends, the open questions, and where your research question fits into the existing landscape.

Step 2: Research Proposal. Using the literature review as context, the Research Proposal Writer generates a complete proposal. It includes a problem statement grounded in the literature gaps identified in Step 1, clear objectives, a methodology section, expected outcomes, and a realistic timeline.

Step 3: Experiment Design. The Experiment Design Doc recipe takes the proposed methodology and produces a detailed experimental plan. Hypothesis formalized, variables defined, controls specified, sample size justified with power analysis, and statistical methods pre-registered.

Step 4: Grant Application. Finally, the Research Grant Application recipe packages everything into the required format for your target funding body. The narrative draws from the literature review, proposal, and experiment design — ensuring consistency across the entire application.

Result: What used to take a research team 3-4 weeks of writing and revision is drafted in an afternoon. The researchers review, refine with their domain expertise, and submit.

Integrations and scheduling

The R&D pack connects to the tools researchers already use:

  • Google Scholar — Pull reference metadata and citation context into literature review recipes
  • Notion — Export research proposals, experiment designs, and lab notebooks to your team knowledge base
  • Google Docs — Push generated documents directly into collaborative editing for team review
  • Slack — Post pipeline updates and experiment results to your research team channel

Two built-in schedules keep research operations running smoothly:

  • Weekly Research Pipeline — Triggers a literature review update on your active research topics every week, so you never miss a relevant new publication
  • Monthly Innovation Sprint — Runs the innovation sprint workflow monthly, surfacing new commercial opportunities from recent research findings

Real results

“Grant writing used to be our biggest bottleneck — researchers would rather run experiments than write proposals. Now the AI drafts the narrative from our research notes, and we spend our time on the science instead of the formatting. Our submission rate doubled last quarter.”

— R&D Director, biotech startup

Teams using the R&D pack typically see:

  • 75% reduction in literature review preparation time
  • 2x more grant submissions per quarter — the writing bottleneck is removed
  • Consistent documentation — lab notebooks and experiment designs follow the same format every time
  • Faster time-to-publication — from experiment results to draft abstract in hours, not weeks

Get started

Install the R&D starter pack from the department page. It sets up all 10 recipes and 4 workflows, configures the weekly and monthly schedules, and walks you through connecting Google Scholar, Notion, Google Docs, and Slack. Your first literature review summary is one run away.

research-development automation workflows recipes
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