> ## Documentation Index
> Fetch the complete documentation index at: https://docs.reducto.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Layout & Table Extraction to Analytics
> Extract and analyze financial data from 10-K annual reports using Parse, Extract, and table extraction
Extract structured financial data from SEC 10-K filings for investment analysis, competitive intelligence, or compliance automation.
***
## Sample Document
Download the sample: [10k-apple.pdf](/samples/10k-apple.pdf)
***
## Create API Key
Go to [studio.reducto.ai](https://studio.reducto.ai) and sign in. From the home page, click **API Keys** in the left sidebar.
The API Keys page shows your existing keys. Click **+ Create new API key** in the top right corner.
In the modal, enter a name for your key and set an expiration policy (or select "Never" for no expiration). Click **Create**.
Copy your new API key and store it securely. You won't be able to see it again after closing this dialog.
Set the key as an environment variable:
```bash theme={null}
export REDUCTO_API_KEY="your-api-key-here"
```
***
## Studio Walkthrough
Go to [studio.reducto.ai](https://studio.reducto.ai) and create an Extract pipeline. Upload the 10-K PDF.
Extract runs Parse under the hood, then uses an LLM to locate and return only the specific fields you define. This is ideal for pulling financial metrics from 10-K filings.
Before defining your schema, check the Parse settings that Extract will use. Open the **Configurations** tab to access settings.
Key settings for financial documents:
* **Enable AI Summarization** — Generate summaries of figures and charts
* **Return Figure/Table Images** — Include extracted images of charts and tables
Click **Run** to see the parsed content. Reducto parses financial tables with their structure preserved, including multi-year comparative data.
Notice how the Products and Services gross margin table keeps proper row/column structure with data for 2023, 2022, and 2021. If a value doesn't appear in Parse output, Extract can't find it either.
Click **Extract** to switch views. While Parse gives you the full document content, Extract lets you define a schema to pull only the specific fields you need as structured JSON.
For 10-K analysis, we want to extract two categories of data:
* **Company info** — Name, ticker symbol, fiscal year end, and SEC CIK number from the cover page
* **Income statement** — Key metrics like total revenue, cost of sales, gross profit, operating expenses, and operating income from the financial statements
Click **Import** in the Schema Builder to paste this pre-defined JSON schema:
```json theme={null}
{
"type": "object",
"properties": {
"company_info": {
"type": "object",
"properties": {
"name": {
"type": "string",
"description": "Company name from cover page"
},
"ticker": {
"type": "string",
"description": "Stock ticker symbol"
},
"fiscal_year_end": {
"type": "string",
"description": "Fiscal year end date"
},
"cik": {
"type": "string",
"description": "SEC CIK number"
}
}
},
"income_statement": {
"type": "object",
"description": "Data from Consolidated Statements of Operations",
"properties": {
"total_revenue": {
"type": "number",
"description": "Total net sales/revenue in millions"
},
"cost_of_sales": {
"type": "number",
"description": "Cost of goods sold in millions"
},
"gross_profit": {
"type": "number",
"description": "Gross profit (revenue minus COGS)"
},
"operating_expenses": {
"type": "number",
"description": "Total operating expenses"
},
"operating_income": {
"type": "number",
"description": "Operating income"
}
}
}
}
}
```
The Schema Builder shows your imported schema with nested structure. Click **Run** to execute the extraction.
The Results tab shows your extracted data matching your schema. Use the toolbar to copy or download the results or switch to JSON.
***
## Using the API
To run this extraction programmatically, click **Deploy** in Studio.
**Pipeline ID** creates a stable endpoint with a single identifier. Your code stays simple regardless of workflow complexity. Update settings in Studio and redeploy without changing code.
```python Python theme={null}
from pathlib import Path
from reducto import Reducto
client = Reducto()
upload = client.upload(file=Path("10k-apple.pdf"))
result = client.pipeline.run(
input=upload,
pipeline_id="k97c67gd5jahhs5anj3zr1gqd97zadrg"
)
```
```typescript TypeScript theme={null}
import Reducto from "reductoai";
import fs from "fs";
const client = new Reducto();
const upload = await client.upload({
file: fs.createReadStream("10k-apple.pdf"),
});
const result = await client.pipeline.run({
input: upload,
pipelineId: "k97c67gd5jahhs5anj3zr1gqd97zadrg",
});
```
```bash cURL theme={null}
# First, upload your file
curl -X POST "https://platform.reducto.ai/upload" \
-H "Authorization: Bearer $REDUCTO_API_KEY" \
-F "file=@10k-apple.pdf"
# This returns a response like: {"upload_id": "abc123"}
# Use the upload_id in the next request
# Run the pipeline
curl -X POST "https://platform.reducto.ai/pipeline" \
-H "Authorization: Bearer $REDUCTO_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"input": "reducto://abc123",
"pipeline_id": "k97c67gd5jahhs5anj3zr1gqd97zadrg"
}'
```
**Direct API Call** exports the raw configuration as code. Use this when you need runtime flexibility or want configuration in version control.
```python Python theme={null}
from pathlib import Path
from reducto import Reducto
client = Reducto()
upload_response = client.upload(file=Path("10k-apple.pdf"))
instructions = {
"schema": {
"type": "object",
"properties": {
"company_info": {
"type": "object",
"properties": {
"name": {"type": "string", "description": "Company name from cover page"},
"ticker": {"type": "string", "description": "Stock ticker symbol"},
"fiscal_year_end": {"type": "string", "description": "Fiscal year end date"},
"cik": {"type": "string", "description": "SEC CIK number"}
},
"required": ["name", "ticker", "fiscal_year_end", "cik"]
},
"income_statement": {
"type": "object",
"description": "Data from Consolidated Statements of Operations",
"properties": {
"total_revenue": {"type": "number", "description": "Total net sales/revenue in millions"},
"cost_of_sales": {"type": "number", "description": "Cost of goods sold in millions"},
"gross_profit": {"type": "number", "description": "Gross profit (revenue minus COGS)"},
"operating_expenses": {"type": "number", "description": "Total operating expenses"},
"operating_income": {"type": "number", "description": "Operating income"}
},
"required": ["total_revenue", "cost_of_sales", "gross_profit", "operating_expenses", "operating_income"]
}
},
"required": ["company_info", "income_statement"]
}
}
settings = {
"citations": {"enabled": True, "numerical_confidence": False}
}
result = client.extract.run(
input=upload_response,
instructions=instructions,
settings=settings
)
print(result)
```
```typescript TypeScript theme={null}
import Reducto from 'reductoai';
import fs from 'fs';
const client = new Reducto();
const uploadResponse = await client.upload({
file: fs.createReadStream("10k-apple.pdf")
});
const instructions = {
"schema": {
"type": "object",
"properties": {
"company_info": {
"type": "object",
"properties": {
"name": {"type": "string", "description": "Company name from cover page"},
"ticker": {"type": "string", "description": "Stock ticker symbol"},
"fiscal_year_end": {"type": "string", "description": "Fiscal year end date"},
"cik": {"type": "string", "description": "SEC CIK number"}
},
"required": ["name", "ticker", "fiscal_year_end", "cik"]
},
"income_statement": {
"type": "object",
"description": "Data from Consolidated Statements of Operations",
"properties": {
"total_revenue": {"type": "number", "description": "Total net sales/revenue in millions"},
"cost_of_sales": {"type": "number", "description": "Cost of goods sold in millions"},
"gross_profit": {"type": "number", "description": "Gross profit (revenue minus COGS)"},
"operating_expenses": {"type": "number", "description": "Total operating expenses"},
"operating_income": {"type": "number", "description": "Operating income"}
},
"required": ["total_revenue", "cost_of_sales", "gross_profit", "operating_expenses", "operating_income"]
}
},
"required": ["company_info", "income_statement"]
}
};
const settings = {
"citations": {"enabled": true, "numerical_confidence": false}
};
const result = await client.extract.run({
input: uploadResponse,
instructions,
settings
});
```
```bash cURL theme={null}
# First, upload your file
curl -X POST "https://platform.reducto.ai/upload" \
-H "Authorization: Bearer $REDUCTO_API_KEY" \
-F "file=@10k-apple.pdf"
# This returns a response like: {"upload_id": "abc123"}
# Use the upload_id in the next request
# Extract the document
curl -X POST "https://platform.reducto.ai/extract" \
-H "Authorization: Bearer $REDUCTO_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"input": "abc123",
"instructions": {
"schema": {
"type": "object",
"properties": {
"company_info": {
"type": "object",
"properties": {
"name": {"type": "string", "description": "Company name from cover page"},
"ticker": {"type": "string", "description": "Stock ticker symbol"},
"fiscal_year_end": {"type": "string", "description": "Fiscal year end date"},
"cik": {"type": "string", "description": "SEC CIK number"}
},
"required": ["name", "ticker", "fiscal_year_end", "cik"]
},
"income_statement": {
"type": "object",
"description": "Data from Consolidated Statements of Operations",
"properties": {
"total_revenue": {"type": "number", "description": "Total net sales/revenue in millions"},
"cost_of_sales": {"type": "number", "description": "Cost of goods sold in millions"},
"gross_profit": {"type": "number", "description": "Gross profit (revenue minus COGS)"},
"operating_expenses": {"type": "number", "description": "Total operating expenses"},
"operating_income": {"type": "number", "description": "Operating income"}
},
"required": ["total_revenue", "cost_of_sales", "gross_profit", "operating_expenses", "operating_income"]
}
},
"required": ["company_info", "income_statement"]
}
},
"settings": {
"citations": {"enabled": true, "numerical_confidence": false}
}
}'
```
***
## Next Steps
Configure table extraction
Optimize document sectioning
Extract multi-year data
Chain multiple operations