Understand the inputs
Review document types, data quality, languages, variations and the information the workflow actually needs.
AI & AUTOMATION
Practical intelligence.
Meaningful progress.
Bring AI into a specific business workflow, with clear boundaries, useful outcomes and people in control.
Discuss your requirements
AUTOMATION WITH A CLEAR PURPOSE
Find the repetitive work.
Keep the important decisions visible.
DEANA INFOTECH approaches AI through the operational task it can help with: reading an incoming document, finding relevant information, preparing a review or moving an approved request to the next team. We combine business rules, application workflows and appropriate AI assistance. The starting point is a defined use case, a representative set of examples and an agreed way to judge whether the result is useful.
Define the review
Make exceptions, uncertain results and consequential approvals visible to the appropriate user.
Connect the next step
Plan the handover to ERP, a business application or an accountable process owner.
01
Document intelligence
Turn incoming information into a structured review.
Business documents arrive in different formats and levels of quality. A document workflow can classify an input, extract selected fields and present the source alongside a proposed structured record. We first define the fields needed, the variations expected and the checks that determine whether the record can continue. Feasibility is assessed with representative examples, including incomplete and difficult documents.
Areas we address
- Document intake, classification and field-extraction requirements
- Source-linked review, correction and missing-information handling
- Business-rule checks before information enters the next system
EXAMPLE WORKFLOW
An invoice intake workflow could extract reference, supplier and line information, flag inconsistencies and send the record to a reviewer. The reviewer confirms or corrects the data before an approved handover.
What to define together
An input catalogue, field schema, validation rules, review experience and evaluation examples.
02
Rules-based workflow automation
Move routine work through explicit responsibilities.
Many useful improvements come from clear rules and dependable handovers. We map the trigger, required information, approval conditions and completion evidence for a process. Routing, reminders and status updates can then be designed around agreed responsibilities. Exceptions need a visible owner and an action path, especially when an approval is missing or a connected system cannot accept an update.
Areas we address
- Request intake, routing, reminders and escalation rules
- Role-based approval steps and task ownership
- Status tracking and ERP-connected handovers
EXAMPLE WORKFLOW
A procurement request can move from the employee to a departmental approver, then to the responsible purchasing team. Missing information returns to the requester with a clear reason and a traceable status.
What to define together
A workflow map, rule catalogue, role model, exception paths and end-to-end acceptance scenarios.
03
Knowledge & information assistance
Help people find relevant context for their work.
Teams often need information spread across operating instructions, product documents and internal guidance. A scoped knowledge assistant can be evaluated against an approved collection and the questions employees need answered. Access should follow the information permissions of the user, and answers should make their supporting sources available. Unanswered or ambiguous questions need a clear route to a person.
Areas we address
- Approved knowledge sources and document ownership
- Source references and access requirements
- Evaluation of relevance, unsupported answers and missing context
EXAMPLE WORKFLOW
A support team could ask questions about an approved operating procedure and review the referenced passage before using the answer. Changes to that procedure should follow an agreed update and review process.
What to define together
A source inventory, access scope, question set, answer-quality criteria and a content maintenance process.
04
AI inside enterprise applications
Fit assistance into the place where work happens.
An AI output becomes useful when it fits the application and the user’s next action. We define what information the assistant receives, where the result appears and what a user must check. The integration should preserve permissions, transaction validation and the application’s business rules. Proposed changes should remain distinguishable from changes that have actually been approved and saved.
Areas we address
- Application screens and user journeys for assisted work
- Input and output validation around connected systems
- Approval, error reporting and transaction traceability
EXAMPLE WORKFLOW
An application could prepare a summary of a service request for review. The user checks the source information and confirms the next step; the application records the authorised action through its normal workflow.
What to define together
A defined application interaction, interface requirements, review checkpoints and integration test cases.
05
Access, review & operational controls
Agree the boundaries before introducing AI.
The design discussion includes the information being processed, who may access it, where it is handled and how long it is retained. Teams need to understand the limits of an assisted result and the circumstances that require review. Logging should support investigation while respecting the agreed data-handling scope. Deployment and model-service choices depend on these requirements and the capabilities available for the project.
Areas we address
- Data access, retention and deployment requirements
- Human approval for consequential actions
- Traceability, fallback behaviour and ownership of exceptions
EXAMPLE WORKFLOW
If a document cannot be interpreted reliably, the workflow should route it for manual handling. A system should not present an uncertain extraction as an approved business record.
What to define together
An agreed control and responsibility model, review policy, fallback route and operational documentation.
06
Pilot, evaluate & improve
Measure the result against your own baseline.
A useful pilot uses real process variations with sensitive information handled appropriately. We agree the task, the evaluation set and the acceptance criteria before wider use. Measures may include field-level correctness, review effort, exception handling and the time required to complete the workflow. Results should be reviewed alongside operating cost and maintainability, rather than relying on a single impressive demonstration.
Areas we address
- A bounded pilot and representative evaluation samples
- Quality, review-effort and exception measures
- Release readiness, monitoring and ongoing review
EXAMPLE WORKFLOW
For a document pilot, the team can compare the reviewed output with an agreed answer set, then examine which document types cause errors and whether the review process catches them.
What to define together
A pilot report, acceptance evidence, known limitations and a decision on the next implementation phase.
ASSISTANCE IN CONTEXT
Useful intelligence.
A clear next action.
Bring the source information, proposed result and review decision into one usable workflow. The surrounding application gives people context and keeps the business process accountable.
Explore AI product enquiriesFROM REQUIREMENTS TO EVERYDAY USE
A practical path
through delivery.
Choose the task
Select a repeated process with clear inputs, an accountable owner and a measurable outcome.
Prototype & evaluate
Test representative examples, review exceptions and confirm how the workflow fits your systems.
Introduce & review
Prepare users, agree operating controls and monitor the result before expanding the scope.
PREPARE FOR THE FIRST CONVERSATION
Bring the context.
We can shape the next step.
A clear starting point helps focus the discussion on your priorities. Share a summary first; detailed records and access can be arranged through the project process.
The process today
Who performs the task, how often it occurs, where delays appear and how completion is checked.
Representative examples
Typical and difficult documents or requests, with confidential details removed or shared through an agreed channel.
The review requirements
Which outputs can be suggested, who approves them and which actions must always remain under explicit control.
The operating environment
Existing applications, integration needs, access rules and your preferred data-handling constraints.
YOUR QUESTIONS, ANSWERED
Questions about
your next step.
Does every automation need AI?
No. A stable task with explicit rules may be better served by conventional workflow logic. AI can be evaluated where interpreting variable information is useful, while deterministic checks and approvals remain part of the surrounding process.
Can AI output be sent straight to ERP?
That decision depends on the use case, validation and approved controls. A proposed or extracted value should not automatically be treated as a correct business transaction. The agreed process defines review, validation and posting authority.
How do you evaluate an AI use case?
Start with representative examples and a clear definition of the expected result. Review correctness, omissions, exceptions, user effort and operational cost. Acceptance criteria and known limitations should be recorded before wider deployment.
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The website AI Assistant currently displays Coming Soon. The service enquiries on this page are for discussing project requirements; they do not activate a chatbot or a live AI service on this website.
LET’S CONNECT
Good technology starts with
a better conversation.
Tell us where work gets complicated. Let’s explore a clearer way forward.