We design automation and artificial intelligence integrations around a specific process. We first understand where manual work is concentrated, then assess which solution makes sense to introduce.
Which task is repeated, using which data and with what exceptions? Clarifying these elements helps distinguish steps that can follow defined rules from those requiring interpretation or a human decision. This is also how we assess whether AI is appropriate.
Information and operations can be linked in a designed workflow, reducing the need to copy data manually.
We define where data is checked, how exceptions are flagged and who needs to intervene.
We choose a limited task and assessment criteria before extending automation to other processes.
Each scenario is assessed against your project’s needs and constraints.
Recurring procedures to collect, transform and organise data, as documented in the IDM project.
Exchange information between business systems after assessing access and integration options.
Classification or preparation of content and information, assessed using representative data and human review where needed.
These activities form the basis of the proposal. Features, materials and responsibilities are defined for each project.
Tasks, frequency, input data, exceptions and responsibilities.
Compare deterministic rules, integrations and AI against the problem to be solved.
Test representative cases, with acceptance criteria and explicit limitations.
Introduce the solution into the agreed workflow, with error handling and instructions for users.
We identify a repetitive step and map how it is currently handled.
We define data, rules, controls and situations requiring a person.
We build and test the workflow against typical cases and exceptions.
We agree on a gradual introduction and how its operation will be monitored.
For IDM, we built automated royalty data procedures within a platform using a Node.js and Express backend and REST APIs. For AI integrations, tools and providers are defined after assessing requirements, data and conditions of use.
Applications for artists and staff, backend services and automated royalty data processing. A project to develop and evolve a software platform.
No. Traditional automation may be suitable when a step follows defined rules. We consider AI when the nature of the problem warrants an interpretive component.
We define test cases, expected output quality, exceptions and necessary human interventions. Any savings must be measured in the actual process, rather than presented as a sales promise.
Before choosing a solution, we clarify which data is involved, who can access it and which processing and retention constraints apply. The technical configuration follows this analysis.
Describe the process, approximate volumes, tools involved and who currently checks the result. A description is enough for the first discussion; please do not send confidential data.
For the first discussion, share your objective, context, timeline and approximate budget. We will get back to you to explore the project and define the next steps.
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