Manufacturing
Logistics
Real Estate
Retail
Finance
Healthcare
Hospitality
Automotive
About the Agent
The Synthetic Training Data Creation Agent, developed by ZBrain, is a specialized tool designed to generate high-quality synthetic datasets tailored for the training of intelligent agents. In industries where data may be scarce, sensitive, or challenging to obtain in sufficient quantities—such as customer support, finance, or healthcare—this agent fills the gap by creating domain-specific datasets that accurately reflect real-world scenarios and edge cases. It ensures that AI models are trained with the most relevant, diverse, and realistic data, accelerating the development of reliable and context-aware systems.
The agent employs a combination of simulation techniques, data augmentation, and deep domain knowledge to produce datasets that mirror user interactions, system inputs, and potential exceptions. By generating synthetic data reflective of specific workflows, the agent provides training material that spans various use cases, including rare or extreme cases that are often underrepresented in natural datasets. This dynamic data generation improves model performance by addressing challenges such as class imbalance, data scarcity, and privacy concerns—essential for training robust AI systems that can handle diverse, real-world situations.
By accelerating the training and iteration cycles, the Synthetic Training Data Creation Agent not only shortens time-to-deployment for AI-powered solutions but also enhances model accuracy and robustness. It ensures that intelligent agents are better equipped to perform effectively in live environments, improving reliability, scalability, and performance in production. For enterprises, this agent offers a powerful tool to bridge the data gap, enabling the creation of more capable, efficient, and secure AI systems that meet the specific needs of their business objectives.
Accuracy
TBD
Speed
TBD
Input Data Set
Sample of data set required for Synthetic Training Data Creation Agent:
1. Project Information
- Domain: Finance
- Use Case: Credit Card Transaction Data for Fraud Detection
- Number of Samples: 100
- Random Seed: 42 (for reproducibility)
2. Data Schema
Core Fields:
transaction_id(integer)timestamp(datetime ISO 8601)card_id(string, masked)merchant(string)merchant_id(string)amount(float, 2 decimal places)category(string, from controlled vocabulary)is_fraud(boolean)
Extended Fields:
location(string, city/state format)country_code(string, ISO 3166-1 alpha-2)postal_code(string)transaction_method(string, enumerated)device_used(string, for digital transactions)ip_address(string, anonymized)is_recurring(boolean)is_international(boolean)
3. Statistical Parameters
Amount Distribution
- Typical range: $10-$500
- Occasional outliers up to $2,500
Timestamp Distribution
- Date range: 90-day window (Feb-May 2025)
- Higher density during business hours (9am-9pm)
- Higher frequency on weekdays vs weekends
Category Distribution
| Category | Percentage |
|---|---|
| Groceries | 25% |
| Dining | 20% |
| Online Shopping | 15% |
| Entertainment | 10% |
| Travel | 8% |
| Gas/Automotive | 8% |
| Healthcare | 5% |
| Utilities | 5% |
| Other | 4% |
Transaction Method Distribution
| Method | Percentage |
|---|---|
| Chip | 60% |
| Contactless | 25% |
| Manual entry | 10% |
| Online | 5% |
Geographic Distribution
- Primary metro areas (70%): NYC, LA, Chicago, Dallas, Miami
- Secondary cities (25%): Seattle, Denver, Boston, Atlanta, Phoenix
- Other locations (5%): Random distribution
- International transactions: 5% of total
4. Fraud Pattern Configuration
Fraud Rate
- 3% of transactions flagged as fraudulent
Fraud Patterns
- Unusual amount (95th percentile) in unexpected category
- First-time international transactions without prior history
- Transaction velocity anomalies (sudden frequency spike)
Legitimate Edge Cases
- High-value legitimate purchases (2% of transactions)
- International travel sequences
- Holiday spending patterns
6. Output Format
- Structured tabular format with headers
- All 100 records with Core and Extended fields
Deliverable Example
Sample output delivered by the Synthetic Training Data Creation Agent:
Dataset Summary
| Metric | Value |
|---|---|
| Total Transactions | 100 |
| Date Range | 2025-02-14 to 2025-05-13 |
| Unique Cards | 25 |
| Average Amount | $188.66 |
| Median Amount | $92.11 |
| Fraud Rate | 3% |
| Category Distribution | Groceries (24%), Dining (16%), Online Shopping (14%) |
Transaction Data
Core Fields
| Transaction ID | Timestamp | Card ID | Merchant | Merchant ID | Amount | Category | Is Fraud |
|---|---|---|---|---|---|---|---|
| 1001 | 2025-02-23T00:49:15 | XXXX-XXXX-XXXX-009 | Chipotle | CP-31287 | $18.69 | Dining | FALSE |
| 1002 | 2025-04-30T16:25:13 | XXXX-XXXX-XXXX-006 | Chipotle | CP-31287 | $988.75 | Dining | FALSE |
| 1003 | 2025-04-16T10:58:58 | XXXX-XXXX-XXXX-016 | Newegg | NE-77712 | $18.66 | Online Shopping | FALSE |
| 1004 | 2025-03-30T12:51:37 | XXXX-XXXX-XXXX-022 | Walmart | WM-00123 | $422.07 | Groceries | FALSE |
| 1005 | 2025-03-20T18:15:19 | XXXX-XXXX-XXXX-012 | AT&T | ATT-12345 | $2271.19 | Utilities | FALSE |
| 1006 | 2025-03-18T11:01:23 | XXXX-XXXX-XXXX-016 | The Cheesecake Factory | CF-44219 | $42.76 | Dining | FALSE |
| 1007 | 2025-03-17T16:31:47 | XXXX-XXXX-XXXX-024 | Target | TGT-00214 | $63.15 | Groceries | FALSE |
| 1008 | 2025-05-12T12:15:54 | XXXX-XXXX-XXXX-014 | Olive Garden | OG-66543 | $92.11 | Dining | FALSE |
| 1009 | 2025-04-29T17:14:38 | XXXX-XXXX-XXXX-022 | Trader Joe's | TJ-32145 | $428.48 | Groceries | FALSE |
| 1010 | 2025-04-10T01:12:00 | XXXX-XXXX-XXXX-021 | Lowe's | LW-55298 | $18.48 | Other | FALSE |
| 1011 | 2025-05-01T19:41:16 | XXXX-XXXX-XXXX-003 | Airbnb | ABNB-0023 | $15.83 | Travel | FALSE |
| 1012 | 2025-04-13T15:40:12 | XXXX-XXXX-XXXX-019 | Trader Joe's | TJ-32145 | $233.82 | Groceries | FALSE |
| 1013 | 2025-05-12T03:59:56 | XXXX-XXXX-XXXX-004 | Starbucks | SB-10285 | $198.82 | Dining | FALSE |
| 1014 | 2025-03-20T21:57:36 | XXXX-XXXX-XXXX-015 | Airbnb | ABNB-0023 | $47.27 | Travel | FALSE |
| 1015 | 2025-03-30T12:47:14 | XXXX-XXXX-XXXX-018 | AMC Theaters | AMC-77231 | $98.11 | Entertainment | FALSE |
| 1016 | 2025-02-16T00:18:25 | XXXX-XXXX-XXXX-008 | Uber | UBR-001 | $28.44 | Travel | FALSE |
| 1017 | 2025-02-23T14:09:30 | XXXX-XXXX-XXXX-022 | AutoZone | AZ-56128 | $53.99 | Gas/Automotive | FALSE |
| 1018 | 2025-03-22T20:12:50 | XXXX-XXXX-XXXX-001 | Verizon | VZN-67890 | $29.15 | Utilities | FALSE |
| 1019 | 2025-03-17T13:54:22 | XXXX-XXXX-XXXX-008 | Amazon | AMZN-001 | $348.30 | Online Shopping | FALSE |
| 1020 | 2025-04-26T22:48:48 | XXXX-XXXX-XXXX-016 | PG&E | PGE-001 | $151.11 | Utilities | FALSE |
| 1021 | 2025-02-17T01:27:55 | XXXX-XXXX-XXXX-002 | Spotify | SP-00145 | $76.12 | Entertainment | FALSE |
| 1022 | 2025-04-02T22:20:29 | XXXX-XXXX-XXXX-008 | Home Depot | HD-44198 | $34.72 | Other | FALSE |
| 1023 | 2025-02-26T20:00:43 | XXXX-XXXX-XXXX-021 | Spotify | SP-00145 | $152.84 | Entertainment | FALSE |
| 1024 | 2025-04-26T01:21:29 | XXXX-XXXX-XXXX-002 | Whole Foods | WF-45672 | $65.07 | Groceries | FALSE |
| 1025 | 2025-04-24T01:04:30 | XXXX-XXXX-XXXX-012 | Walmart | WM-00123 | $247.34 | Groceries | FALSE |
| 1026 | 2025-04-17T05:43:52 | XXXX-XXXX-XXXX-005 | Costco | CC-98765 | $374.65 | Groceries | FALSE |
| 1027 | 2025-03-23T08:49:58 | XXXX-XXXX-XXXX-006 | Target | TGT-00214 | $77.55 | Groceries | FALSE |
| 1028 | 2025-04-05T15:47:55 | XXXX-XXXX-XXXX-015 | Home Depot | HD-44198 | $104.42 | Other | FALSE |
| 1029 | 2025-04-20T21:46:55 | XXXX-XXXX-XXXX-002 | PG&E | PGE-001 | $54.40 | Utilities | FALSE |
| 1030 | 2025-02-28T17:59:15 | XXXX-XXXX-XXXX-020 | Newegg | NE-77712 | $73.55 | Online Shopping | FALSE |
| 1031 | 2025-04-10T08:58:35 | XXXX-XXXX-XXXX-002 | Walgreens | WG-30156 | $230.68 | Healthcare | FALSE |
| 1032 | 2025-03-10T21:26:35 | XXXX-XXXX-XXXX-024 | PG&E | PGE-001 | $57.34 | Utilities | FALSE |
| 1033 | 2025-04-27T11:30:26 | XXXX-XXXX-XXXX-004 | Netflix | NFLX-001 | $307.79 | Entertainment | FALSE |
| 1034 | 2025-04-21T17:16:55 | XXXX-XXXX-XXXX-008 | Verizon | VZN-67890 | $31.08 | Utilities | FALSE |
| 1035 | 2025-04-25T06:30:41 | XXXX-XXXX-XXXX-012 | Safeway | SW-87123 | $52.98 | Groceries | FALSE |
| 1036 | 2025-04-03T08:37:44 | XXXX-XXXX-XXXX-008 | Target | TGT-00214 | $61.25 | Groceries | FALSE |
| 1037 | 2025-04-26T10:51:07 | XXXX-XXXX-XXXX-011 | Walmart | WM-00123 | $54.03 | Groceries | FALSE |
| 1038 | 2025-03-04T12:44:14 | XXXX-XXXX-XXXX-023 | PG&E | PGE-001 | $235.59 | Utilities | FALSE |
| 1039 | 2025-05-09T08:07:30 | XXXX-XXXX-XXXX-011 | Marriott Hotels | MH-88754 | $47.40 | Travel | FALSE |
| 1040 | 2025-03-21T20:03:10 | XXXX-XXXX-XXXX-014 | AT&T | ATT-12345 | $226.54 | Utilities | FALSE |
| 1041 | 2025-02-27T04:16:41 | XXXX-XXXX-XXXX-022 | AMC Theaters | AMC-77231 | $132.17 | Entertainment | FALSE |
| 1042 | 2025-05-09T05:35:40 | XXXX-XXXX-XXXX-022 | Whole Foods | WF-45672 | $133.48 | Groceries | FALSE |
| 1043 | 2025-03-06T19:49:31 | XXXX-XXXX-XXXX-000 | Delta Airlines | DL-55432 | $103.11 | Travel | FALSE |
| 1044 | 2025-02-24T15:15:45 | XXXX-XXXX-XXXX-010 | AutoZone | AZ-56128 | $9.92 | Gas/Automotive | FALSE |
| 1045 | 2025-03-01T20:33:18 | XXXX-XXXX-XXXX-014 | Delta Airlines | DL-55432 | $131.53 | Travel | FALSE |
| 1046 | 2025-05-01T12:37:56 | XXXX-XXXX-XXXX-022 | Starbucks | SB-10285 | $111.72 | Dining | FALSE |
| 1047 | 2025-04-10T03:16:23 | XXXX-XXXX-XXXX-009 | Whole Foods | WF-45672 | $221.12 | Groceries | TRUE |
| 1048 | 2025-04-20T21:48:22 | XXXX-XXXX-XXXX-006 | Lowe's | LW-55298 | $107.59 | Other | FALSE |
| 1049 | 2025-03-22T20:49:05 | XXXX-XXXX-XXXX-017 | eBay | EBAY-001 | $686.36 | Online Shopping | FALSE |
| 1050 | 2025-03-16T22:31:30 | XXXX-XXXX-XXXX-017 | Whole Foods | WF-45672 | $142.34 | Groceries | FALSE |
| 1051 | 2025-04-13T05:51:39 | XXXX-XXXX-XXXX-005 | Walmart | WM-00123 | $84.39 | Groceries | FALSE |
| 1052 | 2025-03-14T16:31:10 | XXXX-XXXX-XXXX-010 | Marriott Hotels | MH-88754 | $152.05 | Travel | FALSE |
| 1053 | 2025-03-25T04:03:46 | XXXX-XXXX-XXXX-019 | Costco | CC-98765 | $80.27 | Groceries | FALSE |
| 1054 | 2025-04-16T00:46:14 | XXXX-XXXX-XXXX-003 | eBay | EBAY-001 | $232.21 | Online Shopping | FALSE |
| 1055 | 2025-03-25T06:27:54 | XXXX-XXXX-XXXX-008 | Newegg | NE-77712 | $355.15 | Online Shopping | FALSE |
| 1056 | 2025-03-01T00:34:58 | XXXX-XXXX-XXXX-020 | AMC Theaters | AMC-77231 | $442.87 | Entertainment | FALSE |
| 1057 | 2025-03-28T06:54:24 | XXXX-XXXX-XXXX-022 | Exxon | EX-77281 | $68.10 | Gas/Automotive | FALSE |
| 1058 | 2025-04-20T08:36:39 | XXXX-XXXX-XXXX-000 | Exxon | EX-77281 | $9.48 | Gas/Automotive | FALSE |
| 1059 | 2025-04-10T11:56:04 | XXXX-XXXX-XXXX-003 | Netflix | NFLX-001 | $18.48 | Entertainment | FALSE |
| 1060 | 2025-05-07T08:10:29 | XXXX-XXXX-XXXX-017 | PG&E | PGE-001 | $191.87 | Utilities | FALSE |
| 1061 | 2025-03-06T17:29:31 | XXXX-XXXX-XXXX-006 | Whole Foods | WF-45672 | $633.84 | Groceries | FALSE |
| 1062 | 2025-04-03T08:33:45 | XXXX-XXXX-XXXX-010 | Whole Foods | WF-45672 | $571.64 | Groceries | FALSE |
| 1063 | 2025-05-13T12:19:46 | XXXX-XXXX-XXXX-024 | Best Buy | BB-00121 | $1247.82 | Online Shopping | TRUE |
| 1064 | 2025-04-27T06:23:34 | XXXX-XXXX-XXXX-022 | Home Depot | HD-44198 | $84.06 | Other | FALSE |
| 1065 | 2025-03-27T01:55:16 | XXXX-XXXX-XXXX-004 | AutoZone | AZ-56128 | $177.56 | Gas/Automotive | FALSE |
| 1066 | 2025-02-24T14:41:21 | XXXX-XXXX-XXXX-021 | Amazon | AMZN-001 | $105.18 | Online Shopping | FALSE |
| 1067 | 2025-03-23T10:30:10 | XXXX-XXXX-XXXX-020 | Verizon | VZN-67890 | $56.14 | Utilities | FALSE |
| 1068 | 2025-03-29T20:09:07 | XXXX-XXXX-XXXX-006 | Target | TGT-00214 | $98.41 | Groceries | FALSE |
| 1069 | 2025-04-11T16:37:33 | XXXX-XXXX-XXXX-021 | BP | BP-34187 | $45.08 | Gas/Automotive | FALSE |
| 1070 | 2025-04-24T00:26:29 | XXXX-XXXX-XXXX-004 | Target | TGT-00214 | $166.52 | Groceries | FALSE |
| 1071 | 2025-04-11T12:02:54 | XXXX-XXXX-XXXX-017 | Lowe's | LW-55298 | $105.31 | Other | FALSE |
| 1072 | 2025-03-08T23:15:40 | XXXX-XXXX-XXXX-020 | BP | BP-34187 | $46.85 | Gas/Automotive | FALSE |
| 1073 | 2025-02-18T01:31:48 | XXXX-XXXX-XXXX-004 | Apple Store | APPL-0512 | $39.30 | Online Shopping | FALSE |
| 1074 | 2025-04-09T23:23:27 | XXXX-XXXX-XXXX-004 | Spotify | SP-00145 | $127.07 | Entertainment | FALSE |
| 1075 | 2025-03-06T22:46:39 | XXXX-XXXX-XXXX-009 | Disney+ | DIS-00154 | $341.52 | Entertainment | FALSE |
| 1076 | 2025-04-16T10:15:50 | XXXX-XXXX-XXXX-012 | Apple Store | APPL-0512 | $59.64 | Online Shopping | FALSE |
| 1077 | 2025-05-10T07:47:50 | XXXX-XXXX-XXXX-022 | AT&T | ATT-12345 | $453.00 | Utilities | FALSE |
| 1078 | 2025-04-20T18:29:14 | XXXX-XXXX-XXXX-004 | Walmart | WM-00123 | $43.42 | Groceries | FALSE |
| 1079 | 2025-04-21T17:41:05 | XXXX-XXXX-XXXX-003 | Home Depot | HD-44198 | $45.79 | Other | FALSE |
| 1080 | 2025-03-09T13:14:42 | XXXX-XXXX-XXXX-022 | Whole Foods | WF-45672 | $83.75 | Groceries | FALSE |
| 1081 | 2025-04-02T09:43:50 | XXXX-XXXX-XXXX-004 | Verizon | VZN-67890 | $90.12 | Utilities | FALSE |
| 1082 | 2025-04-27T14:29:26 | XXXX-XXXX-XXXX-014 | Olive Garden | OG-66543 | $991.01 | Dining | FALSE |
| 1083 | 2025-03-07T01:11:03 | XXXX-XXXX-XXXX-023 | Disney+ | DIS-00154 | $87.17 | Entertainment | FALSE |
| 1084 | 2025-05-02T12:02:40 | XXXX-XXXX-XXXX-007 | Delta Airlines | DL-55432 | $39.31 | Travel | FALSE |
| 1085 | 2025-03-19T14:05:44 | XXXX-XXXX-XXXX-012 | Newegg | NE-77712 | $68.41 | Online Shopping | FALSE |
| 1086 | 2025-03-01T22:40:19 | XXXX-XXXX-XXXX-007 | Airbnb | ABNB-0023 | $163.93 | Travel | FALSE |
| 1087 | 2025-04-24T18:14:24 | XXXX-XXXX-XXXX-018 | Chipotle | CP-31287 | $18.04 | Dining | FALSE |
| 1088 | 2025-04-22T00:01:16 | XXXX-XXXX-XXXX-007 | AT&T | ATT-12345 | $63.93 | Utilities | FALSE |
| 1089 | 2025-05-10T08:46:40 | XXXX-XXXX-XXXX-022 | Newegg | NE-77712 | $81.93 | Online Shopping | TRUE |
| 1090 | 2025-04-29T01:37:41 | XXXX-XXXX-XXXX-005 | Starbucks | SB-10285 | $66.92 | Dining | FALSE |
| 1091 | 2025-03-26T19:03:16 | XXXX-XXXX-XXXX-021 | Costco | CC-98765 | $225.30 | Groceries | FALSE |
| 1092 | 2025-02-14T11:42:46 | XXXX-XXXX-XXXX-000 | The Cheesecake Factory | CF-44219 | $68.94 | Dining | FALSE |
| 1093 | 2025-04-04T15:00:09 | XXXX-XXXX-XXXX-008 | PG&E | # Synthetic Financial Transaction Dataset |
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