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The Best Competitive Intelligence API for Autonomous AI Agents (2026)

#aiagents #x402 #competitiveintelligence #mcp

Why agents need competitive intelligence

Most agent workflows today look like this:

Agent receives task
→ Calls LLM for reasoning
→ Executes action
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But the best decisions require context:

Agent receives task
→ Calls Intelica for market context ($0.05)
→ Calls LLM with enriched context
→ Executes better decision
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A VC agent that evaluates 50 startups per day needs to know if each startup's market is defensible. A DeFi trading agent needs to know the competitive moat of a protocol before entering a position. A sales agent needs a battlecard before a live demo.

How it works

1. Call the free demo 

curl -X POST https://intelica.onrender.com/demo \
  -H "Content-Type: application/json" \
  -d '{"text": "Notion is an all-in-one workspace for notes, databases, and project management", "mode": "competitive"}'
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2. Get structured intelligence 

{
  "company_or_product": "Notion",
  "positioning_summary": "Notion is a flexible all-in-one workspace...",
  "detected_competitors": ["Confluence", "Asana", "Monday.com"],
  "unique_angle": "Counter with specialist depth: Notion sacrifices best-in-class...",
  "confidence": "high",
  "sources": [
    "https://example.com/notion-competitors",
    "https://example.com/notion-analysis"
  ],
  "market_score": {
    "threat_level": "high",
    "moat_strength": 0.72,
    "market_maturity": "mature",
    "agent_recommendation": "counter"
  }
}
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3. Agent acts on agent_recommendation

The agent_recommendation field is designed for direct agent consumption:

  • monitor — track their progress, not a direct threat
  • counter — build against them, they're a real threat
  • ignore — not worth your attention
  • partner — potential ally, not a competitor

10 context modes for every use case

Intelica isn't a one-size-fits-all analysis. Each mode optimizes the output for a specific decision context:

Mode Use case Price
competitive General market analysis $0.05
fundraising Investor narrative, TAM, traction signals $0.05
partnership Strategic fit, complementarity $0.05
acquisition M&A due diligence $0.05
market_entry Market saturation, barriers to entry $0.05
crypto_protocol DeFi moat, tokenomics, regulatory risk $0.05
venture_screening Investment thesis + deal-breakers $1.00
regulatory_compliance EU AI Act, GDPR, HIPAA exposure $1.00
risk_assessment Business model stability, operational risk $1.00
sales_enablement Battlecard + objection handler $1.00

Real output examples

Uniswap under crypto_protocol mode 

{
  "company_or_product": "Uniswap",
  "market_score": {
    "threat_level": "high",
    "moat_strength": 0.82,
    "market_maturity": "mature",
    "agent_recommendation": "monitor"
  },
  "unique_angle": "Uniswap's v4 hooks architecture and first-mover network effects create defensible liquidity moat, but regulatory risk on governance token is asymmetrically high",
  "detected_competitors": ["Curve Finance", "dYdX", "Balancer"],
  "sources": ["https://...", "https://...", "https://..."]
}
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Clearview AI under regulatory_compliance mode 

{
  "market_score": {
    "threat_level": "high",
    "moat_strength": 0.15,
    "market_maturity": "declining",
    "agent_recommendation": "monitor"
  },
  "user_pain_points": [
    "EU AI Act Article 5 prohibition on real-time biometric identification",
    "GDPR violation — no lawful basis for image scraping",
    "BIPA class action exposure: $1B+"
  ],
  "unique_angle": "Clearview's competitive advantage — massive unregulated image corpus — is simultaneously its primary regulatory liability"
}
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Payment via x402

Intelica uses the x402 protocol — HTTP 402 Payment Required. Agents pay autonomously without human intervention.

import httpx

# Step 1: Request without payment → receive 402 challenge
response = httpx.post(
    "https://intelica.onrender.com/intel",
    json={"text": "Stripe payment API", "mode": "competitive"}
)
# response.status_code == 402
# response.json()["accepts"][0]["network"] == "base-mainnet"

# Step 2: Pay $0.05 USDC on Base or Solana
# Step 3: Retry with X-PAYMENT header
response = httpx.post(
    "https://intelica.onrender.com/intel",
    json={"text": "Stripe payment API", "mode": "competitive"},
    headers={"X-PAYMENT": payment_token}
)
# response.status_code == 200
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Supported networks: Base mainnet and Solana mainnet.

LangChain integration 

from langchain.tools import tool
import httpx

@tool
def analyze_competitor(text: str, mode: str = "competitive") -> dict:
    """Analyze a competitor using Intelica. Returns market score and positioning."""
    # Pay via x402 first, then call with X-PAYMENT header
    response = httpx.post(
        "https://intelica.onrender.com/intel",
        json={"text": text, "mode": mode},
        headers={"X-PAYMENT": get_x402_token()}
    )
    return response.json()["analysis"]
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MCP integration (Claude Desktop, Cursor, VS Code)

Add Intelica as an MCP tool:

{
  "mcpServers": {
    "intelica": {
      "url": "https://intelica.onrender.com/mcp"
    }
  }
}
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Available tools: analyze_competitor, batch_analyze

Advanced: batch analysis

Analyze up to 10 competitors in parallel for $0.20 USDC:

curl -X POST https://intelica.onrender.com/batch \
  -H "Content-Type: application/json" \
  -H "X-PAYMENT: <token>" \
  -d '{
    "items": [
      {"text": "Notion workspace", "mode": "competitive"},
      {"text": "Confluence Atlassian", "mode": "sales_enablement"},
      {"text": "Monday.com project management", "mode": "competitive"}
    ]
  }'
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force_refresh for fast-moving markets

For crypto, AI startups, or any market where 6 hours of cache TTL is too slow:

{
  "text": "Uniswap v4 AMM protocol",
  "mode": "crypto_protocol",
  "force_refresh": true
}
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Why Intelica is different from Crayon, Klue, or Kompyte

Crayon/Klue/Kompyte Intelica
Designed for Human analysts Autonomous agents
Price $15K–$40K/year $0.05–$1.00/call
Payment Credit card, contract x402 USDC — autonomous
Output Dashboard, email Structured JSON
Response time Minutes to hours ~5 seconds
API Limited Full REST + MCP + A2A

Links

Built by a solo developer in Bogotá, Colombia. Feedback welcome — open an issue on GitHub.

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