Technically our platform supports any language. By default, our models have been trained on Latin languages (English of course, French, German, etc.).

Absolutely - this is actually what the system has been designed for. We are able to understand the context of a table in a document (footnotes, units, currency, etc.) as well as recognise accurately aggregations or sub-totals for example.

Yes - we can recognise whether a checkbox is ticked or not and integrate the result in our processing.

We can produce JSON or XML as well as simpler CSV files.

The solution can either be hosted by us on a public cloud (currently AWS and Azure, with others current in the pipeline - there are no dependencies on any of the cloud providers’ infrastructure in the code) or deployed “on-prem” (client’s datacenter or hybrid cloud). This really depends on the client’s confidentiality and security constraints: documents that are publicly available can be processed on the cloud, while for documents that are confidential and cannot leave the client’s premises, we can deploy the solution “locally”.

We only store data during the processing and shortly after so you can access results directly, but we do not provide long term storage capabilities, and we usually purge all data every 12 or 24h for example.

(Currently at least) no: the set of programmatic supervision techniques that we use are not client-facing yet, however they allow us to update, adjust and re-train models extremely quickly and efficiently.

Yes - we capture every modification or adjustment made manually to the results and use this information to either improve our internal classifiers or re-train the model itself.