Turn PDFs into Markdown that keeps the document intact

Superlinked gives your agent one API to recover headings, reading order, tables and form fields as Markdown.

Markdown

## Q4 Fiscal 2025 Summary

| GAAP | Q4 FY25 | Q3 FY25 | Q4 FY24 |
|---|---:|---:|---:|
| Revenue | $39,331 | $35,082 | $22,103 |
| Gross margin | 73.0% | 74.6% | 76.0% |

NVIDIA CFO commentary page with four financial tables

Financial tables keep their rows and columns

## Q4 Fiscal 2025 Summary

| GAAP | Q4 FY25 | Q3 FY25 | Q4 FY24 | Q/Q | Y/Y |
|---|---:|---:|---:|---:|---:|
| Revenue | $39,331 | $35,082 | $22,103 | Up 12% | Up 78% |
| Gross margin | 73.0% | 74.6% | 76.0% | Down 1.6 pts | Down 3.0 pts |

NVIDIA Q4 FY2025 CFO Commentary, page 1

SiriusPoint financial results presentation slide

A presentation slide becomes a usable table

## Q1 2025 FINANCIAL RESULTS

| Financial Highlights | Q1'24 | Q1'25 |
|---|---:|---:|
| Gross Premiums Written | $881 | $990 |
| Net Premiums Written | $627 | $752 |
| COR (%) | 91.4% | 95.4% |
| Net Income | $91 | $58 |

SiriusPoint Q1 2025 Investor Presentation, page 13

First page of the two-column Docling technical report

Two columns return in reading order

## Docling Technical Report

## Abstract

This technical report introduces Docling, an easy to use,
self-contained, MIT-licensed open-source package for PDF
document conversion.

## 1 Introduction

Converting PDF documents back into a machine-processable
format has been a major challenge for decades...

Docling Technical Report, page 1

FEMA proof of loss form with sections, fields, and checkboxes

Form labels and choices survive the conversion

## PROOF OF LOSS

## TYPE OF PROOF OF LOSS

- [ ] Individual or Household
- [ ] Business
- [ ] Government
- [ ] Tribal Nation/Tribal Government
- [ ] Not-for-Profit

## AMOUNTS CLAIMED

## CLAIMANT CONTACT INFORMATION

FEMA Hermit’s Peak/Calf Canyon Proof of Loss

Run document extraction with one call

View on GitHub

from sie_sdk import SIEClient
client = SIEClient(
    api_key="sk-sie-…",
    base_url="https://api.superlinked.com",
)
# Parse a document → clean markdown, tables and layout kept.
result = client.extract(
    "modeldocling",
    {"document": filePDFnvidia-q4-fy2025-cfo-commentary.pdfbrowse},
)
print(result["data"]["markdown"])

document_bytes=$(base64 < 'nvidia-q4-fy2025-cfo-commentary.pdf' | tr -d '\n')
curl https://api.superlinked.com/v1/extract/docling \
  -H "Authorization: Bearer sk-sie-…" \
  -H "Content-Type: application/json" \
  -d "{\"items\":[{\"document\":{\"data\":\"$document_bytes\",\"format\":\"pdf\"}}]}"

Build the "Doc to Markdown" capability into my app using the Superlinked Inference Engine (SIE).

Context
- SIE is an OpenAI-style inference API. Python SDK: `from sie_sdk import SIEClient`; TypeScript: `@superlinked/sie-sdk`.
- Base URL: https://api.superlinked.com (or my regional endpoint). Auth: Bearer key from env `SIE_API_KEY` (never hard-code it).
- Model: docling (SIE primitive: /extract). Keep the model id configurable.

Task
- Input: an uploaded document (PDF / Office / scan).
- Behaviour: return clean markdown for the document, preserving tables and reading order
- Call the selected SIE primitive once per request and map the response into your domain type.

Deliverables
- A typed client wrapper, an application-level function for this task, error handling for timeouts/empty input, and unit tests with a stubbed client.
- Wire it into my existing stack (ask me which framework if unclear) and add a short usage example.

Run

Output

Q4 Fiscal 2025 Summary

GAAP	Q4 FY25	Q3 FY25	Q4 FY24	Q/Q	Y/Y
Revenue	$39,331	$35,082	$22,103	Up 12%	Up 78%
Gross margin	73.0%	74.6%	76.0%	Down 1.6 pts	Down 3.0 pts

Extracts clean, agent-ready markdown
Keeps tables and layout intact

PRICE

$ / 1k pages ↓

$0.397 SIE docling

$1.5 AWS Textract

$1.5 Google Enterprise Document OCR

$1.5 Azure Document Intelligence Read

Mistral OCR 4 $4

QUALITY

olmOCR ↑

SIE docling 0.32

0.30

0.29

LATENCY

p50 ms ↓

SIE docling 214ms

240ms

260ms

270ms

300ms

Deploy your way

Managed Cloud

Full compute toolkit for your agents with zero ops.

No idle GPUs, pay for what you use
Fits your stack: SDK, API, CLI, MCP
Zero lock-in, self-host the same stack
SOC 2 Type 2, US or EU data residency

$50 Example credit balance

1.3B tokens embedded

120k pages parsed

27M tokens generated (1:1 in:out)

Get started

no credit card required

Self-host with K8s

Easy & scalable deployment in your own cloud.

Terraform to your cloud in minutes
Apache-2.0, same engine as Cloud
Scales to zero, no bill between jobs
Per-tenant pools, no noisy neighbors

Agent prompt

Deploy SIE to our AWS account with the superlinked/sie/aws Terraform module. Docs: superlinked.com/docs/deploymentDeploy SIE to our GCP project with the superlinked/sie/google Terraform module. Docs: superlinked.com/docs/deploymentDeploy SIE to our Azure AKS cluster via helm install. Requirements: superlinked.com/docs/deployment

Deploy guide

Run locally

Run the same models on your own machine.

Runs on NVIDIA GPU or Apple Silicon
One command, no Docker or cluster
All 100+ Cloud models, fully offline
Same SDK and IDs, no code changes

pip install "sie-server[local]" && sie-server servepip install "sie-server[local]" && sie-server serve --device cuda

Quickstart