Automate a Research Pipeline

What you’ll build

A structured research pipeline that creates multiple source nodes, vectorizes extracted source text, and produces an AI-synthesized report with edges linking conclusions back to source material.

Batch-create 5+ source nodes in one API call
Vectorize extracted source text for semantic search
AI-generated synthesis across all sources
Edges connecting findings to their source nodes

Prerequisites

ChatGrid API key (get one here)
Node.js 18+ or Python 3.10+

Step 1: Create a research board

curl -X POST https://api.chatgrid.ai/v1/boards \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"name": "AI Agent Market Research - Q1 2026"}'

const board = await fetch("https://api.chatgrid.ai/v1/boards", {
  method: "POST",
  headers: { Authorization: `Bearer ${API_KEY}`, "Content-Type": "application/json" },
  body: JSON.stringify({ name: "AI Agent Market Research - Q1 2026" }),
}).then((r) => r.json());
const boardId = board.data.id;

import requests
board = requests.post("https://api.chatgrid.ai/v1/boards",
    headers={"Authorization": f"Bearer {API_KEY}"},
    json={"name": "AI Agent Market Research - Q1 2026"}).json()
board_id = board["data"]["id"]

Step 2: Batch-create source nodes

Use the batch endpoint to add multiple source nodes in one call — faster and atomic.

curl -X POST https://api.chatgrid.ai/v1/boards/{boardId}/nodes/batch \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "operations": [
      {"action": "create", "data": {"type": "url", "position": {"x": 0, "y": 0}, "data": {"url": "https://a16z.com/ai-agents-landscape-2026"}}},
      {"action": "create", "data": {"type": "url", "position": {"x": 420, "y": 0}, "data": {"url": "https://sequoia.com/article/agentic-ai-market-map"}}},
      {"action": "create", "data": {"type": "url", "position": {"x": 840, "y": 0}, "data": {"url": "https://techcrunch.com/2026/02/15/enterprise-ai-agents-funding"}}},
      {"action": "create", "data": {"type": "youtube", "position": {"x": 1260, "y": 0}, "data": {"url": "https://youtube.com/watch?v=agent-market-deep-dive"}}},
      {"action": "create", "data": {"type": "url", "position": {"x": 1680, "y": 0}, "data": {"url": "https://mckinsey.com/capabilities/ai/ai-agents-enterprise-adoption"}}}
    ]
  }'

const sources = [
  { type: "url", position: { x: 0, y: 0 }, data: { url: "https://a16z.com/ai-agents-landscape-2026" } },
  { type: "url", position: { x: 420, y: 0 }, data: { url: "https://sequoia.com/article/agentic-ai-market-map" } },
  { type: "url", position: { x: 840, y: 0 }, data: { url: "https://techcrunch.com/2026/02/15/enterprise-ai-agents-funding" } },
  { type: "youtube", position: { x: 1260, y: 0 }, data: { url: "https://youtube.com/watch?v=agent-market-deep-dive" } },
  { type: "url", position: { x: 1680, y: 0 }, data: { url: "https://mckinsey.com/capabilities/ai/ai-agents-enterprise-adoption" } },
];
const created = await fetch(`https://api.chatgrid.ai/v1/boards/${boardId}/nodes/batch`, {
  method: "POST",
  headers: { Authorization: `Bearer ${API_KEY}`, "Content-Type": "application/json" },
  body: JSON.stringify({
    operations: sources.map((source) => ({ action: "create", data: source })),
  }),
}).then((r) => r.json());

sources = [
    {"type": "url", "position": {"x": 0, "y": 0}, "data": {"url": "https://a16z.com/ai-agents-landscape-2026"}},
    {"type": "url", "position": {"x": 420, "y": 0}, "data": {"url": "https://sequoia.com/article/agentic-ai-market-map"}},
    {"type": "url", "position": {"x": 840, "y": 0}, "data": {"url": "https://techcrunch.com/2026/02/15/enterprise-ai-agents-funding"}},
    {"type": "youtube", "position": {"x": 1260, "y": 0}, "data": {"url": "https://youtube.com/watch?v=agent-market-deep-dive"}},
    {"type": "url", "position": {"x": 1680, "y": 0}, "data": {"url": "https://mckinsey.com/capabilities/ai/ai-agents-enterprise-adoption"}},
]
created = requests.post(f"https://api.chatgrid.ai/v1/boards/{board_id}/nodes/batch",
    headers={"Authorization": f"Bearer {API_KEY}"},
    json={"operations": [{"action": "create", "data": source} for source in sources]}).json()

Save the node IDs from the individual node creation responses if you need to connect findings back to sources later.

Step 3: Vectorize all sources

Vectorize extracted source text so AI can search across it semantically. Run calls in parallel for speed.

curl -X POST https://api.chatgrid.ai/v1/boards/{boardId}/documents/vectorize \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "content": "AI agent market notes: enterprise buyers prioritize reliability, governance, and measurable workflow automation over generic copilots.",
    "metadata": {"source": "research-notes", "topic": "agentic-ai-market"}
  }'

const researchNotes = [
  {
    content: "AI agent market notes: enterprise buyers prioritize reliability, governance, and measurable workflow automation over generic copilots.",
    metadata: { source: "research-notes", topic: "agentic-ai-market" },
  },
  {
    content: "Adoption trend: teams start with internal automation, then expand to customer-facing workflows once observability and approvals are in place.",
    metadata: { source: "analyst-summary", topic: "enterprise-adoption" },
  },
];
await Promise.all(researchNotes.map((note) =>
  fetch(`https://api.chatgrid.ai/v1/boards/${boardId}/documents/vectorize`, {
    method: "POST",
    headers: { Authorization: `Bearer ${API_KEY}`, "Content-Type": "application/json" },
    body: JSON.stringify(note),
  })
));

import concurrent.futures
research_notes = [
    {"content": "AI agent market notes: enterprise buyers prioritize reliability, governance, and measurable workflow automation over generic copilots.", "metadata": {"source": "research-notes", "topic": "agentic-ai-market"}},
    {"content": "Adoption trend: teams start with internal automation, then expand to customer-facing workflows once observability and approvals are in place.", "metadata": {"source": "analyst-summary", "topic": "enterprise-adoption"}},
]
def vectorize(note):
    return requests.post(f"https://api.chatgrid.ai/v1/boards/{board_id}/documents/vectorize",
        headers={"Authorization": f"Bearer {API_KEY}"}, json=note)
with concurrent.futures.ThreadPoolExecutor(max_workers=5) as pool:
    list(pool.map(vectorize, research_notes))

Vectorization runs asynchronously. For large sources, allow 10-30 seconds before querying.

Step 4: Ask AI to synthesize across sources

Create a chat and ask AI for a structured synthesis. It searches all vectorized content before responding.

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

curl -X POST https://api.chatgrid.ai/v1/boards/{boardId}/chats/{chatId}/messages \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "content": "Synthesize the research on this board into a findings report: 1) Market size and growth, 2) Key players and positioning, 3) Enterprise adoption trends, 4) Gaps and opportunities. Cite which source each finding comes from.",
    "stream": false
  }'

const chat = await fetch(`https://api.chatgrid.ai/v1/boards/${boardId}/chats`, {
  method: "POST",
  headers: { Authorization: `Bearer ${API_KEY}`, "Content-Type": "application/json" },
  body: JSON.stringify({ title: "Research Synthesis" }),
}).then((r) => r.json());

const report = await fetch(
  `https://api.chatgrid.ai/v1/boards/${boardId}/chats/${chat.data.id}/messages`,
  {
    method: "POST",
    headers: { Authorization: `Bearer ${API_KEY}`, "Content-Type": "application/json" },
    body: JSON.stringify({
      content: "Synthesize the research on this board into a findings report: 1) Market size and growth, 2) Key players and positioning, 3) Enterprise adoption trends, 4) Gaps and opportunities. Cite which source each finding comes from.",
      stream: false,
    }),
  }
).then((r) => r.json());

chat = requests.post(f"https://api.chatgrid.ai/v1/boards/{board_id}/chats",
    headers={"Authorization": f"Bearer {API_KEY}"},
    json={"title": "Research Synthesis"}).json()

report = requests.post(
    f"https://api.chatgrid.ai/v1/boards/{board_id}/chats/{chat['data']['id']}/messages",
    headers={"Authorization": f"Bearer {API_KEY}"},
    json={"content": "Synthesize the research on this board into a findings report: 1) Market size and growth, 2) Key players and positioning, 3) Enterprise adoption trends, 4) Gaps and opportunities. Cite which source each finding comes from.", "stream": False},
).json()

Step 5: Create findings node and link to sources

Save the synthesis as a notepad, then create edges connecting it to each source for traceability.

curl -X POST https://api.chatgrid.ai/v1/boards/{boardId}/nodes \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"type": "document", "position": {"x": 0, "y": 420}, "data": {"content": "# AI Agent Market Findings\n\n[Paste synthesis here]\n\nGenerated from 5 sources."}}'

# Link findings to a source node (repeat for each source)
curl -X POST https://api.chatgrid.ai/v1/boards/{boardId}/edges \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"source_node_id": "{findingsNodeId}", "target_node_id": "{sourceNodeId}", "data": {"label": "sourced from"}}'

const findings = await fetch(`https://api.chatgrid.ai/v1/boards/${boardId}/nodes`, {
  method: "POST",
  headers: { Authorization: `Bearer ${API_KEY}`, "Content-Type": "application/json" },
  body: JSON.stringify({
    type: "document",
    position: { x: 0, y: 420 },
    data: { content: `# AI Agent Market Findings\n\n${report.data.content}\n\nGenerated from ${sourceNodeIds.length} sources.` },
  }),
}).then((r) => r.json());

for (const sourceNodeId of sourceNodeIds) {
  await fetch(`https://api.chatgrid.ai/v1/boards/${boardId}/edges`, {
    method: "POST",
    headers: { Authorization: `Bearer ${API_KEY}`, "Content-Type": "application/json" },
    body: JSON.stringify({
      source_node_id: findings.data.id,
      target_node_id: sourceNodeId,
      data: { label: "sourced from" },
    }),
  });
}

findings = requests.post(f"https://api.chatgrid.ai/v1/boards/{board_id}/nodes",
    headers={"Authorization": f"Bearer {API_KEY}"},
    json={"type": "document", "position": {"x": 0, "y": 420}, "data": {
        "content": f"# AI Agent Market Findings\n\n{report['data']['content']}\n\nGenerated from {len(source_node_ids)} sources."
    }}).json()

for source_node_id in source_node_ids:
    requests.post(f"https://api.chatgrid.ai/v1/boards/{board_id}/edges",
        headers={"Authorization": f"Bearer {API_KEY}"},
        json={"source_node_id": findings["data"]["id"], "target_node_id": source_node_id, "data": {"label": "sourced from"}})

What’s happening under the hood

The batch endpoint creates all source nodes in a single database transaction. Vectorization runs in parallel on the text you provide, chunks it into searchable segments, and embeds it using pgvector. When AI generates the synthesis, it performs a semantic search across those chunks, retrieves the most relevant passages, and generates a grounded response with citations. Edges are stored as first-class graph relationships, so anyone viewing the board can visually trace a finding back to its original source.

Cookbooks

Automate a Research Pipeline

What you’ll build

Prerequisites

Step 1: Create a research board

Step 2: Batch-create source nodes

Step 3: Vectorize all sources

Step 4: Ask AI to synthesize across sources

Step 5: Create findings node and link to sources

What’s happening under the hood

Next steps

Client Onboarding

Sales Deal Room

​What you’ll build

​Prerequisites

​Step 1: Create a research board

​Step 2: Batch-create source nodes

​Step 3: Vectorize all sources

​Step 4: Ask AI to synthesize across sources

​Step 5: Create findings node and link to sources

​What’s happening under the hood

​Next steps

Client Onboarding

Sales Deal Room

What you’ll build

Prerequisites

Step 1: Create a research board

Step 2: Batch-create source nodes

Step 3: Vectorize all sources

Step 4: Ask AI to synthesize across sources

Step 5: Create findings node and link to sources

What’s happening under the hood

Next steps