Build a Competitive Intelligence Board

What you’ll build

A board that tracks competitor website source nodes, vectorizes extracted competitor notes, and lets you ask AI to compare positioning across all competitors at once.

Add competitor websites as visual nodes
Vectorize content for semantic search
Ask AI to compare positioning and pricing
Create summary sticky notes linked to sources

Prerequisites

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

Step 1: Create a board

curl -X POST https://api.chatgrid.ai/v1/boards \
  -H "Authorization: Bearer cgk_live_..." \
  -H "Content-Type: application/json" \
  -d '{"name": "Competitive Intel — Q1 2026"}'

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: "Competitive Intel — Q1 2026" }),
});
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": "Competitive Intel — Q1 2026"})
board_id = res.json()["data"]["id"]

Save the board_id from the response.

Step 2: Add competitor website nodes

Batch-create a node for each competitor in one request.

cURL

curl -X POST https://api.chatgrid.ai/v1/boards/{boardId}/nodes/batch \
  -H "Authorization: Bearer cgk_live_..." \
  -H "Content-Type: application/json" \
  -d '{
    "operations": [
      {"action": "create", "data": {"type": "url", "position": {"x": 0, "y": 0}, "data": {"url": "https://competitor-a.com", "label": "Competitor A"}}},
      {"action": "create", "data": {"type": "url", "position": {"x": 400, "y": 0}, "data": {"url": "https://competitor-b.com", "label": "Competitor B"}}},
      {"action": "create", "data": {"type": "url", "position": {"x": 800, "y": 0}, "data": {"url": "https://competitor-c.com", "label": "Competitor C"}}}
    ]
  }'

Save the returned node IDs — you need them to link vectorized content.

Step 3: Vectorize competitor content

Vectorize extracted competitor notes. The node_id ties chunks back 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": "Competitor A homepage notes: enterprise positioning, SOC 2 emphasis, integrations with Salesforce and Slack. Pricing page starts at $49 per seat.",
    "node_id": "{nodeIdA}",
    "metadata": {"source": "competitor-a", "type": "homepage-pricing"}
  }'

# Repeat for competitors B and C

notes = [
    ("Competitor A homepage notes: enterprise positioning, SOC 2 emphasis, integrations with Salesforce and Slack. Pricing page starts at $49 per seat.", node_a_id, "competitor-a"),
    ("Competitor B notes: SMB positioning, usage-based pricing, fast onboarding.", node_b_id, "competitor-b"),
    ("Competitor C notes: developer-first positioning, generous free tier, API-led workflow.", node_c_id, "competitor-c"),
]
for content, nid, source in notes:
    requests.post(f"{BASE}/boards/{board_id}/documents/vectorize", headers=headers,
        json={"content": content, "node_id": nid, "metadata": {"source": source}})

Step 4: Ask AI to compare positioning

Create a chat thread and ask a cross-competitor question.

# Create a chat thread
curl -X POST https://api.chatgrid.ai/v1/boards/{boardId}/chats \
  -H "Authorization: Bearer cgk_live_..." \
  -H "Content-Type: application/json" \
  -d '{"title": "Competitive Analysis"}'

# Ask the comparison question (use chatId from above)
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": "Compare the positioning and pricing of all three competitors. What does each one emphasize? Where are the gaps we could exploit?",
    "stream": false
  }'

const chatRes = await fetch(`https://api.chatgrid.ai/v1/boards/${boardId}/chats`, {
  method: "POST",
  headers: { Authorization: "Bearer cgk_live_...", "Content-Type": "application/json" },
  body: JSON.stringify({ title: "Competitive Analysis" }),
});
const { data: chat } = await chatRes.json();

const msgRes = await fetch(
  `https://api.chatgrid.ai/v1/boards/${boardId}/chats/${chat.id}/messages`,
  {
    method: "POST",
    headers: { Authorization: "Bearer cgk_live_...", "Content-Type": "application/json" },
    body: JSON.stringify({
      content: "Compare the positioning and pricing of all three competitors. What does each one emphasize? Where are the gaps we could exploit?",
      stream: false,
    }),
  }
);
const { data: analysis } = await msgRes.json();

The AI searches all vectorized content and responds with sourced comparisons.

Step 5: Create summary sticky notes

Turn findings into visual sticky notes on the canvas.

cURL

curl -X POST https://api.chatgrid.ai/v1/boards/{boardId}/nodes/batch \
  -H "Authorization: Bearer cgk_live_..." \
  -H "Content-Type: application/json" \
  -d '{
    "operations": [
      {"action": "create", "data": {"type": "note", "position": {"x": 200, "y": 400}, "data": {"label": "Key Finding", "text": "A targets enterprise, B targets SMBs, C is developer-first. Gap: mid-market team leads."}}},
      {"action": "create", "data": {"type": "note", "position": {"x": 600, "y": 400}, "data": {"label": "Pricing Insight", "text": "A: per-seat $49/mo. B: usage-based. C: generous free tier. Our opportunity: flat team pricing."}}}
    ]
  }'

Step 6: Connect findings to sources

Link summary nodes back to the competitors they reference.

cURL

curl -X POST https://api.chatgrid.ai/v1/boards/{boardId}/edges \
  -H "Authorization: Bearer cgk_live_..." \
  -H "Content-Type: application/json" \
  -d '{"source_node_id": "{findingNodeId}", "target_node_id": "{competitorANodeId}", "data": {"label": "references"}}'

Step 7: Search across competitors

Run semantic searches to answer specific questions at any time.

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": "which competitor offers an API?", "limit": 5}'

What’s happening under the hood

When you vectorize extracted text, ChatGrid splits it into chunks and generates embeddings stored in pgvector. Each chunk is tagged with the node_id and metadata you provide, so search results trace back to their source. When AI answers a question, it performs semantic search across all vectorized content on the board and uses the top matches as grounded context. Edges between nodes create a traversable graph from findings back to evidence.

Cookbooks

Build a Competitive Intelligence Board

What you’ll build

Prerequisites

Step 1: Create a board

Step 2: Add competitor website nodes

Step 3: Vectorize competitor content

Step 4: Ask AI to compare positioning

Step 5: Create summary sticky notes

Step 6: Connect findings to sources

Step 7: Search across competitors

What’s happening under the hood

Next steps

Synthesize Documents

Streaming Responses

​What you’ll build

​Prerequisites

​Step 1: Create a board

​Step 2: Add competitor website nodes

​Step 3: Vectorize competitor content

​Step 4: Ask AI to compare positioning

​Step 5: Create summary sticky notes

​Step 6: Connect findings to sources

​Step 7: Search across competitors

​What’s happening under the hood

​Next steps

Synthesize Documents

Streaming Responses

What you’ll build

Prerequisites

Step 1: Create a board

Step 2: Add competitor website nodes

Step 3: Vectorize competitor content

Step 4: Ask AI to compare positioning

Step 5: Create summary sticky notes

Step 6: Connect findings to sources

Step 7: Search across competitors

What’s happening under the hood

Next steps