FOMOA for Startups: Finding Indian Companies, Funding, and Market Data
January 15, 2026
7 min read
Tushar Agrawal
Startup Search IndiaCompany Database APIIndian Startup DataFOMOAFunding DataMarket ResearchInvestor ToolsTracxn Alternative
Search 50,000+ Indian startups by industry, location, funding stage. Entity search API for investors, job seekers, and market researchers. Tracxn alternative.
The Indian Startup Data Problem
India's startup ecosystem is the third-largest globally with 100,000+ startups. Finding reliable data about these companies is challenging:
- Tracxn costs $500+/month
- Crunchbase has limited India coverage
- LinkedIn requires manual searching
- MCA data is fragmented and hard to access
Data Sources
FOMOA aggregates and structures data from:
Indian Startup Data Sources
===========================
Government/Official:
├── Zaubacorp.com - MCA company registry
├── mca.gov.in - Ministry of Corporate Affairs
└── startupindia.gov.in - Recognized startups
Business Intelligence:
├── Tracxn.com (public data)
├── Inc42.com - Indian startup news
├── YourStory.com - Startup stories
└── VCCircle.com - Funding news
Professional Networks:
├── LinkedIn company pages
├── AngelList India
└── CrunchBase (India subset)
Financial Data:
├── Tofler.in - Financial statements
├── DRHP filings (IPO-bound startups)
└── Annual reports (public companies)
Entity Search API
Basic Company Search
import requests
def search_startups(
industry: str = None,
location: str = None,
funding_stage: str = None,
founded_after: int = None
) -> dict:
"""
Search Indian startups by various criteria
"""
response = requests.post(
"https://fomoa.cloud/api/entities",
json={
"entity_type": "company",
"filters": {
"industry": industry,
"location": location,
"funding_stage": funding_stage,
"founded_after": founded_after,
"country": "India"
},
"include_details": True
},
headers={"Authorization": "Bearer your_key"}
)
return response.json()
# Example: AI startups in Bangalore founded after 2020
results = search_startups(
industry="Artificial Intelligence",
location="Bangalore",
founded_after=2020
)
Response Structure
{
"query_type": "company",
"filters_applied": {
"industry": "Artificial Intelligence",
"location": "Bangalore",
"founded_after": 2020
},
"total_results": 127,
"companies": [
{
"name": "ExampleAI",
"legal_name": "ExampleAI Technologies Pvt Ltd",
"cin": "U72900KA2021PTC123456",
"overview": {
"description": "AI-powered customer service automation platform",
"industry": "Artificial Intelligence",
"sub_industry": "Conversational AI",
"business_model": "B2B SaaS"
},
"location": {
"headquarters": "Bangalore",
"city": "Bengaluru",
"state": "Karnataka",
"country": "India",
"address": "HSR Layout, Sector 2"
},
"founding": {
"year": 2021,
"founders": [
{"name": "Founder 1", "linkedin": "linkedin.com/in/founder1"},
{"name": "Founder 2", "linkedin": "linkedin.com/in/founder2"}
]
},
"funding": {
"total_raised_usd": 5000000,
"total_raised_inr": "41.5 crore",
"last_funding_round": {
"stage": "Series A",
"amount_usd": 4000000,
"date": "2024-06-15",
"investors": ["Sequoia India", "Accel Partners"]
},
"funding_history": [
{"stage": "Seed", "amount_usd": 1000000, "date": "2022-03"},
{"stage": "Series A", "amount_usd": 4000000, "date": "2024-06"}
]
},
"metrics": {
"employee_count": "51-100",
"employee_growth_yoy": "45%",
"estimated_revenue_range": "$1M - $5M"
},
"online_presence": {
"website": "https://exampleai.com",
"linkedin": "https://linkedin.com/company/exampleai",
"twitter": "@exampleai"
},
"data_freshness": "2026-01-20",
"confidence_score": 0.92
}
]
}
Search Filters
By Industry
# Available industries
industries = [
"Artificial Intelligence",
"Fintech",
"EdTech",
"HealthTech",
"E-commerce",
"SaaS",
"D2C",
"AgriTech",
"CleanTech",
"Logistics",
"PropTech",
"HRTech",
"FoodTech",
"Gaming",
"Media & Entertainment",
"Enterprise Software",
"Cybersecurity",
"IoT",
"Blockchain",
"SpaceTech"
]
# Search fintech startups
fintech = search_startups(industry="Fintech")
By Location
# Major startup hubs
locations = [
"Bangalore", # 35% of Indian startups
"Delhi NCR", # 25%
"Mumbai", # 15%
"Hyderabad", # 8%
"Chennai", # 5%
"Pune", # 4%
"Kolkata", # 2%
"Ahmedabad", # 2%
"Jaipur",
"Kochi"
]
# Search startups in Mumbai
mumbai_startups = search_startups(location="Mumbai")
# Search in tier-2 cities
tier2_startups = search_startups(location="Jaipur")
By Funding Stage
# Funding stages
funding_stages = [
"Pre-seed",
"Seed",
"Series A",
"Series B",
"Series C",
"Series D+",
"Pre-IPO",
"Public",
"Bootstrapped"
]
# Find Series A startups
series_a = search_startups(funding_stage="Series A")
# Find bootstrapped profitable startups
bootstrapped = search_startups(funding_stage="Bootstrapped")
Combined Filters
# Complex search: AI startups in Bangalore
# with Series A funding, founded after 2022
results = requests.post(
"https://fomoa.cloud/api/entities",
json={
"entity_type": "company",
"filters": {
"industry": "Artificial Intelligence",
"location": "Bangalore",
"funding_stage": "Series A",
"founded_after": 2022,
"employee_count_min": 20
},
"sort_by": "funding_total",
"sort_order": "desc",
"limit": 50
}
)
Use Cases
For Investors
def find_investment_targets(criteria: dict) -> list:
"""
Find startups matching investment thesis
"""
response = requests.post(
"https://fomoa.cloud/api/entities",
json={
"entity_type": "company",
"filters": {
"industry": criteria["industry"],
"funding_stage": criteria["target_stage"],
"founded_after": criteria["min_founding_year"],
"employee_count_min": criteria.get("min_employees", 10)
},
"include_details": True
}
)
companies = response.json()["companies"]
# Score by investment criteria
scored = []
for company in companies:
score = calculate_investment_score(company, criteria)
if score > criteria.get("min_score", 0.7):
scored.append({
"company": company,
"score": score,
"thesis_fit": analyze_thesis_fit(company, criteria)
})
return sorted(scored, key=lambda x: x["score"], reverse=True)
# Example: Find SaaS companies for Series A investment
targets = find_investment_targets({
"industry": "SaaS",
"target_stage": "Seed", # Invest at Seed, target Series A
"min_founding_year": 2022,
"min_employees": 15,
"min_score": 0.8
})
For Job Seekers
def find_hiring_startups(
industry: str,
location: str,
min_employee_growth: float = 0.3
) -> list:
"""
Find fast-growing startups likely to be hiring
"""
response = requests.post(
"https://fomoa.cloud/api/entities",
json={
"entity_type": "company",
"filters": {
"industry": industry,
"location": location,
"funding_stage": ["Seed", "Series A", "Series B"],
"has_recent_funding": True # Funded in last 12 months
}
}
)
hiring_likely = []
for company in response.json()["companies"]:
growth = company.get("metrics", {}).get("employee_growth_yoy", "0%")
growth_rate = float(growth.replace("%", "")) / 100
if growth_rate >= min_employee_growth:
hiring_likely.append({
"name": company["name"],
"website": company["online_presence"]["website"],
"linkedin": company["online_presence"]["linkedin"],
"growth_rate": growth,
"employee_count": company["metrics"]["employee_count"],
"recent_funding": company["funding"]["last_funding_round"]
})
return hiring_likely
# Find hiring fintech startups in Bangalore
hiring = find_hiring_startups(
industry="Fintech",
location="Bangalore",
min_employee_growth=0.4
)
For Journalists
def get_funding_news(
time_period: str = "last_week",
min_amount_usd: int = 1000000
) -> list:
"""
Get recent funding announcements for news coverage
"""
response = requests.post(
"https://fomoa.cloud/api/entities",
json={
"entity_type": "funding_round",
"filters": {
"time_period": time_period,
"amount_min_usd": min_amount_usd,
"country": "India"
},
"sort_by": "amount",
"sort_order": "desc"
}
)
rounds = response.json()["funding_rounds"]
stories = []
for round in rounds:
stories.append({
"headline": f"{round['company_name']} raises ${round['amount_usd']/1000000:.1f}M in {round['stage']}",
"company": round["company_name"],
"amount": round["amount_usd"],
"investors": round["investors"],
"use_of_funds": round.get("announced_use_of_funds"),
"company_overview": round["company_overview"]
})
return stories
# Get this week's funding news
news = get_funding_news(time_period="last_week", min_amount_usd=5000000)
For Market Researchers
def industry_analysis(industry: str) -> dict:
"""
Get comprehensive industry analysis
"""
# Get all companies in industry
companies = requests.post(
"https://fomoa.cloud/api/entities",
json={
"entity_type": "company",
"filters": {"industry": industry, "country": "India"},
"limit": 500
}
).json()["companies"]
analysis = {
"total_companies": len(companies),
"total_funding_raised": sum(
c.get("funding", {}).get("total_raised_usd", 0)
for c in companies
),
"by_funding_stage": {},
"by_location": {},
"by_founding_year": {},
"top_funded": sorted(
companies,
key=lambda x: x.get("funding", {}).get("total_raised_usd", 0),
reverse=True
)[:10],
"recent_unicorns": [
c for c in companies
if c.get("funding", {}).get("total_raised_usd", 0) >= 100000000
]
}
# Aggregate by stage
for company in companies:
stage = company.get("funding", {}).get("last_funding_round", {}).get("stage", "Unknown")
analysis["by_funding_stage"][stage] = analysis["by_funding_stage"].get(stage, 0) + 1
# Aggregate by location
for company in companies:
location = company.get("location", {}).get("city", "Unknown")
analysis["by_location"][location] = analysis["by_location"].get(location, 0) + 1
return analysis
# Analyze Indian fintech landscape
fintech_analysis = industry_analysis("Fintech")
print(f"Total Fintech startups: {fintech_analysis['total_companies']}")
print(f"Total funding raised: ${fintech_analysis['total_funding_raised']/1000000000:.1f}B")
Natural Language Queries
FOMOA also supports natural language queries:
Query: "Find AI startups in Bangalore founded after 2022 with seed funding"
FOMOA parses this as:
{
"entity_type": "company",
"filters": {
"industry": "Artificial Intelligence",
"location": "Bangalore",
"founded_after": 2022,
"funding_stage": "Seed"
}
}
# Natural language API
response = requests.post(
"https://fomoa.cloud/api/answer",
json={
"query": "List top 10 edtech startups in India by funding",
"entity_search": True
}
)
# Returns formatted answer with company data
Data Coverage
FOMOA Indian Startup Database
=============================
Total companies indexed: 50,000+
├── Active startups: 35,000+
├── Inactive/Acquired: 15,000+
└── Unicorns: 110+
By Stage:
├── Pre-seed/Angel: 15,000+
├── Seed: 12,000+
├── Series A: 5,000+
├── Series B: 1,500+
├── Series C+: 800+
└── Bootstrapped: 15,000+
By Industry:
├── Fintech: 8,000+
├── E-commerce: 6,000+
├── EdTech: 4,500+
├── HealthTech: 3,000+
├── SaaS: 5,000+
└── Others: 23,500+
Data Freshness:
├── Funding data: Updated daily
├── Company profiles: Updated weekly
├── Employee data: Updated monthly
└── Financial data: Updated quarterly
Comparison with Alternatives
Feature Comparison
==================
Feature FOMOA Tracxn Crunchbase LinkedIn
------- ----- ------ ---------- --------
Price Free $500+/mo $29-199/mo Free
Indian coverage 50K+ 60K+ 20K+ Varies
API access Yes $$ Yes Limited
Funding data Yes Yes Yes No
Employee data Yes Yes Limited Yes
Financial data Basic Yes No No
Real-time updates Daily Daily Weekly -
Export Yes Yes Yes No
---
Access Indian startup intelligence without enterprise pricing.
Try FOMOA's startup search at fomoa.cloud.
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