DOCUMENT AI & AUTOMATION
AI over documents. In the context of process, data and responsibility.
A documentʼs value depends on more than its text. Origin, metadata, permissions, process context, related information and human responsibility determine how it can be understood and used.
ALTAIR connects Document AI with DMS/ECM, records management, workflow and enterprise systems so AI results become part of controlled organisational processes.
- DOCUMENTSource
- UNDERSTANDClassification · Extraction · Interpretation
- CONTEXTMetadata · Permissions · Process
- DECIDEBusiness Rules · AI · Human
- ACTWorkflow · Automation · Integration
- MEASUREOutcome
DOCUMENT AI
A document is more than text.
Document AI can identify document type, important information, relationships, completeness, likely next steps and where human judgement is required. Documents become active sources of structured information for enterprise processes.
STRUCTURED INFORMATION
From unstructured content to usable data.
Classification, information and metadata extraction, document understanding and validation turn emails, attachments, forms and documents into enterprise information that can continue through a process—not merely an answer displayed to a user.
FROM INFORMATION TO PROCESS
AI creates most value when its result continues through the process.
Document received → Classification → Extraction → Validation → Context enrichment → Business rule → Automated processing or human decision → Update DMS and connected systems.
PROCESS AUTOMATION
Automate repetitive work. Keep control where decisions matter.
Intake, classification, extraction, metadata, mandatory-data checks, routing, task creation, workflow, notifications, escalation, document generation, system-to-system processing and exception detection can be automated while accountability remains explicit.
HUMAN IN THE LOOP
AI can recommend. Responsibility must remain governed.
Document type, confidence, data quality, business impact, process rules and security context determine whether AI may act automatically or a person must verify and decide. Human-in-the-loop enables safe automation rather than opposing it.
SEMANTIC SEARCH
Search by meaning, not only an exact word.
Semantic search, similar-content discovery, summarisation, comparison, relationship discovery and controlled querying improve access to document knowledge. Every search must still respect the permissions of the underlying DMS.
CONTROLLED Q&A
An answer must come from permitted, traceable context.
Enterprise Q&A identifies source context, enforces permissions, preserves traceability, uses the correct current version and distinguishes advisory output from an action allowed to trigger a process. It is governed access to information, not a generic chatbot.
DOCUMENT GENERATION
AI can help create documents as well as read them.
Drafting, response preparation, summarisation, combining sources, completeness checks, comparison and decision-support preparation remain subject to source control, permissions, process rules, responsibility and approval.
AI INSIDE DMS / ECM
AI should work where information already has context.
DMS/ECM provides content, metadata, versions, permissions, relationships, workflow and audit history. AI processing can use that authorised context, return a validated result and continue through workflow or a business system.
AI & ELECTRONIC RECORDS
AI can assist document processing. It cannot remove records rules.
AI can classify, extract, suggest metadata, identify submission type, search and prepare the next step. Formal records-management, process and security rules remain authoritative in DEPO and other governed records environments.
SECURITY & PERMISSIONS
AI must not see more than the user it assists.
If a user cannot access a DMS document, AI must not expose its content through search, summaries or answers. Identity, authorised context, sensitive-data separation, service inputs, audit, retention, output handling and human verification belong in the AI architecture.
ARCHITECTURE
Enterprise AI is not one model.
Information sources feed authorised context and permissions into extraction, classification, semantic retrieval or generative services. Business rules and workflow determine what happens next; integration returns results; audit and monitoring provide operational control.
PRIVATE, CLOUD & HYBRID AI
AI architecture must reflect the nature of the information.
Enterprise cloud AI, private AI, customer-operated services and hybrid architectures may each be appropriate. The design must establish which data is processed, where, by whom, for how long and who owns each responsibility.
QUALITY & VALIDATION
A convincing answer is not necessarily correct.
AI output can be uncertain. Validation, reference sources, confidence handling, human review, low-confidence routes, error behaviour and traceability where applicable let the process work safely with uncertainty.
MEASUREMENT
Automation must have a measurable outcome.
Useful measures can include automated-processing rate, human-review rate, processing time, exceptions, extraction quality, user corrections and manual work reduced. These are operating measures to define and observe—not invented performance claims.
DOCUMENT AI AS A PROCESS CAPABILITY
The goal is not to add AI to documents. It is to change how the organisation works with them.
Trusted sources, DMS and records, permissions, processes, business rules, integration, human responsibility and measurable outcomes move Document AI from an isolated experiment into enterprise operation.