Important information is trapped in scanned or inconsistent documents.
Technology services
AI & Document Intelligence
Human-in-the-loop AI workflows for OCR document extraction, classification, RAG knowledge search over policies/research, and meeting notes automation.
Service explained
What is AI & Document Intelligence?
AI and document-intelligence services apply OCR, classification, retrieval, and language-model capabilities to document-heavy workflows. The purpose is to help extract, organize, find, or summarize information while keeping source evidence and human review available.
These systems are probabilistic and can misread, omit, or generate incorrect information. Sensitive decisions therefore require defined confidence thresholds, access controls, testing, source citation, exception handling, and accountable human oversight.
What it addresses
When this service becomes relevant
Teams cannot efficiently search approved policy or research collections.
Draft meeting notes require repetitive organization and review.
Capabilities explained
What the documented scope means in practice
Each capability below is part of the archived service description. Its inclusion in a specific engagement depends on the requirement agreed during scoping.
Extract
Extraction uses OCR and document parsing to turn images or files into machine-readable text and fields. Results require validation because layout, scan quality, handwriting, tables, and unfamiliar formats can create errors.
Search
Search can combine indexing and retrieval-augmented generation to locate relevant passages from an approved corpus. Answers should preserve source references and permissions so users can inspect the underlying material.
Review
Review checks extracted fields, classifications, summaries, or retrieved answers against the source and the decision’s sensitivity. Low-confidence and exceptional cases should be routed to a responsible person.
Workflow explained
Core workflow
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Step 1
Extract
Documents are ingested, parsed, and converted into text, metadata, or candidate fields with provenance retained.
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Step 2
Search
Approved content is indexed and retrieved in response to a query, with access boundaries and source passages preserved.
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Step 3
Review
A human verifies material outputs, corrects errors, and decides whether the information is suitable for the intended use.
Useful inputs
Information that helps define the requirement
- →Representative documents, formats, quality levels, and expected fields
- →Approved knowledge sources, access rules, retention, and privacy constraints
- →Evaluation cases, risk thresholds, reviewers, and exception procedures
Documented outputs
What an agreed scope may produce
- →Document workflow and evaluation design
- →Extraction, classification, search, or note capabilities within scope
- →Source, confidence, review, access, and operating controls
Clear answers
Frequently asked questions about AI & Document Intelligence
Service-specific answers about terminology, scope, controls, and practical use.
What is OCR in document intelligence?
Optical character recognition converts text in scans or images into machine-readable characters. Accuracy varies with layout and image quality, so important fields require validation.
What is retrieval-augmented generation?
RAG retrieves passages from an approved source collection and supplies them to a language model when forming an answer. It can improve grounding, but the answer and cited source still require review.
Where is human review required?
Review is especially important for low-confidence extraction, ambiguous classification, sensitive personal information, compliance or financial decisions, and any output that could materially affect a person or organization.
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A focused first conversation
Discuss the service in the context of your priorities.
Use a 30-minute call to clarify the work, its place in your operating model, and the most useful next step.