HyperAPI by Hyperbots: document intelligence APIs for enterprise solutions

One API to parse, extract, classify, split and redact any enterprise document, from invoices, statements and POs to contracts, KYC packs, HR files, claims, lab reports and email. It returns schema-validated JSON with a confidence score and a source bounding box for every field. HyperAPI runs inside your own applications, and an MCP server for AI agents is available on request.

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Five operations, one endpoint

Every response includes per-field confidence, source boxes, document-level checks (for example, line items must add up to the total), and request_id, model_used, duration and page count for audit. You can run calls sync, async (X-Async) or through the Batch API with webhooks. Results are cached for 24 hours, and cached re-runs are not billed.

Get a free key (30 pages/month, no credit card), set HYPERAPI_KEY and follow the quickstart to use the SDK or REST API:

from hyperapi import HyperAPIClient

with HyperAPIClient() as client:
    doc = client.process("packet.pdf")          # parse + extract in one call
    kind = client.classify("packet.pdf")        # what is it?
    parts = client.split("packet.pdf")          # where does each document start and end?
    safe = client.redact("packet.pdf", mode="deidentify")

What it reads

• Formats: native, scanned and mixed PDFs (including forms and files of several hundred pages); PNG, JPEG, TIFF, HEIC and other images; Word, Excel and PowerPoint, with workbooks parsed natively; and email (EML, MSG).
• Document types: a 100-type reference taxonomy in 15 categories, covering payables, statements, payments, procurement, logistics, HR, onboarding and compliance, financial statements, credit and lending, healthcare, legal, and operations.
• Hard pages: skewed, rotated and shadowed scans, stamps and seals, handwriting, dense multi-column layouts, and tables with hundreds of rows. Text in more than 100 languages, including mixed-language documents.

📊 Open synthetic datasets for document AI

These are fully synthetic, labelled in HyperAPI's output formats, and free to use under CC BY 4.0. Use them to benchmark HyperAPI or any other document pipeline.

Built for agents

HyperAPI's five operations can be exposed to agents over MCP (available on request: hyperapi-support@hyperbots.com), so coding and workflow agents (Claude Code, Cursor, GitHub Copilot, Codex, OpenCode and any other MCP client) can read documents reliably. With Agent Builder (beta), you give an agent documents and a goal, for example "reconcile this month's vendor invoices against open POs, flag anything over $500 and mask bank details". The agent picks the operations, runs them with guardrails and returns one structured result with a citation for every field.

In production

• Invoice processing at scale: a US marketing-services enterprise with 21 entities on SAP and NetSuite processes more than 1M invoices a year through an agent built on HyperAPI. It reports 99%+ extraction accuracy, 80% less manual AP work and 40% lower AP cost.
• $10B manufacturer: more than 300K invoices a year posted into SAP ECC, with a 40% reduction in outsourced (KPO) staff.
• Dense archive digitisation: more than 1M multi-column directory pages parsed into structured records within a fixed 10-day window.
• Document reconstruction pilot: one parse call per file across vector PDF, scanned PDF, XPS, Outlook email and Excel, with 97% median text recall on the client's own files.

The models

HyperAPI runs on HyperLM, Hyperbots' mixture-of-experts document stack. It combines small, vision and large language models with multimodal encoders for layout, text and image, and decoders specialised by task and field. HyperLM is trained on 35M+ real-world documents and 0.5B synthetic samples and retrained continuously. Related research: DocuLite (4th Deployable AI Workshop, AAAI 2026) shows how to adapt small LLMs and VLMs to invoice extraction with synthetic data instead of human annotation.

Security and enterprise deployment

ISO 27001, SOC 1 Type 2 and SOC 2 Type 2. Documents are processed in memory and deleted when the run completes unless you enable retention, and customer data is never used for training. Redaction can run before a document reaches any model. BYOK, IP allow-listing, SSO (Okta, Entra ID, Google Workspace, OneLogin), role-based access and full audit trails are available. Enterprise evaluations run as a POC on your own sample documents, scored field by field against an answer key.

About Hyperbots

Hyperbots builds AI agents for finance and accounting (procure-to-pay, order-to-cash and the CFO office). HyperAPI is the same document intelligence that runs inside those agents, offered as APIs. The team has 150+ people, including 50 machine-learning scientists, with 50+ patents and 100+ publications.

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