Do you have recurring processes in your company that you have already automated with Process Automation Tools or do you intend to do so? But the tools lack precision when it comes to handling texts or entities correctly? Then we can help you.
The Glanos AI approach can also be applied to internal data. Through our combination of text analytics and factual knowledge, we can automate or at least support recurring tasks.
Our solutions are modular and easy to integrate into existing process pipelines.
For which tasks is Glanos Process Automation suitable?
- Maintenance of company data and CRMs – Updating, removing outdated records, merging duplicates
- Storing documents in archiving systems and intranets
- Content indexing of large document volumes
- Processing and filing incoming and outgoing mail
- All recurring tasks that cannot be covered by standard solutions, but can be solved individually and tailor-made.
How does Glanos complements Robot Process Automation?
- Standard Robot Process Automation approaches work like macro recorders: user’s mouse actions and clicks are recorded and can be repeated as often as desired.
- With more complex processes that go beyond simple playback and decisions at the keyword level, these systems quickly reach their limits.
- Processes that require external data (e.g. if an employee has to obtain information about web search engines for the process) result in breaks
- Glanos Process Automation provides information on reliability (confidence), with which a cost-saving combination of light and dark processing can be realized.
- Glanos Process Automation can support or fully automate complex and specialized processes that require text analysis and/or external information.
At Glanos, we always rely on a combination of machine learning and rules. In order to have a precise control possibility and to guarantee the traceability of the results, rules are preferred where it makes sense.
How can Glanos Process Automation be used?
Glanos Process Automation can either be installed on-premise or used as a software-as-a-service. In a proof-of-concept phase, the requirements are precisely determined, an initial text analytics system is set up and evaluated, and a roadmap for a productive implementation is developed together.