AI document automation
Most of what a company knows is still locked in scans, forms, and PDFs. We build the pipeline that reads them the way a trained reviewer would — layout, tables, seals, handwriting — and hands your systems validated records.
Core technologies
Recognition is only the start.
Beyond plain recognition — several AI techniques combined to take a document from understanding all the way to automation.
- OCR
- Turns the characters in an image or document into machine-readable text.
- LLM
- A large language model trained on vast amounts of text to understand and generate language.
- RAG
- Retrieves relevant material first, then generates an answer grounded in it for higher accuracy.
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Document understanding
High-accuracy OCR fused with language models to read the context and structure of unstructured documents, then build a standard dataset from them.
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Knowledge retrieval
Retrieval-augmented generation across internal document and knowledge bases, so answers are grounded in your material instead of the model's priors.
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Agentic execution
Agents that read intent and work context, decide the next step, and carry the task through rather than stopping at a suggestion.
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Workflow automation
Intake, extraction, validation, and system entry wired into one path. The repetitive middle of the process disappears.
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Rule-based review
Sector regulation and internal rules encoded so the system checks every required field and flags what is missing before a human sees it.
How it runs
Read, structure, validate — then hand off.
From intake through recognition, classification, integration, review, and reporting — the whole business process, automated.
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01
Recognition
Read the document as it actually arrives — skewed, low-resolution, stamped, handwritten.
Hybrid recognition
Vision models paired with OCR recover text from skewed scans, low-resolution captures, and creased or damaged pages.
Layout parsing
Tables, charts, signatures, seals, and handwriting are separated from body text, with reading order and structural context preserved.
Multilingual & forms
Mixed Korean-English documents and complex form regions are classified precisely, holding accuracy across source conditions.
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02
Datafication
Turn what was read into records a database will accept — and prove they are right.
Contextual extraction
Key fields are pulled from contracts, receipts, tax invoices, and certificates by NLP and LLM reasoning, not fixed templates.
Schema transformation
Fragmented text becomes JSON, CSV, or relational rows your systems can consume immediately.
Validation & control
Every extraction carries a confidence score and passes rule-based cross-checks. This is the step that keeps hallucinated values out of the database.
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03
Automation
Close the loop into the systems your team already works in.
End-to-end pipeline
Intake, recognition, classification, transformation, and system entry run as one path, removing the manual handoffs in between.
Legacy integration
ERP, CRM, and groupware connect over API for real-time read and write. No rip-and-replace.
Verification & reporting
Summary reports, missing-field detection on contracts, and compliance review run on the extracted data, not on the raw scan.
Where it applies
Built for the documents that carry risk.
The pipeline is domain-agnostic. What changes per sector is the rule set, the target schema, and what counts as an error.
- Sector 01
Finance & Legal
Documents where an extraction error is a liability. Precise field capture and compliance review on financial and legal paperwork.
- Sector 02
HR, Admin & Contracts
Repetitive contract review and comparison, plus the employment and benefits records that sit behind every HR request.
- Sector 03
Disclosure, Manufacturing & Quality
Standard-form documents in quality management and corporate disclosure, where the form itself is the regulation.
- Sector 04
Pharma & Regulatory
Clinical and non-clinical submissions with dense tables and fixed structures. The domain our MFDS R&D has been built in.
Applied AI
Applied AI, wired into the business.
We don't ship demos. Each engagement ends with a model, a pipeline, and an operator who can run it without us.
- A.01
Data Analysis & Predictive Modeling
Pattern discovery and forecasting on your operational data, with interpretable outputs for decision-makers.
- A.02
Natural Language Processing
Document processing, sentiment, and conversational systems that speak your domain — not a generic one.
- A.03
Multimodal Document AI
Layout-aware extraction fused with LLM reasoning for contracts, filings, and clinical forms. Classify, parse, and route at scale.
- A.04
Custom Model & Evaluation Harness
Tailored models for healthcare, finance, and manufacturing — delivered with the eval harness that proves they work, not a slide deck.
- A.05
Integration with Existing Systems
AI that plugs into ERP, CRM, and legacy pipelines without forcing a rip-and-replace.
- A.06
Governance, Lineage & Audit Trails
Data lineage, evaluation reports, and audit-grade logs shipped alongside every model — aligned with the AI Framework Act high-impact obligations.
- A.07
Agentic AI Operations
Sustained execution beats clever prompts. We wire AI agents into production systems with the access controls, audit trails, and rollback paths that keep humans accountable.
- A.08
Enterprise RAG & Knowledge Systems
Hybrid retrieval, domain-tuned embeddings, and access-controlled context graphs. The knowledge source — not the model — is the primary investment.
- A.09
Edge Inference Stack
Model packaging, NPU/GPU acceleration, and edge-cloud orchestration for sub-10ms agentic workloads at the data source.
Security & compliance
Compliance, by construction.
We align engineering practice with the Korea AI Framework Act, the FDA-EMA joint principles, and MFDS generative-AI medical device guidelines.
- Effective 2026-01-22
Korea AI Framework Act
Risk classification documented per deployment, with generative-AI labeling and audit-grade logs applied by default.
- Published Jan 2026 · 10 principles
FDA-EMA Good AI Practice
Context-of-use specifications, human-in-the-loop checkpoints, and data lineage from source document to submitted artifact.
- World's first review guideline
MFDS generative-AI medical devices
For work that touches a regulated medical workflow, we treat the MFDS guidelines as binding.
FAQ
Common questions
What documents can it handle?
Documents in every shape — contracts, receipts, tax invoices, medical certificates — through to clinical and non-clinical submissions. Skewed scans, low resolution, and mixed Korean-English documents are in scope.
Does it connect to the ERP or groupware we already use?
Yes. It connects to ERP, CRM, and groupware over API for real-time read and write. Nothing gets replaced.
How do you catch values the AI read wrong?
Every extracted value gets a confidence score and passes rule-based cross-checks. Anything below the threshold is not passed through automatically — it goes to a reviewer.
Can it run on our own servers?
We support on-premise and cloud, matched to your environment. Cloud deployments default to Korean regions — NHN Cloud, KT Cloud, NAVER Cloud.