Automate grant proposal generation from research outlines and academic papers

A researcher stares at a blank grant proposal template. Two months to deadline. Fifty pages minimum. The literature review alone will consume three weeks, even though she's already read the papers. The methodology section requires rewriting the same explanations her last proposal contained. Nobody's building on her previous work; she's rebuilding it from scratch, manually copying citations and restructuring paragraphs to fit a new funder's requirements. This is where most researchers live. Grant writing consumes 15-20% of academic time, yet most of it involves tedious document assembly rather than intellectual work. The outline exists. The papers are written. The citations are there. What's missing is the bridge: a system that takes those raw materials and generates a structured proposal that actually reads like prose, not like it was assembled by a committee of bots. The workflow below solves this specific problem. You'll extract research content from PDFs, rephrase it contextually for grant language, then orchestrate the pieces into a formatted proposal document. The result is a first draft that's genuinely usable, not something requiring a full rewrite.

The Automated Workflow

This workflow runs on n8n because it needs conditional logic and file handling. Zapier would work but feels brittle for this; Make requires too much manual intervention between steps. n8n lets you build once and reuse across multiple researchers. The architecture has four stages: extraction, transformation, generation, and assembly. Stage 1: Extract content from research PDFs using Chat With PDF Researchers upload a thesis chapter, recent paper, or detailed outline to Chat With PDF by Copilot.us. You'll interact with it via webhook to pull structured summaries.

CopyPOST https://api.copilot.us/v1/pdf-chat
Content-Type: application/json { "file_id": "research_paper_2024.pdf", "query": "Summarise the methodology section in 150 words. Include all statistical approaches and sample sizes.", "context_window": 3000
}

The response returns structured text that n8n can parse and store in a variable.

Copyjson
{ "response": "This study employed a mixed-methods design combining quantitative analysis of 247 patient records with qualitative interviews from 18 clinicians...", "tokens_used": 847, "source_pages": [3, 4, 5]
}

Store this in n8n as extracted_methodology. Stage 2: Transform content with QuillBot for grant-appropriate language Raw research text often doesn't match grant funder voice. QuillBot's API accepts text and a "creativity" setting; at low values (2-3) it maintains meaning whilst adjusting tone for formal grant contexts.

CopyPOST https://api.quillbot.com/api/rephrase
Authorization: Bearer YOUR_QUILLBOT_API_KEY { "text": "{{ extracted_methodology }}", "creativity": 2, "mode": "formal", "length": "same"
}

Store the rephrased output as methodology_grant_ready.

Stage 3: Generate proposal sections using Claude

This is where Wordware becomes useful if your team includes domain experts who aren't engineers. But if you're building for a single research group, Claude Opus 4.6 in n8n via a simple HTTP request works faster. Set up a Wordware agent or use Claude directly with a structured prompt:

CopyPOST https://api.anthropic.com/v1/messages
Authorization: x-api-key YOUR_ANTHROPIC_KEY
Content-Type: application/json { "model": "claude-opus-4.6", "max_tokens": 2000, "messages": [ { "role": "user", "content": "You are a grant proposal writer. Using the following methodology section, write an 800-word 'Research Design' section suitable for an NIH R01 grant. Match the formal tone and structure of NIH proposals. Include subsections for Study Design, Sample, and Data Collection. Here is the source material: {{ methodology_grant_ready }}" } ]
}

Claude returns a formatted section. Store it as research_design_section. Stage 4: Repeat for other sections and assemble into a document Build parallel workflows for:

• Literature review (from Chat With PDF summaries of multiple papers)
• Specific aims (from your outline)
• Innovation statement (generated from key differentiators extracted from your papers)
• Preliminary data (from structured results summaries) Each returns a section. Then use a final n8n step to merge into a single document using a templating tool or simple text concatenation.

CopyPOST https://api.n8n.io/webhook/document-assembly { "title": "Grant Proposal: {{ project_title }}", "sections": { "specific_aims": "{{ specific_aims_section }}", "research_design": "{{ research_design_section }}", "literature_review": "{{ literature_review_section }}", "preliminary_data": "{{ preliminary_data_section }}" }, "output_format": "docx"
}

This generates a downloadable Word document with proper formatting, page breaks, and a table of contents if your tool supports it.

Orchestration in n8n

Set up a workflow trigger: upload a file to a monitored folder (or trigger via form submission). The workflow then: 1. Calls Chat With PDF webhook for content extraction 2. Branches into parallel n8n nodes: one for methodology, one for literature review, one for innovation 3. Each branch calls QuillBot, then Claude 4. A final "Merge" node collects all sections 5. Document assembly and email notification Set rate limits: QuillBot and Anthropic both have per-minute caps. Use n8n's "Wait" nodes to space out API calls by 500ms between QuillBot calls; Claude can handle higher concurrency.

Copy// Example n8n workflow structure (conceptual) START (File Upload) → Chat With PDF Extraction (get methodology + literature) → SPLIT: 3 parallel branches BRANCH 1: QuillBot (methodology) → Claude (expand to 800 words) BRANCH 2: QuillBot (literature) → Claude (structure into sections) BRANCH 3: Chat With PDF (specific aims) → Claude (formalize) → MERGE: Combine all outputs → Document Assembly → Email to researcher END

The Manual Alternative

If your grant requires heavily customised sections or funder-specific language that templates can't handle, skip Claude's generation stage. Instead, use the workflow purely for extraction and rephrasing: Chat With PDF extracts, QuillBot tones it, and you paste the results into your draft manually. This removes the repetitive parts (reading papers, initial paraphrasing) whilst preserving your ability to customise tone and emphasis. This hybrid approach takes perhaps 30% of the time a full manual draft does.

Pro Tips

Rate limiting and costs:

Claude Opus 4.6 costs roughly 2-3p per 1000 tokens.

A full five-section proposal generates around 80,000-100,000 tokens total. Budget 2-3 pounds per proposal. QuillBot's API charges per request, not tokens, so set rate limits first to avoid surprise billing.

Handling multi-document extraction:

Chat With PDF can process multiple files simultaneously, but it performs best with 5-10 PDFs per batch. Split larger literature reviews across multiple API calls using n8n's loop node.

Cold starts and regeneration:

If Claude's output requires rework, store the original extracted text so you can regenerate without re-querying Chat With PDF. This cuts costs in half for iteration cycles.

Template matching:

Different funders (NIH, NSF, Leverhulme, RCUK) expect different section structures. Create separate n8n workflows per funder type, each with its own Claude prompt. One prompt for NIH R01s, another for UKRI Future Leaders Fellowships.

Validation before finalisation:

Insert a manual approval step before document assembly. Have the researcher review Claude's generated sections for factual accuracy and funder fit. This takes 10 minutes rather than two hours.

Cost Breakdown