AI teams, data partners, and institutions building finance-domain models
Synthetic Data
Synthetic Data gives teams a finance-specific corpus filtered by language, theme, and optional country so they can evaluate or fine-tune models without relying on raw publisher text as the starting point.
Positioned for model evaluation, fine-tuning, and finance-domain adaptation with licensing options.
Refresh model
Recurring daily or weekly delivery can be paired with event-driven pushes for high-impact releases.
Sample output
JSON record preview
Synthetic
{
"language": "English (EN)",
"theme": "Monetary Policy",
"country": "IN",
"headline": "Synthetic policy brief captures the inflation, liquidity, and rate-cut debate in India.",
"summary": "The generated record reframes a finance event into training-friendly structured text for model evaluation and fine-tuning workflows.",
"tags": [
"central-bank",
"rates",
"inflation",
"emerging-markets"
],
"training_safe": true
}
The public website should show why the format matters: structured, multilingual, finance-specific output that teams can test before moving into a broader data conversation.
Synthetic Data is best explained through the generate-sample workflow: language and theme filters, structured JSON output, and a clear bridge into licensing for model teams.
Workflow
Choose the language and theme, generate a sample record, inspect the JSON output, and move into sample-pack or licensing conversations if the data fits the model-development workflow.
Step 1
Select the language, theme, and optional country you want to evaluate.
Step 2
Generate a sample finance record and inspect the JSON structure used for model development.
Step 3
Request a larger evaluation pack or delivery plan once the coverage and format are aligned.
Language, theme, and optional country filtering for sample generation
Sample JSON output for quick evaluation before a deeper commercial discussion
