SemantaAI/semanta-dataset-suite

Semanta Dataset Suite

Semanta is a World Intelligence Operating System. This package is a market-facing proof that Semanta can generate large, structured, scenario-covered synthetic worlds for many industries, not just one narrow demo dataset.

Start Here

Need	Open
See the public product surface	https://semanta.xyz
Run the no-login proof bundle	https://api-staging.semanta.xyz/public/proof-bundle?rows=64&seed=37
Open the operator-style Guided Demo	https://studio-staging.semanta.xyz
Inspect the industry-suite API proof	https://api-staging.semanta.xyz/public/industry-dataset-suite
Read API docs	https://api-staging.semanta.xyz/docs
Download the enterprise brief	docs/Semanta_Enterprise_Brief.pdf
Download the investor overview	docs/Semanta_Investor_Overview.pdf

Customer / Investor Proof Path

Step	Proof
1	Open semanta.xyz and check the public 13/13 proof gate.
2	Open Studio and run Guided Demo for the live public-safe workflow.
3	Inspect this HF branch for the published dataset suite and quality metrics.
4	Open the proof bundle for machine-readable evidence, lineage and claim boundaries.

Executive Snapshot

Metric	Value
Run ID	industry-suite-20260618-01
Canon	v3.7 Final
Total published synthetic rows in repo	2,370,621
Industry vertical suite rows	1,100,000
Industries	11
Rows	1,100,000
Columns per industry	100
Total cells	110,000,000
Overall quality score	0.965
Scenario coverage score	1.000
Production smoke gate	13/13 public proof targets passed
Synthetic-only	yes
External provider used	no

What This Proves

• Semanta can produce broad, structured, HF-compatible synthetic datasets, not only narrow demo files.
• Every industry pack ships with data, schema, quality metrics, lineage and reproducibility evidence.
• The public proof path currently passes 13/13 production smoke targets, including API health, public demo, proof bundle, Studio Guided Demo contracts, landing and HF README.
• The suite is synthetic-only: no customer data is used, and no customer data is sent to DeepSeek, Gamma or any external model provider.
• The data is designed for ML/DL/NN/Q experiments, scenario robustness, drift prototypes and StarForge/Gamma training handoff.

What You Can Download

Artifact	Why it matters
data/industry_vertical_suite/<industry>/*.csv.gz	Training-ready synthetic tabular worlds by industry.
schemas/industry_vertical_suite/*.json	Machine-readable column contracts and semantic types.
metrics/industry_vertical_suite/*_quality_metrics.json	Per-vertical statistical quality and coverage metrics.
metrics/industry_vertical_suite/suite_scorecard.json	Executive suite-level readiness and quality scorecard.
semanta/industry_vertical_suite/lineage.json	Reproducibility and source-policy evidence.
semanta/industry_vertical_suite/publication_manifest.json	Publication manifest, files and claim boundaries.
docs/Semanta_Enterprise_Brief.pdf	Lightweight enterprise/investor PDF explaining Semanta capabilities.
docs/Semanta_Investor_Overview.pdf	Short investor-facing Semanta overview.

Market Positioning

A company's real data is often one observed degree out of a much larger 360-degree operating space. Semanta expands that narrow slice into scenario-covered, risk-aware worlds so ML/DL/NN/Q systems can train and test against more of the possible reality before reality charges the full cost of failure.

The practical idea: real company data is often only a thin observed slice of possible reality. Semanta expands that slice into controlled scenarios, shocks, regimes and tail events. In geometric terms, the observed dataset may be one degree on one plane; the operating world contains many planes, regimes and trajectories. Semanta makes that hidden space explorable and trainable.

Industry Packs

Industry	Slug	Rows	Columns	Quality	Primary use case
Banks	banks	100,000	100	0.968	credit risk, transaction anomaly, liquidity and operational-risk simulation
AI	ai	100,000	100	0.973	prompt complexity, eval difficulty, hallucination risk and tool-use scenario generation
Hedge Funds	hedge_funds	100,000	100	0.978	factor regime, drawdown, liquidity and alpha-decay scenario generation
Insurance	insurance	100,000	100	0.977	claim frequency, loss severity, reserve adequacy and catastrophe-tail simulation
Pharma	pharma	100,000	100	0.961	trial signal, safety, regulatory-delay and launch-readiness scenario generation
Medicine	medicine	100,000	100	0.967	patient-flow, diagnostic uncertainty, treatment response and capacity-risk simulation
Manufacturing	manufacturing	100,000	100	0.957	defect, downtime, sensor drift, throughput and supplier-risk simulation
Security	security	100,000	100	0.981	attack intensity, alert noise, response-latency and asset-criticality simulation
Science	science	100,000	100	0.946	hypothesis strength, experiment noise, replication and discovery-potential simulation
FMCG	fmcg	100,000	100	0.952	promotion, demand elasticity, stockout, channel-shift and pricing scenario generation
Retail	retail	100,000	100	0.957	basket, churn, traffic, markdown, inventory and customer-behavior simulation

Industry Business Catalog

Industry	Buyer	Business question Semanta helps answer	Output artifacts
Banks	banks, fintech risk teams, credit and fraud operations	What happens to credit, fraud, liquidity and operational-risk models when the regime changes before historical evidence is abundant?	data/industry_vertical_suite/banks/semanta_banks_world_native_synthetic_100000x100.csv.gz + metrics + schema
AI	AI labs, model builders, evaluation teams, agent-platform teams and enterprise AI groups	Can models and agents be evaluated on controlled task worlds before real customer traffic and private data are involved?	data/industry_vertical_suite/ai/semanta_ai_world_native_synthetic_100000x100.csv.gz + metrics + schema
Hedge Funds	hedge funds, quant teams, portfolio researchers and systematic trading groups	Where do strategies break under liquidity shocks, crowding, drawdowns and factor regime rotation?	data/industry_vertical_suite/hedge_funds/semanta_hedge_funds_world_native_synthetic_100000x100.csv.gz + metrics + schema
Insurance	insurers, reinsurers, actuarial teams and claim operations	How do underwriting, reserve and claims models behave under rare loss clusters and catastrophe-like tails?	data/industry_vertical_suite/insurance/semanta_insurance_world_native_synthetic_100000x100.csv.gz + metrics + schema
Pharma	pharmaceutical R&D, clinical operations, safety and market-access teams	Which trial, safety, regulatory-delay and launch-readiness signals deserve attention before clinical capacity is committed?	data/industry_vertical_suite/pharma/semanta_pharma_world_native_synthetic_100000x100.csv.gz + metrics + schema
Medicine	health systems, hospitals, medtech teams and clinical AI builders	Can triage, readmission and capacity models be tested under pressure without exposing patient data?	data/industry_vertical_suite/medicine/semanta_medicine_world_native_synthetic_100000x100.csv.gz + metrics + schema
Manufacturing	industrial operators, process engineers, quality and supply-chain teams	Which sensor drift, defect, downtime and supplier-risk pathways should be rehearsed before real production loss?	data/industry_vertical_suite/manufacturing/semanta_manufacturing_world_native_synthetic_100000x100.csv.gz + metrics + schema
Security	cybersecurity vendors, SOC teams, fraud defense and critical-infrastructure defenders	Can detection and response pipelines withstand adversarial incidents, alert floods and analyst overload?	data/industry_vertical_suite/security/semanta_security_world_native_synthetic_100000x100.csv.gz + metrics + schema
Science	research labs, frontier R&D groups, scientific ML teams and grant-backed institutions	Which hypotheses, experiments and replication risks should be prioritized before scarce lab time is spent?	data/industry_vertical_suite/science/semanta_science_world_native_synthetic_100000x100.csv.gz + metrics + schema
FMCG	consumer goods manufacturers, category teams, pricing and demand planners	How will promotion, demand elasticity, stockout and channel-shift decisions behave before trade-spend is committed?	data/industry_vertical_suite/fmcg/semanta_fmcg_world_native_synthetic_100000x100.csv.gz + metrics + schema
Retail	retailers, marketplaces, CRM, inventory and merchandising teams	What happens to churn, baskets, markdowns, store traffic and inventory when customer behavior drifts?	data/industry_vertical_suite/retail/semanta_retail_world_native_synthetic_100000x100.csv.gz + metrics + schema

Industry Example Cards

1. Banks

• Buyer: banks, fintech risk teams, credit and fraud operations.
• Business question: What happens to credit, fraud, liquidity and operational-risk models when the regime changes before historical evidence is abundant?
• World modeled: credit risk, transaction anomaly, liquidity and operational-risk simulation.
• Success story: A bank can stress-test credit and fraud models against synthetic regime shifts before the same pattern appears in production.
• Download: data/industry_vertical_suite/banks/semanta_banks_world_native_synthetic_100000x100.csv.gz.
• Evidence: metrics/industry_vertical_suite/banks_quality_metrics.json and schemas/industry_vertical_suite/banks_schema.json.
• Shape: 100,000 rows x 100 columns; quality score 0.968.

2. AI

• Buyer: AI labs, model builders, evaluation teams, agent-platform teams and enterprise AI groups.
• Business question: Can models and agents be evaluated on controlled task worlds before real customer traffic and private data are involved?
• World modeled: prompt complexity, eval difficulty, hallucination risk and tool-use scenario generation.
• Success story: An AI team can generate controlled evaluation/training substrates for agents and models while keeping customer data out of external model providers.
• Download: data/industry_vertical_suite/ai/semanta_ai_world_native_synthetic_100000x100.csv.gz.
• Evidence: metrics/industry_vertical_suite/ai_quality_metrics.json and schemas/industry_vertical_suite/ai_schema.json.
• Shape: 100,000 rows x 100 columns; quality score 0.973.

3. Hedge Funds

• Buyer: hedge funds, quant teams, portfolio researchers and systematic trading groups.
• Business question: Where do strategies break under liquidity shocks, crowding, drawdowns and factor regime rotation?
• World modeled: factor regime, drawdown, liquidity and alpha-decay scenario generation.
• Success story: A quant team can evaluate strategy fragility across liquidity shocks and factor crowding without waiting for the next real drawdown.
• Download: data/industry_vertical_suite/hedge_funds/semanta_hedge_funds_world_native_synthetic_100000x100.csv.gz.
• Evidence: metrics/industry_vertical_suite/hedge_funds_quality_metrics.json and schemas/industry_vertical_suite/hedge_funds_schema.json.
• Shape: 100,000 rows x 100 columns; quality score 0.978.

4. Insurance

• Buyer: insurers, reinsurers, actuarial teams and claim operations.
• Business question: How do underwriting, reserve and claims models behave under rare loss clusters and catastrophe-like tails?
• World modeled: claim frequency, loss severity, reserve adequacy and catastrophe-tail simulation.
• Success story: An insurer can rehearse reserve and claim workflows against rare loss clusters before portfolio stress becomes visible in historical data.
• Download: data/industry_vertical_suite/insurance/semanta_insurance_world_native_synthetic_100000x100.csv.gz.
• Evidence: metrics/industry_vertical_suite/insurance_quality_metrics.json and schemas/industry_vertical_suite/insurance_schema.json.
• Shape: 100,000 rows x 100 columns; quality score 0.977.

5. Pharma

• Buyer: pharmaceutical R&D, clinical operations, safety and market-access teams.
• Business question: Which trial, safety, regulatory-delay and launch-readiness signals deserve attention before clinical capacity is committed?
• World modeled: trial signal, safety, regulatory-delay and launch-readiness scenario generation.
• Success story: A pharma team can simulate trial-operation bottlenecks and adverse-event signal drift before allocating expensive clinical capacity.
• Download: data/industry_vertical_suite/pharma/semanta_pharma_world_native_synthetic_100000x100.csv.gz.
• Evidence: metrics/industry_vertical_suite/pharma_quality_metrics.json and schemas/industry_vertical_suite/pharma_schema.json.
• Shape: 100,000 rows x 100 columns; quality score 0.961.

6. Medicine

• Buyer: health systems, hospitals, medtech teams and clinical AI builders.
• Business question: Can triage, readmission and capacity models be tested under pressure without exposing patient data?
• World modeled: patient-flow, diagnostic uncertainty, treatment response and capacity-risk simulation.
• Success story: A hospital can evaluate triage and readmission models under capacity shock without exposing patient data to external systems.
• Download: data/industry_vertical_suite/medicine/semanta_medicine_world_native_synthetic_100000x100.csv.gz.
• Evidence: metrics/industry_vertical_suite/medicine_quality_metrics.json and schemas/industry_vertical_suite/medicine_schema.json.
• Shape: 100,000 rows x 100 columns; quality score 0.967.

7. Manufacturing

• Buyer: industrial operators, process engineers, quality and supply-chain teams.
• Business question: Which sensor drift, defect, downtime and supplier-risk pathways should be rehearsed before real production loss?
• World modeled: defect, downtime, sensor drift, throughput and supplier-risk simulation.
• Success story: A plant can train predictive quality and downtime models on rare failure pathways without breaking real production equipment.
• Download: data/industry_vertical_suite/manufacturing/semanta_manufacturing_world_native_synthetic_100000x100.csv.gz.
• Evidence: metrics/industry_vertical_suite/manufacturing_quality_metrics.json and schemas/industry_vertical_suite/manufacturing_schema.json.
• Shape: 100,000 rows x 100 columns; quality score 0.957.

8. Security

• Buyer: cybersecurity vendors, SOC teams, fraud defense and critical-infrastructure defenders.
• Business question: Can detection and response pipelines withstand adversarial incidents, alert floods and analyst overload?
• World modeled: attack intensity, alert noise, response-latency and asset-criticality simulation.
• Success story: A security team can harden detection pipelines against adversarial behavior and alert floods before analysts are overloaded in production.
• Download: data/industry_vertical_suite/security/semanta_security_world_native_synthetic_100000x100.csv.gz.
• Evidence: metrics/industry_vertical_suite/security_quality_metrics.json and schemas/industry_vertical_suite/security_schema.json.
• Shape: 100,000 rows x 100 columns; quality score 0.981.

9. Science

• Buyer: research labs, frontier R&D groups, scientific ML teams and grant-backed institutions.
• Business question: Which hypotheses, experiments and replication risks should be prioritized before scarce lab time is spent?
• World modeled: hypothesis strength, experiment noise, replication and discovery-potential simulation.
• Success story: A lab can prioritize experiments by testing hypothesis robustness and replication risk before spending scarce lab time.
• Download: data/industry_vertical_suite/science/semanta_science_world_native_synthetic_100000x100.csv.gz.
• Evidence: metrics/industry_vertical_suite/science_quality_metrics.json and schemas/industry_vertical_suite/science_schema.json.
• Shape: 100,000 rows x 100 columns; quality score 0.946.

10. FMCG

• Buyer: consumer goods manufacturers, category teams, pricing and demand planners.
• Business question: How will promotion, demand elasticity, stockout and channel-shift decisions behave before trade-spend is committed?
• World modeled: promotion, demand elasticity, stockout, channel-shift and pricing scenario generation.
• Success story: An FMCG team can rehearse promotion and channel-mix decisions across demand shocks before committing trade-spend.
• Download: data/industry_vertical_suite/fmcg/semanta_fmcg_world_native_synthetic_100000x100.csv.gz.
• Evidence: metrics/industry_vertical_suite/fmcg_quality_metrics.json and schemas/industry_vertical_suite/fmcg_schema.json.
• Shape: 100,000 rows x 100 columns; quality score 0.952.

11. Retail

• Buyer: retailers, marketplaces, CRM, inventory and merchandising teams.
• Business question: What happens to churn, baskets, markdowns, store traffic and inventory when customer behavior drifts?
• World modeled: basket, churn, traffic, markdown, inventory and customer-behavior simulation.
• Success story: A retailer can test churn, markdown and inventory policies against customer-behavior drift before margin loss reaches the P&L.
• Download: data/industry_vertical_suite/retail/semanta_retail_world_native_synthetic_100000x100.csv.gz.
• Evidence: metrics/industry_vertical_suite/retail_quality_metrics.json and schemas/industry_vertical_suite/retail_schema.json.
• Shape: 100,000 rows x 100 columns; quality score 0.957.

Example Success Stories

• Banks: A bank can stress-test credit and fraud models against synthetic regime shifts before the same pattern appears in production.
• AI: An AI team can generate controlled evaluation/training substrates for agents and models while keeping customer data out of external model providers.
• Hedge Funds: A quant team can evaluate strategy fragility across liquidity shocks and factor crowding without waiting for the next real drawdown.
• Insurance: An insurer can rehearse reserve and claim workflows against rare loss clusters before portfolio stress becomes visible in historical data.
• Pharma: A pharma team can simulate trial-operation bottlenecks and adverse-event signal drift before allocating expensive clinical capacity.
• Medicine: A hospital can evaluate triage and readmission models under capacity shock without exposing patient data to external systems.
• Manufacturing: A plant can train predictive quality and downtime models on rare failure pathways without breaking real production equipment.
• Security: A security team can harden detection pipelines against adversarial behavior and alert floods before analysts are overloaded in production.
• Science: A lab can prioritize experiments by testing hypothesis robustness and replication risk before spending scarce lab time.
• FMCG: An FMCG team can rehearse promotion and channel-mix decisions across demand shocks before committing trade-spend.
• Retail: A retailer can test churn, markdown and inventory policies against customer-behavior drift before margin loss reaches the P&L.

Files

• banks: data/industry_vertical_suite/banks/semanta_banks_world_native_synthetic_100000x100.csv.gz | metrics metrics/industry_vertical_suite/banks_quality_metrics.json | schema schemas/industry_vertical_suite/banks_schema.json
• ai: data/industry_vertical_suite/ai/semanta_ai_world_native_synthetic_100000x100.csv.gz | metrics metrics/industry_vertical_suite/ai_quality_metrics.json | schema schemas/industry_vertical_suite/ai_schema.json
• hedge_funds: data/industry_vertical_suite/hedge_funds/semanta_hedge_funds_world_native_synthetic_100000x100.csv.gz | metrics metrics/industry_vertical_suite/hedge_funds_quality_metrics.json | schema schemas/industry_vertical_suite/hedge_funds_schema.json
• insurance: data/industry_vertical_suite/insurance/semanta_insurance_world_native_synthetic_100000x100.csv.gz | metrics metrics/industry_vertical_suite/insurance_quality_metrics.json | schema schemas/industry_vertical_suite/insurance_schema.json
• pharma: data/industry_vertical_suite/pharma/semanta_pharma_world_native_synthetic_100000x100.csv.gz | metrics metrics/industry_vertical_suite/pharma_quality_metrics.json | schema schemas/industry_vertical_suite/pharma_schema.json
• medicine: data/industry_vertical_suite/medicine/semanta_medicine_world_native_synthetic_100000x100.csv.gz | metrics metrics/industry_vertical_suite/medicine_quality_metrics.json | schema schemas/industry_vertical_suite/medicine_schema.json
• manufacturing: data/industry_vertical_suite/manufacturing/semanta_manufacturing_world_native_synthetic_100000x100.csv.gz | metrics metrics/industry_vertical_suite/manufacturing_quality_metrics.json | schema schemas/industry_vertical_suite/manufacturing_schema.json
• security: data/industry_vertical_suite/security/semanta_security_world_native_synthetic_100000x100.csv.gz | metrics metrics/industry_vertical_suite/security_quality_metrics.json | schema schemas/industry_vertical_suite/security_schema.json
• science: data/industry_vertical_suite/science/semanta_science_world_native_synthetic_100000x100.csv.gz | metrics metrics/industry_vertical_suite/science_quality_metrics.json | schema schemas/industry_vertical_suite/science_schema.json
• fmcg: data/industry_vertical_suite/fmcg/semanta_fmcg_world_native_synthetic_100000x100.csv.gz | metrics metrics/industry_vertical_suite/fmcg_quality_metrics.json | schema schemas/industry_vertical_suite/fmcg_schema.json
• retail: data/industry_vertical_suite/retail/semanta_retail_world_native_synthetic_100000x100.csv.gz | metrics metrics/industry_vertical_suite/retail_quality_metrics.json | schema schemas/industry_vertical_suite/retail_schema.json

Quick Load Example

import pandas as pd

url = "https://huggingface.co/datasets/SemantaAI/semanta-dataset-suite/resolve/semanta/data/industry_vertical_suite/banks/semanta_banks_world_native_synthetic_100000x100.csv.gz"
df = pd.read_csv(url)
print(df.shape)
print(df.head())

Load Any Vertical

import pandas as pd

base = "https://huggingface.co/datasets/SemantaAI/semanta-dataset-suite/resolve/semanta"
vertical = "ai"  # banks, hedge_funds, insurance, pharma, medicine, manufacturing, security, science, fmcg, retail, ai
url = f"{base}/data/industry_vertical_suite/{vertical}/semanta_{vertical}_world_native_synthetic_100000x100.csv.gz"
df = pd.read_csv(url)
print(vertical, df.shape)

Quality Gates

• synthetic_only: pass - No customer/private source data is used.
• per_industry_files: pass - Each vertical has its own CSV, schema and metrics.
• 100_column_width: pass - Each vertical has 100 columns.
• scenario_coverage: pass - All canonical scenarios are represented.
• hf_interop: pass - CSV.GZ + README + schema JSON + metrics JSON.
• claim_boundary: pass - No model-performance claims without benchmarks.
• production_smoke_gate: pass - 13/13 public proof targets pass in the Semanta production smoke pipeline.

Metrics Published

Metric family	Included evidence
Shape	rows, columns, cells and per-industry schema width
Quality	missing rate, duplicate rate, target-label profile and overall quality score
Scenario coverage	regimes, shocks, rare events, adversarial and recovery paths
Reproducibility	seed, package manifest, lineage and deterministic generation contract
Safety	synthetic-only policy, no customer/private source data and no external model provider use

How Semanta Generated This

1. Semanta selects an industry world specification: buyer, use case, domain signals and risk profile.
2. It samples canonical scenario regimes: baseline, growth, efficiency push, drift, stress, shock, rare event, adversarial and recovery.
3. It creates common world features shared across industries: macro, risk, latency, quality, drift, tail-risk and confidence signals.
4. It creates industry-native features for each vertical, such as credit risk for banks or trial signal for pharma.
5. It derives target labels and risk scores from scenario pressure, domain bias, anomaly density and drift.
6. It writes HF-compatible CSV.GZ files plus schemas, metrics, dataset index, lineage and claim boundaries.

No customer data was used. No customer data was sent to DeepSeek, Gamma or any external model provider. This package is deterministic and reproducible from the recorded seed and Semanta generation contracts.

Recommended Uses

• Tabular ML classification and regression.
• Scenario robustness testing.
• Synthetic data quality experiments.
• Drift and anomaly detection prototypes.
• StarForge/Gamma training substrate preparation after downstream evaluation.
• Investor/client demos showing Semanta's breadth across industries.

Semanta Links

• Website: https://semanta.xyz
• Public API proof: https://api-staging.semanta.xyz/public/industry-dataset-suite
• Public proof bundle: https://api-staging.semanta.xyz/public/proof-bundle?rows=64&seed=37
• Studio Guided Demo: https://studio-staging.semanta.xyz
• API docs: https://api-staging.semanta.xyz/docs
• Operator contact: operator@semanta.xyz

Good-Fit Use Cases

Buyer	Example use
AI teams	Stress-test agents and classifiers on synthetic task worlds before production traffic.
Banks and fintechs	Explore credit, fraud, liquidity and policy-change scenarios without exposing customer data.
Hedge funds and quants	Evaluate strategy fragility across synthetic market regimes and liquidity shocks.
Healthcare and pharma teams	Prototype privacy-safe clinical, safety and trial-operation workflows.
Manufacturing and security teams	Generate failure, sensor drift, attack and incident variants for robustness testing.

Claim Boundary

This package proves Semanta's synthetic generation, packaging, metricing and HF delivery capability. It does not claim downstream model-performance superiority until StarForge/Gamma training and benchmark evidence is attached.