2026 年金融文件處理 AI 推理平台評測：Superlinked SIE 為何列為首選

A production incident usually gets my attention faster than an invoice. But in financial document processing, an invoice can be a warning of its own: you're paying a model to do work that a simple parser could have handled.

There's a Swahili proverb I keep coming back to whenever I see a team rush into a technical decision: Haraka haraka haina baraka, hurry hurry has no blessing. In this case, the expensive mistake is picking a document-AI service because it says "PDF" on the landing page, sending every filing through it, and checking the bill only when finance comes knocking.

Let's look at how to choose an inference platform for financial documents without paying to process things that were never hard to read in the first place.

Introduction: Turning Clean Markup Into Pixels (and Back Again)

Picture a team building a pipeline to read US annual reports. They sign up for a document-AI service, pay per-page rates, and feed it every 10-K they can find.

Here's the punchline: those 10-Ks were born-digital HTML. Every figure on the face of the financial statements was already tagged in Inline XBRL. The service dutifully took perfectly clean, machine-readable markup, rendered it into pixels, and then used OCR to turn those pixels back into text. That's like printing an email so you can scan it and email the scan.

Meanwhile, the one source that genuinely needed document AI, a foreign issuer's annual report published only as a PDF, was the part that broke. A multi-page segment table split across pages and lost its header row, so every number below the page break was now an orphan with no column to belong to.

Two problems, one root cause: the team picked a tool before they understood which of their documents were actually hard. So before we rank anything, let's fix that.

Before You Pick a Platform: Do You Even Need Document AI?

This is the part most "best platform" listicles skip, and it's the part that saves you the most money.

EDGAR primary documents are HTML, and have been Inline XBRL HTML since the 2019 to 2021 phase-in. EX-99.1 earnings releases are HTML too. For tagged statement figures, you don't need OCR at all. You need an HTTP client:

import requests

# The SEC asks for a descriptive User-Agent with contact details.
HEADERS = {"User-Agent": "Your Name your.email@example.com"}

cik = "0000320193"  # Apple, zero-padded to 10 digits
url = f"https://data.sec.gov/api/xbrl/companyfacts/CIK{cik}.json"

response = requests.get(url, headers=HEADERS, timeout=30)
response.raise_for_status()
facts = response.json()

revenue = facts["facts"]["us-gaap"][
    "RevenueFromContractWithCustomerExcludingAssessedTax"
]["units"]["USD"]

annual = [
    fact
    for fact in revenue
    if fact.get("form") == "10-K"
    and fact.get("file")
    and fact.get("end")
]

# Sort by filing date instead of assuming the last item is latest.
annual.sort(key=lambda fact: fact.get("file", ""))

latest = annual[-1]
print(latest["end"], latest["val"])
# Period end date and value in base units (dollars, not millions).

No pixels, no per-page fee, and the values come back in base units, so nobody has to guess whether "394,328" means thousands or millions.

There is a catch, and it matters. The companyfacts and frames APIs only return entity-wide facts on standard taxonomies. Company extension tags, segment facts and member facts live in the filing's own iXBRL, which is still HTML you can parse. Same story for untagged content like MD&A tables and press-release exhibits: parse the HTML tables.

So where does a document-AI platform actually earn its fee? Two places:

1. PDF-only sources. Non-US annual reports, data-room documents, client uploads, and paper-era scanned exhibits.
2. The chain after the text exists. Pulling fields with their period and scale, embedding passages for search, and reranking whatever retrieval returns.

Notice that the second one has nothing to do with OCR. That's the part people forget, and it's exactly where a single-API platform starts to pull ahead.

The Scorecard: Four Questions That Actually Matter

I ranked every platform against one question, broken into four parts:

1. Coverage: Does it give me PDF OCR, field extraction, embeddings and reranking behind one API?
2. Price transparency: Does each step have a published per-unit price I can put in a spreadsheet before I run anything?
3. Model choice: Can I pick (and swap) the model at each step, or am I stuck with whatever the vendor chose?
4. Data residency: Is there a path to run the exact same thing inside my own VPC for client documents that must never leave?

One distinction before the ranking: these platforms don't all start from the same place. Some are document-processing APIs, some are ingestion platforms, and some are infrastructure for serving models you package yourself. I'm comparing them against the same practical requirements, but that doesn't make their outputs or operating models interchangeable. The right choice still depends on which parts of the pipeline you want the platform to own.

Financial data makes question 4 non-negotiable more often than you'd think. Client notes, portfolio data and data-room documents frequently come with contractual "this stays in your environment" clauses, and "we'll figure out self-hosting later" is how a three-week project becomes a six-month security review.

1. Superlinked Inference Engine (SIE): The Whole Chain, One Client

SIE is an inference engine built for task-specific models, and the first thing worth understanding is that it runs three ways with the same engine underneath:

• SIE Cloud: the managed service. Superlinked runs the GPUs, you call an API, you pay for what you use.
• Self-hosted: the same engine under Apache 2.0, deployed into your own Kubernetes cluster in your own VPC, with Terraform modules for AWS, GCP and Azure. Here, you operate the cluster.
• Local: the same models on a single NVIDIA or Apple Silicon machine with one command. Great for prototyping on your laptop before anyone approves a GPU budget.

That "same engine, same model IDs, three deployment modes" property is the quiet superpower here. The pipeline you prototype locally is the pipeline you cost on SIE Cloud, which is the pipeline you deploy into your VPC when a client contract demands it. No rewrite, no "the self-hosted edition has different models," no surprises.

Coverage of the Chain

The SIE Cloud task menu lists OCR, Doc to Markdown, Doc field extraction, Structured output, Named entities, Search and Rerank, each with per-model prices. For a filings pipeline, that's every step after "the PDF arrived" on one client.

Here are a few of the listed prices (as of 2026-09-30):

That reranker price looks scary until you do the math. A top-50 rerank is 50 query-document pairs, so:

50 pairs x ($61.89 / 1,000,000) ≈ $0.003 per query   (4B reranker)
50 pairs x ($6.63  / 1,000,000) ≈ $0.0003 per query  (0.6B reranker)

Less than a third of a cent per query for the big one. I've seen teams spend more than that on the logging for the same request.

Swapping Models Is a String Change

This is the part I genuinely love. Because every step lives on the same client, changing your OCR or extraction model is a model ID change, not a migration. That makes the single most valuable experiment in document processing, "which model actually handles our ugly PDFs best?", cheap to run.

Conceptually, your pipeline config ends up looking like this:

# Illustrative only: the shape of the pipeline, not SIE's exact client API.
# Check the SIE docs for the real client calls and full model IDs.
PIPELINE = {
    "doc_to_markdown": "docling",
    "ocr": "LightOnOCR-2-1B",          # try PaddleOCR-VL-1.5, MinerU2.5-Pro or GLM-OCR self-hosted
    "extract": "Qwen3.8-27B-FP8",
    "rerank": "Qwen3-Reranker-4B",      # drop to the 0.6B if quality holds on your eval set
}

Remember that foreign issuer's report with the segment table that lost its header row? This is how you fix it: run the same 50 nasty pages through three OCR models by changing one string, compare the output, and keep the winner. SIE's model catalog adds self-hosted OCR options like PaddleOCR-VL-1.5, MinerU2.5-Pro and GLM-OCR, so you're not limited to one vendor's opinion of what a table looks like.

Costing It Before You Run It

Published per-model prices let you estimate parts of the monthly bill before a single page hits a GPU. This small example calculates Doc-to-Markdown parsing and reranking only; it does not include OCR, field extraction, embeddings, infrastructure, or other operational costs. Treat it as a starting point for a cost model, not the full pipeline bill.

# Partial estimate only: parsing + reranking.
# Add OCR, extraction, embeddings, infrastructure, and other costs separately.
PRICES = {
    "docling_per_1k_pages": 0.397,
    "rerank_4b_per_1m_pairs": 61.89,
    "rerank_06b_per_1m_pairs": 6.63,
}

def monthly_cost(pdf_pages: int, queries: int, top_k: int = 50, reranker: str = "rerank_4b_per_1m_pairs") -> dict:
    parsing = pdf_pages / 1_000 * PRICES["docling_per_1k_pages"]
    reranking = queries * top_k / 1_000_000 * PRICES[reranker]
    return {"parsing": round(parsing, 2), "reranking": round(reranking, 2), "total": round(parsing + reranking, 2)}

print(monthly_cost(20_000, 100_000))
# {'parsing': 7.94, 'reranking': 309.45, 'total': 317.39}

print(monthly_cost(20_000, 100_000, reranker="rerank_06b_per_1m_pairs"))
# {'parsing': 7.94, 'reranking': 33.15, 'total': 41.09}

(Field-extraction tokens depend heavily on your schema and page density, so measure those on a sample and add them in.)

That's the kind of number you can put in front of a manager on day one. Try doing the same thing with a vendor whose pricing page says "Contact Sales."

Compliance

For the managed service, Superlinked lists SOC 2 Type 2 along with US or EU data residency. For anything that can't leave your environment at all, you self-host the same engine in your VPC, with the same model IDs. The code is in the open-source SIE repository, so your security team can actually read what they're approving.

My honest take: SIE is the only platform on this list that ticks all four boxes. OCR, extraction, embeddings and reranking on one API. A published price for every step. Open weights you choose and swap by model ID. And the same engine on SIE Cloud or in your own cluster under Apache 2.0. For a financial document pipeline, that's the production path.

A Quick Reality Check on Per-Page Pricing

Here's what 10,000 PDF pages costs at listed rates across the platforms in this article. Important caveat: these tiers don't all produce identical output (plain OCR text versus layout-aware structure versus form fields), so treat this as a sense of scale, not a benchmark. Benchmark on your own PDFs.

Prices as listed on 2026-09-30; Azure figures are US East and Textract figures are US West (Oregon), and other regions differ.

The price difference is worth investigating, but don't treat these rows as interchangeable. Doc-to-Markdown conversion, OCR, and layout-aware parsing can produce different outputs, and the right choice depends on what the downstream pipeline needs. If Markdown is sufficient for your documents, SIE's listed docling rate is an attractive starting point. If you need reliable table structure or form fields, benchmark that output directly against your requirements before comparing the price tags.

The Rest of the Field

To be fair to everyone else, these are all real, capable products. They just answer my four questions differently.

2. Azure AI Document Intelligence

Formerly Form Recognizer, now listed as Azure Document Intelligence in Foundry Tools. Its standout is the Layout model: tables come back with structure and there's no model to choose. Containers are available, and Azure's pricing page notes container pricing matches the cloud service. Embeddings (Azure OpenAI) and a semantic ranker (Azure AI Search) live in the same tenant and the same bill.

The trade-off: the chain is assembled from several Azure services, each with Azure's models. If you're an all-Azure shop, that's comfortable. If you want to pick the model at each step, it's limiting.

3. Google Cloud Document AI

A set of hosted processors on GCP: Enterprise Document OCR, Form Parser, Layout Parser, Custom Extractor, plus a classifier and splitter. The Layout Parser returns chunks along with layout, so part of your retrieval prep arrives pre-done. Form Parser and Custom Extractor list at $30 per 1,000 pages up to one million pages.

The trade-off: embedding and ranking are separate Vertex services, and processing and billing are on GCP. Model your tier mix carefully, because sending every page through a custom extractor costs many times what OCR does.

4. Amazon Textract (with Bedrock)

Inside an all-AWS estate, Textract is zero-ops and the security review is short because everything shares the same account and IAM. Pair it with Bedrock Data Automation for structured output from a "blueprint" at $0.040 per page for 30 fields or fewer.

The trade-off: there's no self-hosted edition, and watch the Forms tier: at $0.05 per page it's thirty-three times the plain text rate. Flip it on for every page of a 300-page annual report and you'll feel it.

5. Reducto

One API for Parse, Extract, Split, Classify and Edit, plus Studio, a visual interface a non-engineer can use to inspect results. It's strong on complex tables with little setup.

The trade-off: on-prem and VPC deployment are Enterprise-only, you configure processing options rather than the underlying models, and embedding and reranking happen elsewhere.

6. Unstructured

An ingestion and ETL platform with an Apache 2.0 open-source library. It states 40+ connectors and 45+ file types, which makes it genuinely useful upstream of an inference layer if you're pulling from shared drives, data rooms and object stores.

The trade-off: you choose from the hosted models it integrates rather than arbitrary open models, reranking sits elsewhere, and in-VPC deployment is on the Business plan.

7. Baseten

Managed GPU inference for models you package yourself, billed per GPU minute (an H100 is $0.10833 per minute, about $6.50 an hour) or per token on Model APIs.

The trade-off: it's not priced per page and it doesn't ship document models; you package and operate them. It's the right call when you're bringing your own model and want to run it yourself.

(Runtimes like vLLM, SGLang and TEI often sit underneath platforms like these. They're engines, not rivals, so I haven't ranked them.)

Summary and a Useful Cheat Sheet

What I'd Tell My Past Self

A few honest lessons, in the spirit of not pretending anyone gets this right on the first draft:

1. Sort your documents before you sort your vendors. HTML and XBRL first, PDFs second. The cheapest page to process is the one you never send to a model.
2. Price the whole chain, not just the OCR. Extraction, embedding and reranking are recurring costs that scale with queries, not pages. A platform that looks cheap at step one can get expensive at step four.
3. Build a nasty-PDF eval set early. Fifty of your ugliest pages, multi-page segment tables included. If swapping a model is a string change, there's no excuse not to test three of them.
4. Decide your data-residency story on day one. It's much easier to choose a platform that self-hosts the same models than to retrofit "this can't leave our VPC" six months in.

Frequently Asked Questions

Do I need document AI for 10-K and 10-Q filings?

For the tagged statements, no. The SEC's companyfacts and frames APIs return entity-wide facts on standard taxonomies in base units. Company extension tags and segment or member facts need the filing's own iXBRL, which is HTML you can parse. Document AI earns its cost on PDF-only sources and on the extraction and retrieval steps after that.

Why not just keep everything in Azure?

You can, and Azure assembles the chain from Document Intelligence, Azure OpenAI and AI Search. SIE puts OCR, extraction, embedding and reranking on one API with open weights you choose, on SIE Cloud or in your own VPC. One client plus a catalog of open models is why I'd run a filings pipeline on SIE.

What about IFRS filers and data that can't leave our environment?

Foreign private issuers filing 20-F or 40-F appear in the XBRL APIs under the ifrs-full taxonomy, but their home-market annual reports are often PDF-only, which is exactly where OCR earns its keep. For client notes or portfolio data that must stay put, run the same SIE models self-hosted in your VPC or locally; the deployment documentation covers the Terraform and Helm paths.

Conclusion

The expensive mistake in financial document processing is rarely the model. It's sending the wrong documents to it, paying per page to read HTML that was never hard to read, and then discovering that the one PDF that actually mattered broke on a table nobody tested.

SIE fixes the parts that a platform can fix. It covers OCR, field extraction, embedding and reranking behind one API, on SIE Cloud or self-hosted on your own GPUs under Apache 2.0. The open weights are the document models you actually put into production, and you swap them by model ID. And because every model has a published price, you can cost the whole chain against your own page and query counts before you run a single thing.

Haraka haraka haina baraka. Slow down long enough to look at your documents, and the right platform picks itself.

Final Thoughts & Conversation

Financial documents are a perfect example of a problem where the tooling question is easy and the thinking question is hard. Which of your documents are actually difficult? Which steps scale with pages, and which scale with queries? Which data can never leave your cluster? Answer those first, and the platform choice becomes almost boring, which is exactly how infrastructure decisions should feel.

Have you built a pipeline over 10-Ks, IFRS reports or messy client PDFs? Did you get burned by a per-page bill or a table that split across pages and took its headers with it? I'd love to hear your horror stories and your favorite OCR models in the comments below!

A Quick Side Note:

I regularly write about backend performance, AI infrastructure, system reliability, and practical engineering trade-offs from the real world. If you enjoy deep dives into document pipelines, defensive engineering, or building things that don't quietly burn through a budget, I'd love to connect and chat!

You can find me on LinkedIn, follow my latest technical thoughts on Twitter/X, or check out my open-source projects and previous articles on GitHub.

Thanks for reading, and see you in the next one!