Manual work repeats every week
The process is known, but people still copy, check, route or summarise information manually.
Use cases
Practical routes where data, workflow software and AI create real control.
These examples show where Dattha typically helps: less manual work, more reliable data, clearer ownership and AI that can be used without losing governance.
When Dattha is relevant
Most use cases start with the same operational symptoms.
Data exists, trust does not
Teams have dashboards or exports, but spend time debating definitions, quality or ownership.
AI is interesting, but the foundation is unclear
There is ambition, but not enough control over source data, permissions, context or auditability.
Existing software does not fit the workflow
The core systems stay, but teams need a smarter workflow layer around them.
Examples
Concrete use cases, designed to start focused and scale only when value is proven.
Data foundation
One version of the truth for KPIs and reporting
Problem: Teams use different exports, definitions and spreadsheet logic, which slows decisions and creates debate about the numbers.
Dattha route: Dattha defines ownership, KPI logic, data quality rules and a reusable data model that dashboards, reports and AI flows can trust.
- Consistent KPI definitions
- Less manual correction
- Stronger basis for analytics and AI
Workflow software
Internal workflow portal for handovers and approvals
Problem: Requests move through email, chat and spreadsheets, making status, responsibility and audit trail difficult to control.
Dattha route: We build a role-based workflow layer with structured intake, status logic, approvals, notifications and audit logging.
- Clear ownership per step
- Fewer missed handovers
- Better auditability
AI application
Document intake with AI-assisted extraction and validation
Problem: Teams spend too much time reading PDFs, emails and attachments before information can be checked or processed.
Dattha route: A controlled AI-assisted intake flow extracts key fields, flags uncertainty and routes exceptions to the right person.
- Faster intake
- Lower manual workload
- Human control on exceptions
Customer operations
Customer request triage and follow-up
Problem: Customer requests arrive through multiple channels and depend too much on individual memory or manual follow-up.
Dattha route: Dattha structures intake, prioritisation, summaries, next actions and task routing while keeping teams in control.
- Faster response
- Consistent follow-up
- Less operational noise
Finance and invoicing
Invoice and cash-flow follow-up workflow
Problem: Invoice status, exceptions and follow-up actions are spread across systems and inboxes.
Dattha route: We connect data sources, define status logic and build a workflow view for actions, exceptions and predictable follow-up.
- Better cash-flow visibility
- Fewer manual checks
- Clear exception handling
Governance and control
AI-ready governance and access model
Problem: AI initiatives are blocked because data access, sensitive fields, ownership and lineage are not clear enough.
Dattha route: Dattha helps define roles, access patterns, metadata, lineage and security controls before AI is scaled.
- Safer AI adoption
- Clearer compliance position
- Reusable governance model
Choosing the first route
The right first step depends on what is blocking value today.
1
Foundation route
Start with data quality, definitions and governance
Best when trust, ownership or AI readiness is the main constraint.
2
Workflow route
Start with one process where friction is visible
Best when teams lose time in handovers, status tracking, exceptions or manual coordination.
3
Application route
Start with a focused internal tool or AI-assisted flow
Best when the value is clear and the organisation needs a practical layer that teams can use quickly.
Questions
Use case questions
Do these use cases require replacing existing software?
Usually not. Often the best first step is a workflow, data or application layer around existing systems, with integration where it creates clear value.
Can Dattha build a custom use case for our sector?
Yes. The examples show common patterns, but the delivery route is shaped around your process, data landscape, permissions and business case.
When should we start with data foundation instead of automation?
Start with the foundation when definitions, quality, ownership or security are unclear. Automation becomes more reliable once those basics are controlled.
How small can the first version be?
Small enough to prove value in one workflow or decision point. The first version should reduce visible friction without creating a large platform project too early.
Next step
Want to find the use case with the strongest first value?
Use the workflow scan or book an introduction to map the first route with the least risk and most visible impact.