Intelica

Intelica

Competitive intelligence API for autonomous AI agents — pay-per-call via x402.

Analyze any URL or company description and get back structured JSON with market positioning, user pain points, detected competitors, unique angles, and an executable Market Score. Pay $0.05 USDC per call on Base or Solana mainnet. No accounts, no API keys, no subscriptions.

Live: https://api.intelica.dev · Version: v4.5.6

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What makes Intelica different

Feature	Description
5 Context Modes	competitive · fundraising · partnership · acquisition · market_entry
Market Score	threat_level · moat_strength · market_maturity · agent_recommendation
Decision	action · confidence_score · rationale · risk_level
8 Languages	Auto-detects ES, PT, DE, FR, IT, JA, KO, ZH — applies regional market context
Trend Tracking	Detects changes between analyses — status: changed, changes: [...]
Competitive Graph	GET /graph — maps relationships between competitors across all analyses
A2A Protocol	/message/send — compatible with LangGraph, CrewAI, AutoGen, Google ADK
Intelligence Budget	Per-operator daily limits via operator_id field
Dual network	Base mainnet + Solana mainnet — agent pays on whichever network it prefers

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Quick start

# 1. Without payment → returns 402 with payment instructions
curl -X POST https://api.intelica.dev/intel \
  -H "Content-Type: application/json" \
  -d '{"text": "Notion is an all-in-one workspace for teams"}'

# 2. Free demo (300 char limit)
curl -X POST https://api.intelica.dev/demo \
  -H "Content-Type: application/json" \
  -d '{"text": "Notion is an all-in-one workspace for teams"}'

---

POST /intel

Single competitive intelligence analysis — $0.05 USDC via x402.

POST /intel
X-PAYMENT: <x402 signed payment>

{
  "url": "https://notion.so",
  "context": "I'm building a note-taking app for developers",
  "mode": "competitive"
}

Response:

{
  "source": "https://notion.so",
  "analysis": {
    "company_or_product": "Notion",
    "positioning_summary": "All-in-one workspace combining notes, docs, and databases...",
    "target_customer": "Teams and individuals seeking productivity tools",
    "core_value_props": ["Flexible databases", "Collaborative docs", "Template library"],
    "user_pain_points": ["Complex for new users", "Slow on large pages", "Limited offline support"],
    "detected_competitors": ["Obsidian", "Coda", "Confluence"],
    "unique_angle": "Developer-focused simplicity gap — Notion targets everyone, leaving power users underserved",
    "tone": "professional",
    "confidence": "high",
    "market_score": {
      "threat_level": "high",
      "moat_strength": 0.78,
      "market_maturity": "mature",
      "agent_recommendation": "counter"
    },
    "decision_recommendation": {
      "action": "avoid",
      "confidence_score": 0.91,
      "rationale": "IMI 0.78 exceeds avoid threshold; AlphaSense holds $100M+ in switching costs with enterprise contracts.",
      "risk_level": "high"
    }
  },
  "mode": "competitive",
  "detected_language": "en",
  "market_context": "global",
  "trend": {
    "status": "new",
    "changes": [],
    "snapshot_count": 1
  },
  "cached": false,
  "response_ms": 1240,
  "price_paid_usdc": "0.05"
}

---

Context Modes

Pass mode to get context-specific analysis:

Mode	Use when
competitive	Standard competitor analysis (default)
fundraising	Evaluating investor narrative, TAM, traction signals
partnership	Assessing strategic fit — complement or rival?
acquisition	Due diligence — moat, technical risk, acquisition thesis
market_entry	Market gaps, saturation, barriers to entry

{ "text": "...", "mode": "fundraising" }

---

Market Score

Every analysis includes a market_score block with executable fields:

• threat_level — high | medium | low
• moat_strength — 0.0 to 1.0 (how hard is it to compete against them)
• market_maturity — emerging | growing | mature | declining
• agent_recommendation — monitor | counter | ignore | partner

---

8 Languages — Auto-detected

Intelica detects the language of the content and applies the appropriate regional market context automatically:

Language	Market Context
ES	LATAM / Spain — pricing sensitivity, mobile-first, informal economy
PT	Brazil / Portugal — Pix ecosystem, LGPD compliance
DE	DACH — enterprise-first, privacy-conscious, structured procurement
FR	France / Francophone — European regulatory, state involvement in tech
IT	Italy — SME-heavy, design-driven, family business culture
JA	Japan — keiretsu structures, quality-obsessed, long sales cycles
KO	Korea — chaebol dynamics, Kakao/Naver ecosystem
ZH	China — BAT ecosystem, data localization, domestic/international split

No configuration needed — just pass the content in any language.

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Trend Tracking

On repeated analyses of the same company, Intelica compares the current result with the previous snapshot and returns a trend object:

"trend": {
  "status": "changed",
  "changes": [
    { "field": "threat_level", "from": "medium", "to": "high" },
    { "field": "agent_recommendation", "from": "monitor", "to": "counter" },
    { "field": "moat_strength", "from": 0.5, "to": 0.7, "delta": 0.2 },
    { "field": "detected_competitors", "added": ["Linear"] }
  ],
  "previous_snapshot": "2026-06-01T00:00:00Z"
}

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Competitive Graph

GET /graph — returns the accumulated competitive relationship map across all analyses.

# Full graph
curl https://api.intelica.dev/graph

# Subgraph centered on a company
curl "https://api.intelica.dev/graph?company=Notion"

Returns nodes (companies), edges (competitive relationships), and hub_competitors — companies that appear most frequently as competitors across all analyses.

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A2A Protocol

POST /message/send — compatible with LangGraph, CrewAI, AutoGen, Google ADK.

POST /message/send

{
  "message": {
    "role": "user",
    "parts": [
      { "type": "text", "text": "mode: fundraising\nAnalyze Notion as a competitor" }
    ]
  }
}

Free tier — basic analysis without payment. Upgrade to /intel for full analysis with trend tracking and graph updates.

---

POST /batch

Up to 10 analyses in one call — $0.20 USDC via x402.

{
  "items": [
    { "url": "https://notion.so", "mode": "competitive" },
    { "text": "Coda is a collaborative doc platform", "mode": "acquisition" },
    { "url": "https://obsidian.md", "mode": "partnership" }
  ]
}

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Intelligence Budget

Limit daily analyses per operator pipeline. Pass operator_id in any request:

{ "text": "...", "mode": "competitive", "operator_id": "my-pipeline-v1" }

Budget enforcement is configured server-side via intel_budgets table in Supabase.

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Endpoints

Method	Path	Auth	Price
GET	/	None	Free
GET	/health	None	Free
GET	/pricing	None	Free
GET	/llms.txt	None	Free
GET	/graph	None	Free
GET	/.well-known/x402.json	None	Free
GET	/agent-card.json	None	Free
POST	/demo	None	Free (300 chars)
POST	/message/send	None	Free (A2A basic)
POST	/mcp	x402	$0.05 USDC
POST	/intel	x402	$0.05 USDC
POST	/batch	x402	$0.20 USDC (up to 10)

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Methodology

How Intelica works

Every analysis runs through a structured pipeline:

Input (URL or text)
  ↓
Input sanitization (HTML strip, prompt injection protection)
  ↓
Language detection (8 languages: EN, ES, PT, DE, FR, IT, JA, KO, ZH)
  ↓
Web search enrichment via Exa (3 verified sources per analysis)
  ↓
Context mode selection (10 specialized modes)
  ↓
Claude Haiku — structured JSON generation
  ↓
Output validation (schema enforcement, range clamping)
  ↓
Trend tracking (snapshot comparison vs previous analysis)
  ↓
Competitive Graph update (nodes + edges in Supabase)
  ↓
Cache (6h TTL, Supabase primary + in-memory fallback)

Data sources

• Primary: Content from the URL or text provided by the caller
• Web enrichment: Real-time search via Exa — 3 verified URLs returned as sources[] in every analysis
• LLM inference: Claude Haiku (Anthropic) generates the structured analysis from the enriched content

Market Score methodology

The market_score object is generated by Claude Haiku and validated against strict ranges:

Field	Type	Range	Description
threat_level	enum	high / medium / low	Competitive threat to the caller
moat_strength	float	0.0 → 1.0	Defensibility of the analyzed company
market_maturity	enum	emerging / growing / mature / declining	Market lifecycle stage
agent_recommendation	enum	monitor / counter / ignore / partner	Executable action for the agent
decision_recommendation.action	enum	enter / avoid / monitor / acquire / partner	Primary recommended action
decision_recommendation.confidence_score	float	0.0–1.0	Confidence in recommendation (≥0.80 = strong signal)
decision_recommendation.rationale	string	—	One sentence anchored to a specific data point
decision_recommendation.risk_level	enum	high / medium / low	Downside risk of acting on this recommendation

All enum values are enforced server-side — the model cannot return values outside these sets. moat_strength is clamped to [0.0, 1.0] regardless of LLM output.

Competitor detection

Competitors are detected by Claude Haiku from the input content + web enrichment context. With Exa integration, detected competitors are cross-referenced against real web sources. The sources[] field in the response shows which URLs informed the analysis.

Confidence scoring

• high — sufficient content and web context to produce reliable analysis
• medium — partial content, some inference required
• low — insufficient content, treat output as directional only

Cache and freshness

Results are cached for 6 hours (Supabase primary, in-memory fallback). This is appropriate for most competitive intelligence use cases. For fast-moving markets (crypto, AI, early-stage startups), pass force_refresh: true in the request body to bypass cache and run a fresh analysis.

Limitations

• Analysis quality depends on the richness of the input content
• Competitor detection is LLM-generated — treat as directional, not exhaustive
• Market Score reflects the model's interpretation of the content, not a proprietary scoring database
• Cache TTL of 6h may not capture intraday events in fast-moving markets

Integration Examples

LangChain

from langchain.tools import tool
import httpx

@tool
def analyze_competitor(url: str, mode: str = "competitive") -> dict:
    """Analyze a competitor URL using Intelica. Returns structured competitive intelligence."""
    from x402.client import PaymentClient
    client = PaymentClient(wallet_private_key="your_evm_private_key")
    response = client.post(
        "https://api.intelica.dev/intel",
        json={"url": url, "mode": mode}
    )
    return response.json()

# Use in an agent
from langchain.agents import initialize_agent, AgentType
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4")
agent = initialize_agent([analyze_competitor], llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION)
agent.run("Analyze Notion as a competitor for a note-taking app")

LlamaIndex

from llama_index.core.tools import FunctionTool
import httpx

def intelica_analyze(text: str, mode: str = "competitive") -> str:
    """Analyze a company or product description and return competitive intelligence."""
    from x402.client import PaymentClient
    client = PaymentClient(wallet_private_key="your_evm_private_key")
    response = client.post(
        "https://api.intelica.dev/intel",
        json={"text": text, "mode": mode}
    )
    return str(response.json()["analysis"])

intelica_tool = FunctionTool.from_defaults(fn=intelica_analyze)

Free demo (no payment required)

import httpx

response = httpx.post(
    "https://api.intelica.dev/demo",
    json={"text": "Notion is an all-in-one workspace for notes and databases"}
)
print(response.json()["analysis"]["market_score"])
# {"threat_level": "high", "moat_strength": 0.78, "agent_recommendation": "counter"}
# {"action": "avoid", "confidence_score": 0.91, "rationale": "IMI 0.78 exceeds avoid threshold.", "risk_level": "high"}

---

Payment via x402

Base mainnet (EVM):

import httpx
from x402.client import PaymentClient

client = PaymentClient(wallet_private_key="your_evm_private_key")
response = client.post(
    "https://api.intelica.dev/intel",
    json={"url": "https://competitor.com", "mode": "competitive"}
)

Solana mainnet: The 402 response includes both Base and Solana payment options. Solana clients use solana-mainnet network entry from the accepts array.

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Deploy your own

1. Fork this repo
2. Connect to Render → New Web Service → select repo
3. Render detects render.yaml automatically
4. Add environment variables:
   • ANTHROPIC_API_KEY
   • WALLET_ADDRESS (EVM — Base mainnet)
   • WALLET_SOLANA (Solana mainnet)
   • SUPABASE_URL
   • SUPABASE_SERVICE_KEY
5. Deploy — live in ~2 minutes

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Human Access — Buy reports with USDC

For founders, consultants, and investors who need competitive intelligence without an API setup.

Plan	Reports	Days	Price
Starter	30	10	$15 USDC
Pro	100	30	$30 USDC

One page, complete experience:

👉 intelica.dev/intelica.html

The page guides you through everything in one scroll:

1. See the plans and clear rules — exactly what you're buying
2. Pay in USDC on Solana — session key activated instantly
3. Enter your session key — generate reports immediately
4. Download your report as HTML or JSON

Additional links:

• 🔍 Sample report — Japan market entry example

Payment network: Solana mainnet · Asset: USDC · No accounts, no subscriptions

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Stack

• Runtime: FastAPI + Python 3.11
• LLM: Claude Haiku 4.5 (Anthropic)
• Payments: x402 protocol — USDC on Base + Solana mainnet
• Facilitator: PayAI Network
• Persistence: Supabase (PostgreSQL)
• Deploy: Render (AWS us-east)
• Wallets:
  • Base: 0xCf1d31020A7915421f6d66B9835Dcb6f422337E7
  • Solana: 45q8KyCAGSHHd6qYP2ZkEJh22SzeQeXRfyFsENcx3KN6

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License

MIT