KIMY · Software engineering & digital solutions
Automated data processing
Structure, validate and route data while retaining transformation traceability.
Scope built around your use case
Structure, validate and route data while retaining transformation traceability.
Design and implementation
We propose selecting a bounded use case, preparing representative examples and comparing results against business criteria. Sensitive actions remain subject to human approval; access and data shared with providers are explicitly scoped.
Features and checkpoints
- Define input formats and quality checks.
- isolate rejected rows without losing the batch.
- trace transformations and rule versions.
These items are a starting point for discussion rather than a fixed package. Features are selected to serve the priority workflow identified during scoping. Dependencies on existing tools and access constraints are reviewed before implementation.
Potential use cases
An internal assistant can search an approved knowledge base and show its sources. An automated workflow can extract information from a document and request approval before updating a business system.
Delivery, testing and continuity
An acceptance scenario should verify the solution against the initial need. Checks cover workflows, permissions, failures and performance relevant to your context. Documentation, access handover and maintenance terms are defined in the contractual scope. Technical choices follow your constraints; no technology stack is imposed before that assessment.
What should I prepare for a proposal?
Describe a workflow, users, expected volumes and tools to connect. Share fixed deadlines and available access. This information helps identify what needs further assessment.
How is the first release decided?
Scoping ranks features by usefulness, risk and dependencies. A first release can then be proposed with acceptance criteria and a separate list of future improvements.