AI Startup Suite — Data Dictionary & Sample Datasets (Projects 1

1. Startup Deal Flow Analyzer

Automates screening of startup pitches

Problem: Manual screening wastes time

Data Dictionary

Field	Type	Description	Example
record_id	INT PK	Unique record id	1001
startup_id	INT FK	Link to Startups.master	101
pitch_doc_url	VARCHAR	Location of pitch deck	/docs/pitch_101.pdf
deal_flow_score	FLOAT	AI score (0-100) of pitch quality	82.5
key_topics	TEXT	Extracted topics/keywords	"fintech, payments"
expected_input	JSON	Inputs used (deck, demo link, metrics)	'{"revenue":...}'
algorithm	VARCHAR	AI model used	"NLP Document Classifier"
status	ENUM	Processed / Pending / Error	Processed
last_run	DATETIME	Last scoring time	2025-11-05 10:12
remarks	TEXT	Auditor notes	"Good TAM evidence"

Mock Dataset

record_id	startup_id	deal_flow_score	key_topics	algorithm	status
1001	101	82.5	fintech;payments;KYC	NLP-Doc-1	Processed
1002	102	67.2	agritech;supply-chain	NLP-Doc-1	Processed
1003	103	45.8	edtech;retention	NLP-Doc-1	Processed
1004	104	90.1	cleantech;solar	NLP-Doc-1	Processed

2. Investor–Startup Matchmaking

Matches startups with right investors

Problem: Misaligned funding opportunities

Data Dictionary

Field	Type	Description	Example
record_id	INT PK	Unique match id	2001
startup_id	INT	Startup	101
investor_id	INT	Investor	501
match_score	FLOAT	Compatibility score 0-100	87.3
stage_fit	VARCHAR	Seed/Series A/etc.	Series A
sector_overlap	FLOAT	Sector similarity 0-1	0.84
algorithm	VARCHAR	Recommender model	"MatrixFactor"
notes	TEXT	Advisor remarks	"Lead interest"

Mock Dataset

record_id	startup_id	investor_id	match_score	stage_fit
2001	101	501	87.3	Series A
2002	102	502	78.0	Seed
2003	103	503	65.5	Pre-Seed
2004	104	504	92.1	Series B

3. AI Due Diligence Assistant

Automates startup background checks

Problem: Tedious manual due diligence

Data Dictionary

Field	Type	Description	Example
record_id	INT	Check id	3001
startup_id	INT	Target startup	101
db_checks	JSON	Results from registry, litigation, tax	'{"litigation":false}'
score_completeness	FLOAT	Data completeness 0-100	92.0
fraud_flags	INT	Number of flags	0
report_url	VARCHAR	DD report	/reports/dd_3001.pdf
analyst	VARCHAR	Human reviewer	"R. Sharma"

Mock Dataset

record_id	startup_id	score_completeness	fraud_flags	report_url
3001	101	92.0	0	/reports/dd_3001.pdf
3002	103	74.5	1	/reports/dd_3002.pdf
3003	104	88.9	0	/reports/dd_3003.pdf
3004	105	55.2	3	/reports/dd_3004.pdf

4. Family Office Impact Tracker

Tracks investments & social impact

Problem: No structured way to measure impact

Data Dictionary

Field	Type	Description	Example
record_id	INT	Impact record id	4001
startup_id	INT	Recipient startup	101
investor_id	INT	Family office id	701
sdg_aligned	VARCHAR	Primary SDG tag	SDG7
impact_score	FLOAT	Impact metric 0-100	75.4
measurement_period	VARCHAR	Quarter/Year	Q3-2025
evidence_docs	TEXT	Links to evidence	"/evidence/..."

Mock Dataset

record_id	startup_id	investor_id	sdg_aligned	impact_score
4001	110	701	SDG7	75.4
4002	115	702	SDG3	82.0
4003	120	703	SDG6	64.5
4004	101	701	SDG9	71.2

5. Angel Network Dashboard

Unified view of deals & returns

Problem: Fragmented systems

Data Dictionary

Field	Type	Description	Example
dashboard_id	INT	Dashboard record	5001
syndicate_id	INT	Angel group	801
deals_count	INT	Total deals	18
portfolio_return	FLOAT	IRR or ROI	24.5
exposure_sector	TEXT	Sector breakdown	"fintech:40%,health:30%"
last_refresh	DATETIME	Refresh timestamp	2025-11-04 09:22

Mock Dataset

dashboard_id	syndicate_id	deals_count	portfolio_return
5001	801	18	24.5
5002	802	22	18.2
5003	803	12	30.1
5004	804	9	12.0

6. Startup Valuation Predictor

Predicts fair valuation of startups

Problem: Over/undervaluation in funding

Data Dictionary

Field	Type	Description	Example
valuation_id	INT	Valuation record	6001
startup_id	INT	Startup	101
predicted_valuation	FLOAT	Predicted valuation (₹ Cr)	18.5
method	VARCHAR	Model/approach	"Regression-ensemble"
confidence	FLOAT	Confidence %	88.6
features_used	TEXT	Features	"rev, growth, churn"
last_run	DATETIME	Run timestamp	2025-11-03 11:00

Mock Dataset

valuation_id	startup_id	predicted_valuation	confidence
6001	101	18.5	88.6
6002	102	7.2	76.4
6003	103	3.1	62.1
6004	104	45.0	92.0

7. Fraud Detection Engine

Detects fake startups & scams

Problem: Fake data in funding

Data Dictionary

Field	Type	Description	Example
fraud_id	INT	Fraud event id	7001
startup_id	INT	Startup analyzed	110
anomaly_score	FLOAT	0-100 anomaly score	92.0
graph_flags	INT	Number of suspicious links in graph	3
evidence	TEXT	Summary of anomalies	"revenue mismatch"
detected_by	VARCHAR	Model name	"PyTorch-AnomV1"
action	VARCHAR	Suggested action	"Escalate to DD"

Mock Dataset

fraud_id	startup_id	anomaly_score	graph_flags	action
7001	201	92	4	Escalate
7002	202	65	1	Monitor
7003	203	28	0	No action
7004	204	80	2	Deep Audit

8. AI-powered Term Sheet Generator

Auto-generates legal investment docs

Problem: Legal delays

Data Dictionary

Field	Type	Description	Example
ts_id	INT	Term sheet id	8001
deal_id	INT	Linked deal	4001
ts_version	INT	Version number	1
clauses	TEXT	Generated clauses	"vesting:4yr..."
generated_by	VARCHAR	LLM name	"GPT-TermGen-1"
signed_status	ENUM	Pending/Partially/Complete	Pending

Mock Dataset

ts_id	deal_id	ts_version	generated_by	signed_status
8001	4001	1	GPT-TermGen-1	Pending
8002	4002	1	GPT-TermGen-1	Complete
8003	4003	2	GPT-TermGen-2	Pending
8004	4004	1	GPT-TermGen-1	Partially

9. Investor Portfolio Optimizer

Recommends portfolio balancing

Problem: Poor diversification

Data Dictionary

Field	Type	Description	Example
opt_id	INT	Optimizer run id	9001
investor_id	INT	Investor	501
allocation_suggestion	JSON	Suggested allocations by startup	'{"101":45,"102":30}'
expected_return	FLOAT	Projected ROI%	18.2
risk_tolerance	VARCHAR	Low/Med/High	Medium
solver	VARCHAR	Algorithm used	"OR-Tools-LP"

Mock Dataset

opt_id	investor_id	allocation_suggestion	expected_return
9001	501	{"101":45,"102":30,"103":25}	18.2
9002	502	{"104":60,"105":40}	14.5
9003	503	{"106":20,"107":80}	22.1
9004	504	{"108":50,"109":50}	16.0

10. AI News Sentiment Tracker

Tracks market & startup news

Problem: Noise in news data

Data Dictionary

Field	Type	Description	Example
news_id	INT	Article id	10001
startup_id	INT	Related startup	101
source	VARCHAR	News source	Reuters
publish_date	DATE	Article date	2025-11-02
sentiment_score	FLOAT	-1 to +1	0.45
topics	TEXT	Extracted topics	"funding,partnership"
summary	TEXT	Auto summary	"Raised $5M..."

Mock Dataset

news_id	startup_id	source	sentiment_score	summary
10001	101	Reuters	0.45	Raised $5M seed
10002	102	TechCrunch	-0.12	Layoffs announced
10003	103	EconomicTimes	0.25	New partnership
10004	104	LocalNews	0.00	Neutral coverage

11. MIS Deal Flow Heatmap

Geographic startup deal mapping

Problem: Lack of visibility by region

Data Dictionary

Field	Type	Description	Example
heatmap_id	INT	Record id	11001
region	VARCHAR	Region name	Mumbai
deals_count	INT	No. deals	12
avg_deal_size	FLOAT	Avg amount (₹ Cr)	3.2
geo_coords	VARCHAR	lat,long	"18.96,72.82"
last_update	DATETIME	Refresh	2025-11-05

Mock Dataset

heatmap_id	region	deals_count	avg_deal_size
11001	Mumbai	12	3.2
11002	Bengaluru	22	5.1
11003	Delhi NCR	18	4.0
11004	Chennai	6	1.2

12. Social Impact Scorecard

Measures startup’s ESG alignment

Problem: No quantifiable metrics

Data Dictionary

Field	Type	Description	Example
esg_id	INT	ESG score id	12001
startup_id	INT	Startup	101
environment_score	FLOAT	0-100	78.2
social_score	FLOAT	0-100	69.4
governance_score	FLOAT	0-100	72.0
composite_esg	FLOAT	Weighted	73.2
evidence_docs	TEXT	Proof	"/evidence/esg_12001.pdf"

Mock Dataset

esg_id	startup_id	composite_esg	evidence_docs
12001	101	73.2	/evidence/esg_12001.pdf
12002	115	81.0	/evidence/esg_12002.pdf
12003	130	55.1	/evidence/esg_12003.pdf
12004	140	66.4	/evidence/esg_12004.pdf

13. Founders Mental Health Monitor

AI-based well-being support

Problem: Founder burnout

Data Dictionary

Field	Type	Description	Example
mh_id	INT	Record id	13001
founder_id	INT	Founder	901
stress_score	FLOAT	0-100	72.5
mood_trend	TEXT	Weekly mood summary	"anxiety trending"
intervention	VARCHAR	Suggested action	Coaching
last_checkin	DATE	Last assessment	2025-11-01

Mock Dataset

mh_id	founder_id	stress_score	intervention
13001	901	72.5	Coaching
13002	902	45.2	Wellness plan
13003	903	28.1	Monitoring
13004	904	85.4	Immediate help

14. Startup Exit Predictor

Predicts likelihood of startup exits

Problem: Investors unsure of ROI

Data Dictionary

Field	Type	Description	Example
exit_id	INT	Prediction id	14001
startup_id	INT	Startup	101
exit_prob	FLOAT	0-1 probability	0.28
predicted_horizon_months	INT	Months to exit	36
model_used	VARCHAR	Survival/ML	Survival-Prophet
confidence	FLOAT	Confidence %	78.2

Mock Dataset

exit_id	startup_id	exit_prob	predicted_horizon_months
14001	101	0.28	36
14002	102	0.12	60
14003	103	0.45	18
14004	104	0.05	120

15. AI Market Sizing Tool

Automates TAM, SAM, SOM calculations

Problem: Market sizing guesswork

Data Dictionary

Field	Type	Description	Example
market_id	INT	Record id	15001
sector	VARCHAR	Sector	Fintech
TAM	FLOAT	Total addressable market	12000
SAM	FLOAT	Serviceable addressable	3200
SOM	FLOAT	Share of market	300
methodology	TEXT	Sources & assumptions	"Gov data, analyst"

Mock Dataset

market_id	sector	TAM	SAM	SOM
15001	Fintech	12000	3200	300
15002	HealthTech	8000	1500	120
15003	EduTech	5000	900	70
15004	CleanTech	20000	4500	500

16. Family Office Co-investment Finder

Identifies co-investment opportunities

Problem: Families invest in silos

Data Dictionary

Field	Type	Description	Example
match_id	INT	Co-invest match id	16001
family_office_id	INT	Family office	701
potential_partner_id	INT	Other investor	702
overlap_score	FLOAT	0-100 fit score	82.1
preferred_sectors	TEXT	Sector match	"agritech;cleantech"

Mock Dataset

match_id	family_office_id	potential_partner_id	overlap_score
16001	701	702	82.1
16002	703	704	71.5
16003	705	706	65.0
16004	707	708	88.2

17. AI Patent Scanner

Validates startup IP

Problem: Duplicate/invalid patents

Data Dictionary

Field	Type	Description	Example
patent_id	INT	Patent record	17001
startup_id	INT	Owner	101
patent_text	TEXT	Claim text	"An apparatus..."
similarity_score	FLOAT	Similarity to existing patents	0.82
status	ENUM	Unique/Duplicate/Review	Review
matched_patents	TEXT	Matches found	"US12345;EP6789"

Mock Dataset

patent_id	startup_id	similarity_score	status
17001	201	0.12	Unique
17002	202	0.78	Review
17003	203	0.95	Duplicate
17004	204	0.34	Unique

18. Startup Risk Heatmap

Dynamic risk analysis

Problem: Risk often hidden

Data Dictionary

Field	Type	Description	Example
riskmap_id	INT	Record id	18001
startup_id	INT	Startup	101
financial_risk	FLOAT	0-100	34
operational_risk	FLOAT	0-100	45
composite_risk	FLOAT	Weighted index	38.5
heatmap_region	VARCHAR	Region	West

Mock Dataset

riskmap_id	startup_id	composite_risk	heatmap_region
18001	101	38.5	West
18002	102	55.2	South
18003	103	22.1	North
18004	104	72.4	East

19. AI Fund Allocation Planner

Optimizes fund allocation

Problem: Poor allocation strategies

Data Dictionary

Field	Type	Description	Example
plan_id	INT	Allocation plan id	19001
investor_id	INT	Investor	501
total_fund	FLOAT	Total fund (₹ Lakhs)	20000
allocations	JSON	Allocations per startup	'{"101":4500}'
objective	VARCHAR	Max ROI / Min Risk	Max ROI
solver	VARCHAR	OR-Tools / custom	OR-Tools

Mock Dataset

plan_id	investor_id	total_fund	allocations
19001	501	20000	{"101":4500,"102":5500,"103":10000}
19002	502	10000	{"104":4000,"105":6000}
19003	503	5000	{"106":2500,"107":2500}
19004	504	15000	{"108":8000,"109":7000}

20. Karmic Startup Karma Tracker

Tracks good/bad impact (PRUTL)

Problem: Only profits tracked

Data Dictionary

Field	Type	Description	Example
karma_id	INT	Karma record id	20001
startup_id	INT	Startup	101
positive_soul	FLOAT	Positive soul metric (0-100)	55
negative_soul	FLOAT	Negative soul metric	12
materialism_score	FLOAT	Materialism composite	40
karma_index	FLOAT	Composite karma score	63
notes	TEXT	Ethical remarks	"Supports recycling"

Mock Dataset

karma_id	startup_id	karma_index	notes
20001	101	63	Supports recycling
20002	102	42	High materialism
20003	103	75	Community programs
20004	104	30	Profit-first

21. Cyber Risk Monitor for Startups

Tracks startup’s cyber posture

Problem: Investors blind to cyber risk

Data Dictionary

Field	Type	Description	Example
cyber_id	INT	Cyber record id	21001
startup_id	INT	Startup	201
threat_score	FLOAT	0-100	56
vuln_count	INT	No. vulnerabilities	12
compliance_iso27001	BOOL	ISO 27001 certified	true
recommendation	TEXT	Suggested actions	"Enable MFA"

Mock Dataset

cyber_id	startup_id	threat_score	vuln_count
21001	201	56	12
21002	202	82	24
21003	203	28	3
21004	204	41	7

22. Startup Hiring Analyzer

Predicts hiring needs & gaps

Problem: Mis-hiring by startups

Data Dictionary

Field	Type	Description	Example
hire_id	INT	Record id	22001
startup_id	INT	Startup	301
current_headcount	INT	Employees	23
predicted_optimal	INT	AI recommended headcount	28
gap	INT	Difference	5
attrition_risk	FLOAT	% attrition risk	12.4
recommendation	TEXT	Hiring plan	"Hire 2 DevOps"

Mock Dataset

hire_id	startup_id	current_headcount	predicted_optimal	gap
22001	301	23	28	5
22002	302	12	12	0
22003	303	8	14	6
22004	304	5	5	0

23. Investor Bias Detector

Detects gender/regional bias

Problem: Biased funding

Data Dictionary

Field	Type	Description	Example
bias_id	INT	Bias record id	23001
investor_id	INT	Investor	501
metric	VARCHAR	Gender/Region/Sector	Gender
bias_score	FLOAT	0-100 fairness index	46
explanation	TEXT	Why bias flagged	"Favors metro founders"
mitigation	TEXT	Recommended fix	"Blind evaluation"

Mock Dataset

bias_id	investor_id	metric	bias_score
23001	501	Gender	46
23002	502	Region	62
23003	503	Sector	84
23004	504	Gender	71

24. AI Pitch Voice Analyzer

Analyzes founder pitch tone

Problem: Subjective judging

Data Dictionary

Field	Type	Description	Example
pitch_id	INT	Record id	24001
founder_id	INT	Founder	901
avg_pitch_hz	FLOAT	Average pitch	215
confidence_score	FLOAT	0-100	89
emotion	VARCHAR	Detected emotion	Calm
fairness_score	FLOAT	Bias adjusted score	92

Mock Dataset

pitch_id	founder_id	avg_pitch_hz	confidence_score	fairness_score
24001	901	215	89	92
24002	902	140	76	68
24003	903	230	59	52
24004	904	125	72	64

25. AI Startup Growth Path Simulator

Simulates future growth scenarios

Problem: No clarity of projections

Data Dictionary

Field	Type	Description	Example
sim_id	INT	Simulation id	25001
startup_id	INT	Startup	101
scenario	VARCHAR	Best/Base/Worst	Base
projected_revenue_3yr	FLOAT	₹ Cr	27.7
projected_revenue_5yr	FLOAT	₹ Cr	58.1
confidence	FLOAT	%	89

Mock Dataset

sim_id	startup_id	scenario	projected_revenue_3yr	confidence
25001	101	Base	27.7	89
25002	102	Best	30.4	85
25003	103	Worst	15.1	74
25004	104	Base	14.2	69

26. AI-Powered Startup Watchlist

Flags startups needing attention

Problem: Post-investment neglect

Data Dictionary

Field	Type	Description	Example
watch_id	INT	Watchlist id	26001
startup_id	INT	Startup	201
anomaly_score	FLOAT	0-1	0.83
health_index	FLOAT	%	62
risk_level	VARCHAR	High/Medium/Low	High
justification	TEXT	AI explanation	Rapid cash burn

Mock Dataset

watch_id	startup_id	anomaly_score	health_index	risk_level
26001	201	0.83	62	High
26002	202	0.55	78	Medium
26003	203	0.24	90	Low
26004	204	0.41	70	Medium

27. SDG Startup Alignment Dashboard

Classifies startups to SDGs

Problem: ESG ignored in funding

Data Dictionary

Field	Type	Description	Example
sdg_id	INT	Record id	27001
startup_id	INT	Startup	110
primary_sdg	VARCHAR	Main SDG	SDG7
alignment_score	FLOAT	0-1	0.91
esg_impact_index	FLOAT	%	88
notes	TEXT	Assessment details	"Solar microgrids"

Mock Dataset

sdg_id	startup_id	primary_sdg	alignment_score	esg_impact_index
27001	110	SDG7	0.91	88
27002	115	SDG3	0.74	79
27003	120	SDG4	0.64	72
27004	130	SDG6	0.42	55

28. AI Competitor Landscape Mapper

Maps startup vs competitors

Problem: Founders lack data

Data Dictionary

Field	Type	Description	Example
comp_id	INT	Record id	28001
startup_id	INT	Startup	101
competitor	VARCHAR	Competitor name	PayEase
overlap_pct	FLOAT	Market overlap %	80
product_similarity	VARCHAR	High/Medium/Low	High
ai_insight	TEXT	Recommendation	"Focus B2B"

Mock Dataset

comp_id	startup_id	competitor	overlap_pct	ai_insight
28001	101	PayEase	80	Focus B2B micro-payments
28002	102	FarmNet	65	Introduce IoT sensors
28003	103	HealthNext	50	Enhance explainability
28004	104	GreenHive	70	Target EU grants

29. Startup Gender Diversity Analyzer

Measures diversity in teams

Problem: Diversity under-reported

Data Dictionary

Field	Type	Description	Example
div_id	INT	Record id	29001
startup_id	INT	Startup	120
total_employees	INT	Total	120
male_pct	FLOAT	% male	70
female_pct	FLOAT	% female	28
nonbinary_pct	FLOAT	%	2
diversity_score	FLOAT	0-10	6.4

Mock Dataset

div_id	startup_id	male_pct	female_pct	diversity_score
29001	120	70	28	6.4
29002	121	60	38	7.9
29003	122	80	19	5.2
29004	123	55	43	8.8

30. Investor Relations Chatbot

Handles FAQs for investors

Problem: Manual queries

Data Dictionary

Field	Type	Description	Example
faq_id	INT	FAQ record	30001
question	TEXT	Investor question	"Valuation?"
answer	TEXT	AI response	"$15M as of Q3 2025"
category	VARCHAR	Financial/Resources/etc	Financial
usage_count	INT	Times asked	123
last_updated	DATETIME	Update timestamp	2025-11-05

Mock Dataset

faq_id	question	answer	category
30001	What is current valuation?	Our latest valuation is $15M.	Financial
30002	Who are lead investors?	BluePeak Ventures.	Funding
30003	View pitch deck?	Investor Portal > Documents	Resources
30004	Next update date?	Dec 15, 2025	Scheduling

31. Startup Carbon Footprint Tracker

Tracks emissions

Problem: No carbon accounting

Data Dictionary

Field	Type	Description	Example
carbon_id	INT	Record id	31001
site	VARCHAR	Location	Office HQ
energy_kwh	FLOAT	Energy used	320
waste_kg	FLOAT	Waste	18
vehicle_co2	FLOAT	Vehicle CO2 (kg)	56
total_co2	FLOAT	Total CO2 (kg)	394
status	VARCHAR	Optimal/Moderate/High	Above Target

Mock Dataset

carbon_id	site	energy_kwh	total_co2	status
31001	Office HQ	320	394	Above Target
31002	Manufacturing	1240	1512	High
31003	Remote Office	180	197	Optimal
31004	R&D Center	480	577	Moderate

32. AI Startup Scalability Analyzer

Measures ability to scale

Problem: Investors miss signals

Data Dictionary

Field	Type	Description	Example
scal_id	INT	Record id	32001
startup_id	INT	Startup	401
current_users	INT	User base	15000
monthly_growth_pct	FLOAT	Monthly growth %	22
automation_level	FLOAT	% automation	87
ai_scalability_score	FLOAT	0-100	91
status	VARCHAR	Scale class	Highly Scalable

Mock Dataset

scal_id	startup_id	monthly_growth_pct	ai_scalability_score	status
32001	401	22	91	Highly Scalable
32002	402	12	74	Moderate
32003	403	6	59	Needs Optimization
32004	404	28	96	Excellent

33. AI-Powered MIS Reporting

Auto-generates MIS reports

Problem: Manual report prep

Data Dictionary

Field	Type	Description	Example
mis_id	INT	MIS record id	33001
startup_id	INT	Startup	101
revenue_lacs	FLOAT	Revenue	120
expense_lacs	FLOAT	Expenses	80
profit_pct	FLOAT	Profit %	33
ai_summary	TEXT	Auto-generated summary	"Strong Q3 growth"
last_generated	DATETIME	Timestamp	2025-11-05

Mock Dataset

mis_id	startup_id	revenue_lacs	profit_pct	ai_summary
33001	101	120	33	Strong Q3 growth
33002	102	95	26	Stable performance
33003	103	60	-8	Operational losses
33004	104	200	40	Outstanding quarter

34. Investor Deal Syndication Manager

Helps manage syndicates

Problem: Coordination is messy

Data Dictionary

Field	Type	Description	Example
synd_id	INT	Syndicate id	34001
lead_investor	INT	Lead investor id	801
co_investors	TEXT	List of co-investors	"802,803"
deals_managed	INT	No. deals	18
avg_turnaround_days	INT	Avg days	9
workflow_index	FLOAT	Efficiency score	91

Mock Dataset

synd_id	lead_investor	co_investors	deals_managed
34001	801	802,803,804	18
34002	805	806,807	22
34003	808	809	15
34004	810	811,812	12

35. AI Liquidity Risk Model

Predicts liquidity crunch

Problem: Investors blindsided

Data Dictionary

Field	Type	Description	Example
liq_id	INT	Record id	35001
investor_id	INT	Investor	501
predicted_shortfall	FLOAT	Amount (₹ Lacs)	500
horizon_months	INT	Months	6
confidence	FLOAT	%	82
mitigation	TEXT	Suggested actions	"Increase reserve"

Mock Dataset

liq_id	investor_id	predicted_shortfall	horizon_months
35001	501	500	6
35002	502	1200	3
35003	503	200	12
35004	504	50	18

36. Startup Financial Integrity Checker

Validates accounting integrity

Problem: Fraudulent accounts

Data Dictionary

Field	Type	Description	Example
int_id	INT	Integrity id	36001
startup_id	INT	Startup	201
cashflow_match_pct	FLOAT	% match	97
anomaly_score	FLOAT	0-100	48
integrity_index	FLOAT	0-100	88
remarks	TEXT	Details	"Round-off manipulations"

Mock Dataset

int_id	startup_id	cashflow_match_pct	anomaly_score
36001	201	97	8
36002	202	89	23
36003	203	72	52
36004	204	95	12

37. AI Social Media Perception Tracker

Monitors startup buzz

Problem: No real-time tracking

Data Dictionary

Field	Type	Description	Example
sm_id	INT	Record id	37001
startup_id	INT	Startup	101
mentions_count	INT	Mentions in window	320
avg_sentiment	FLOAT	-1..+1	0.12
top_sources	TEXT	Source list	"Twitter,LinkedIn"
trend_flag	VARCHAR	Up/Down/Stable	Up

Mock Dataset

sm_id	startup_id	mentions_count	avg_sentiment
37001	101	320	0.12
37002	102	150	-0.20
37003	103	78	0.45
37004	104	12	0.00

38. Early Warning System

Flags failing startups early

Problem: Investors shocked

Data Dictionary

Field	Type	Description	Example
ews_id	INT	EWS record id	38001
startup_id	INT	Startup	201
risk_score	FLOAT	0-100	70
flag_reason	TEXT	Trigger reason	"cash burn spike"
alert_level	VARCHAR	Low/Med/High	High
notified	BOOL	Investors notified	true

Mock Dataset

ews_id	startup_id	risk_score	alert_level
38001	201	70	High
38002	202	35	Medium
38003	203	22	Low
38004	204	55	Medium

39. AI Grant Mapping Tool

Maps startups to grants

Problem: Startups miss govt. grants

Data Dictionary

Field	Type	Description	Example
grant_map_id	INT	Record id	39001
startup_id	INT	Startup	101
grant_id	INT	Grant	5001
match_score	FLOAT	0-1	0.87
sdg_relevance	VARCHAR	SDG tag	SDG7
apply_url	VARCHAR	Application link	https://gov/grant/5001

Mock Dataset

grant_map_id	startup_id	grant_id	match_score
39001	101	5001	0.87
39002	102	5002	0.56
39003	103	5003	0.93
39004	104	5004	0.42

40. AI Investor Education Portal

Teaches new investors

Problem: Angel investors lack tools

Data Dictionary

Field	Type	Description	Example
module_id	INT	Module id	40001
title	VARCHAR	Module title	"Startup Valuation 101"
duration_mins	INT	Length	45
level	VARCHAR	Beginner/Intermediate	Beginner
content_url	VARCHAR	Resource link	/modules/val101
usage_stats	JSON	Completions, scores	'{"completions":120}'

Mock Dataset

module_id	title	duration_mins	level
40001	Startup Valuation 101	45	Beginner
40002	Due Diligence Essentials	60	Intermediate
40003	Portfolio Diversification	30	Beginner
40004	Term Sheet Key Terms	50	Intermediate

41. AI Deal Room

Centralized negotiation room

Problem: Scattered processes

Data Dictionary

Field	Type	Description	Example
dealroom_id	INT	Room id	41001
deal_id	INT	Deal linked	4001
documents	TEXT	Doc list	"term_v1.pdf"
status	VARCHAR	Negotiation status	Pending
participants	TEXT	List of users	"founder,lead_inv,legal"
last_activity	DATETIME	Last action	2025-11-05

Mock Dataset

dealroom_id	deal_id	status	participants
41001	4001	Approved	founder,alphaVent
41002	4002	Pending	founder,ecoFund
41003	4003	Rejected	founder,mediCap
41004	4004	In Review	founder,investClub

42. Startup Culture Analyzer

Monitors startup culture

Problem: Investors ignore team health

Data Dictionary

Field	Type	Description	Example
culture_id	INT	Culture record id	42001
startup_id	INT	Startup	501
trust_score	FLOAT	0-100	82
communication_score	FLOAT	0-100	77
innovation_score	FLOAT	0-100	85
leadership_score	FLOAT	0-100	73
worklife_balance_score	FLOAT	0-100	80
overall_culture_index	FLOAT	Composite	79.4
scan_date	DATE	Last assessment	2025-11-03

Mock Dataset

culture_id	startup_id	overall_culture_index	scan_date
42001	501	79.4	2025-11-03
42002	502	68.2	2025-10-20
42003	503	85.1	2025-11-01
42004	504	55.0	2025-09-30

43. Blockchain Investment Ledger

Transparent recordkeeping

Problem: Trust issues in funds

Data Dictionary

Field	Type	Description	Example
ledger_txn	VARCHAR	Blockchain txn id	0xabc123
deal_id	INT	Deal linked	4001
amount	FLOAT	Amount	12.5
timestamp	DATETIME	Txn time	2025-11-05
status	VARCHAR	Confirmed/Pending	Confirmed

Mock Dataset

ledger_txn	deal_id	amount	status
0xabc123	4001	12.5	Confirmed
0xdef456	4002	4.2	Confirmed
0xghi789	4003	8.0	Pending
0xjkl012	4004	25.0	Confirmed

44. AI-Enabled Exit Strategy Recommender

Suggests exit strategies

Problem: Investors lack strategy

Data Dictionary

Field	Type	Description	Example
exitrec_id	INT	Record id	44001
startup_id	INT	Startup	101
recommended_strategy	VARCHAR	M&A/IPO/Liquidation	M&A
rationale	TEXT	Why recommended	"Strong revenue growth"
estimated_value	FLOAT	Expected exit value	58

Mock Dataset

exitrec_id	startup_id	recommended_strategy	estimated_value
44001	101	M&A	58
44002	102	IPO	120
44003	103	Acquire	30
44004	104	Hold	15

45. AI Fundraising Coach

Prepares founders for pitches

Problem: Poor pitch skills

Data Dictionary

Field	Type	Description	Example
coach_id	INT	Session id	45001
founder_id	INT	Founder	901
session_score	FLOAT	Performance	84
feedback	TEXT	AI coaching notes	"Clear ask needed"
module	VARCHAR	Topic	Storytelling

Mock Dataset

coach_id	founder_id	session_score	module
45001	901	84	Storytelling
45002	902	68	Financials
45003	903	91	Pitch Demo
45004	904	72	Q&A

46. AI LP/GP Transparency Dashboard

Tracks Limited Partner visibility

Problem: Opaque structures

Data Dictionary

Field	Type	Description	Example
lp_id	INT	LP/GP record	46001
fund_id	INT	Fund	9001
visibility_index	FLOAT	Transparency score	72
reports_available	BOOL	Reports accessible	true
last_update	DATETIME	Timestamp	2025-11-05

Mock Dataset

lp_id	fund_id	visibility_index	reports_available
46001	9001	72	true
46002	9002	55	false
46003	9003	81	true
46004	9004	65	true

47. AI-powered Crowdfunding Monitor

Tracks crowdfunding campaigns

Problem: Fraud & inefficiency

Data Dictionary

Field	Type	Description	Example
cf_id	INT	Campaign id	47001
startup_id	INT	Startup	701
raised_amount	FLOAT	Amount	25
backers_count	INT	No. backers	1200
fraud_risk	FLOAT	0-100	12
status	VARCHAR	Active/Closed	Active

Mock Dataset

cf_id	startup_id	raised_amount	backers_count
47001	701	25	1200
47002	702	3.2	200
47003	703	0.8	50
47004	704	12	600

48. AI Angel Network Health Index

Tracks health of angel groups

Problem: No visibility

Data Dictionary

Field	Type	Description	Example
ang_idx_id	INT	Index record	48001
syndicate_id	INT	Syndicate	801
health_score	FLOAT	0-100	78
activity_level	INT	Deals per year	12
trust_index	FLOAT	Internal trust metric	82

Mock Dataset

ang_idx_id	syndicate_id	health_score	activity_level
48001	801	78	12
48002	802	85	22
48003	803	64	6
48004	804	90	30

49. AI-driven Startup Knowledge Graph

Connects startups, investors, sectors

Problem: Disconnected data

Data Dictionary

Field	Type	Description	Example
kg_node_id	INT	Node id	49001
node_type	VARCHAR	Startup/Investor/Patent	Startup
node_label	VARCHAR	Name	FinSync
relations	TEXT	Edges	"invests_in,cofounder"
last_indexed	DATETIME	Timestamp	2025-11-05

Mock Dataset

kg_node_id	node_type	node_label	relations
49001	Startup	FinSync	invested_by:501
49002	Investor	BluePeak	invests_in:101,102
49003	Patent	US12345	owned_by:201
49004	Founder	Riya Mehta	cofounder_of:101

50. AI MIS + Karma Dashboard

Unified dashboard linking PCOMBINATOR, Family Office, Angels

Problem: Multiple disconnected views

Data Dictionary

Field	Type	Description	Example
dash_id	INT	Dashboard id	50001
module_refs	TEXT	Linked modules	"MIS,ESG,Watchlist"
user_scope	VARCHAR	User or org	FamilyOfficeXYZ
last_sync	DATETIME	Sync timestamp	2025-11-05
summary_indices	JSON	Aggregated scores	'{"esg":73,"risk":34}'

Mock Dataset

dash_id	module_refs	user_scope	summary_indices
50001	MIS,ESG,Watchlist	FamilyOfficeXYZ	{"esg":73,"risk":34}
50002	DealFlow,Valuation	AngelNetworkA	{"deal_flow":82,"valuation_conf":88}
50003	Watchlist,ExitPred	FamilyOfficeB	{"watch_health":62,"exit_prob":0.28}
50004	GrantMap,SDG	GovUnit	{"sdg_coverage":0.78}