Synthesize Multiple Documents into Visual Findings

What you’ll build

A research synthesis board that organizes multiple documents, vectorizes extracted text, finds cross-cutting themes, and creates a visual map linking findings to source material.

Upload and vectorize 5+ PDFs
Ask AI to identify themes across all sources
Create finding nodes connected to source documents
Search for specific topics across your entire document set

Prerequisites

ChatGrid API key (get one here)
Node.js 18+ or Python 3.10+
PDF files or document URLs to analyze

Step 1: Create a research board

curl -X POST https://api.chatgrid.ai/v1/boards \
  -H "Authorization: Bearer cgk_live_..." \
  -H "Content-Type: application/json" \
  -d '{"name": "Q1 Market Research Synthesis"}'

const res = await fetch("https://api.chatgrid.ai/v1/boards", {
  method: "POST",
  headers: { Authorization: "Bearer cgk_live_...", "Content-Type": "application/json" },
  body: JSON.stringify({ name: "Q1 Market Research Synthesis" }),
});
const { data: board } = await res.json();
const boardId = board.id;

import requests
API_KEY = "cgk_live_..."
BASE = "https://api.chatgrid.ai/v1"
headers = {"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"}
res = requests.post(f"{BASE}/boards", headers=headers, json={"name": "Q1 Market Research Synthesis"})
board_id = res.json()["data"]["id"]

Step 2: Upload PDFs and create document nodes

Upload each PDF as an asset, then create a node on the canvas.

cURL

# Upload the file
curl -X POST https://api.chatgrid.ai/v1/boards/{boardId}/assets \
  -H "Authorization: Bearer cgk_live_..." \
  -F "file=@gartner-report-2026.pdf"

# Create a PDF node using fields from the asset upload response
curl -X POST https://api.chatgrid.ai/v1/boards/{boardId}/nodes \
  -H "Authorization: Bearer cgk_live_..." \
  -H "Content-Type: application/json" \
  -d '{"type": "pdf", "position": {"x": 0, "y": 0}, "data": {"label": "Gartner Report 2026", "publicUrl": "{assetUrl}", "storagePath": "{storagePath}"}}'

Repeat for all documents. Here is how to batch it in Python:

Python

documents = [
    {"file": "gartner-report-2026.pdf", "label": "Gartner Report", "x": 0},
    {"file": "forrester-wave-q1.pdf", "label": "Forrester Wave Q1", "x": 400},
    {"file": "internal-survey.pdf", "label": "Customer Survey", "x": 800},
    {"file": "competitor-teardown.pdf", "label": "Competitor Teardown", "x": 0, "y": 350},
    {"file": "win-loss-analysis.pdf", "label": "Win/Loss Analysis", "x": 400, "y": 350},
]
node_ids = []
for doc in documents:
    with open(doc["file"], "rb") as f:
        upload = requests.post(f"{BASE}/boards/{board_id}/assets",
            headers={"Authorization": f"Bearer {API_KEY}"}, files={"file": f})
    asset = upload.json()["data"]
    node = requests.post(f"{BASE}/boards/{board_id}/nodes", headers=headers, json={
        "type": "pdf", "position": {"x": doc["x"], "y": doc.get("y", 0)},
        "data": {
            "label": doc["label"],
            "publicUrl": asset["url"],
            "storagePath": asset["storage_path"],
            "fileSize": asset["size"],
        },
    })
    node_ids.append({"id": node.json()["data"]["id"], "label": doc["label"]})

Step 3: Vectorize all documents

Vectorize extracted document text, linking chunks to the source node.

curl -X POST https://api.chatgrid.ai/v1/boards/{boardId}/documents/vectorize \
  -H "Authorization: Bearer cgk_live_..." \
  -H "Content-Type: application/json" \
  -d '{
    "content": "Paste extracted text from the report here. Include the sections you want ChatGrid to search and cite later.",
    "node_id": "{nodeId}",
    "metadata": {"source": "gartner-report-2026.pdf"}
  }'
# Repeat for each document

const vectorizePromises = documents.map((doc) =>
  fetch(`https://api.chatgrid.ai/v1/boards/${boardId}/documents/vectorize`, {
    method: "POST",
    headers: { Authorization: "Bearer cgk_live_...", "Content-Type": "application/json" },
    body: JSON.stringify({
      content: doc.extractedText,
      node_id: doc.nodeId,
      metadata: { source: doc.fileName },
    }),
  })
);
await Promise.all(vectorizePromises);

The REST API vectorizes text you provide. Use your own extraction pipeline, then send the extracted text to /documents/vectorize.

Step 4: Ask AI to find themes

Create a chat and ask the AI to synthesize across all documents.

curl -X POST https://api.chatgrid.ai/v1/boards/{boardId}/chats \
  -H "Authorization: Bearer cgk_live_..." \
  -H "Content-Type: application/json" \
  -d '{"title": "Cross-Document Synthesis"}'

curl -X POST https://api.chatgrid.ai/v1/boards/{boardId}/chats/{chatId}/messages \
  -H "Authorization: Bearer cgk_live_..." \
  -H "Content-Type: application/json" \
  -d '{
    "content": "Analyze all documents on this board. Identify the top 5 recurring themes. For each theme, cite which documents mention it and summarize the key points.",
    "stream": false
  }'

chat = requests.post(f"{BASE}/boards/{board_id}/chats", headers=headers,
    json={"title": "Cross-Document Synthesis"})
chat_id = chat.json()["data"]["id"]

response = requests.post(f"{BASE}/boards/{board_id}/chats/{chat_id}/messages", headers=headers, json={
    "content": "Analyze all documents on this board. Identify the top 5 recurring themes. For each theme, cite which documents mention it and summarize the key points.",
    "stream": False,
})
analysis = response.json()["data"]["content"]

Step 5: Create finding nodes and connect to sources

Turn each theme into a sticky note and draw edges to the documents it references.

Python

themes = [
    {"label": "AI adoption accelerating", "content": "3 of 5 reports cite >40% YoY growth in AI tool adoption.", "sources": [node_ids[0], node_ids[1]], "x": 200},
    {"label": "Buyer consolidation", "content": "Teams reducing vendor count from 8 to 3.", "sources": [node_ids[1], node_ids[4]], "x": 600},
    {"label": "Self-serve preferred", "content": "72% prefer self-serve onboarding.", "sources": [node_ids[2], node_ids[4]], "x": 1000},
]
for theme in themes:
    node = requests.post(f"{BASE}/boards/{board_id}/nodes", headers=headers, json={
        "type": "note", "position": {"x": theme["x"], "y": 700},
        "data": {"label": f"Theme: {theme['label']}", "text": theme["content"]},
    })
    finding_id = node.json()["data"]["id"]
    for source in theme["sources"]:
        requests.post(f"{BASE}/boards/{board_id}/edges", headers=headers, json={
            "source_node_id": finding_id, "target_node_id": source["id"], "data": {"label": "cited in"},
        })

Step 6: Search for specific topics

Query across all documents without re-reading them.

cURL

curl -X POST https://api.chatgrid.ai/v1/boards/{boardId}/documents/search \
  -H "Authorization: Bearer cgk_live_..." \
  -H "Content-Type: application/json" \
  -d '{"query": "what do analysts say about pricing pressure in 2026?", "limit": 5}'

Results include matching text, similarity score, and source metadata so you can trace every finding back to its document.

What’s happening under the hood

Each vectorize call chunks submitted text and embeds it via pgvector. The node_id parameter ties chunks to their visual node, so edges between finding nodes and source nodes are meaningful. When AI answers a synthesis question, it performs semantic search across all chunks on the board and reasons over the top matches. The metadata you attach appears in search results, making claims easy to verify.

Cookbooks

Synthesize Multiple Documents into Visual Findings

What you’ll build

Prerequisites

Step 1: Create a research board

Step 2: Upload PDFs and create document nodes

Step 3: Vectorize all documents

Step 4: Ask AI to find themes

Step 5: Create finding nodes and connect to sources

Step 6: Search for specific topics

What’s happening under the hood

Next steps

Meeting Knowledge

Competitive Intel

​What you’ll build

​Prerequisites

​Step 1: Create a research board

​Step 2: Upload PDFs and create document nodes

​Step 3: Vectorize all documents

​Step 4: Ask AI to find themes

​Step 5: Create finding nodes and connect to sources

​Step 6: Search for specific topics

​What’s happening under the hood

​Next steps

Meeting Knowledge

Competitive Intel

What you’ll build

Prerequisites

Step 1: Create a research board

Step 2: Upload PDFs and create document nodes

Step 3: Vectorize all documents

Step 4: Ask AI to find themes

Step 5: Create finding nodes and connect to sources

Step 6: Search for specific topics

What’s happening under the hood

Next steps