Introduction
The age of human-AI teaming is here. At Alkymi, we believe that AI should be used to drastically reduce the amount of time people spend on tasks that donāt fully utilize their skills and talents. Tasks like reading emails, parsing documents, copying tables, and validating data prevent people from doing activities that bring them satisfaction in their work and directly contribute to the companyās success. Thatās where Alkymi Data Inbox comes in.
With Data Inbox, emails (including attachments of varying formats) can be automatically read for key information like new leads, consultation requests, or data inquiries to be detected and flagged. Relevant details such as company names, industry, and deadlines captured- even if not explicitly stated. In just a few clicks, a human can validate the extracted information and send it on its way to help secure your next consultation, sale, or investor.
At the end of the day, your organizationās growth shouldnāt be stalled or limited by the speed at which you can process the constant flow of information. Instead, with Data Inbox, you can use that information to act as fast as you need to.
Data Inbox utilizes the concept of "Patterns", which allows the user to define fields that values should go in. This can be used in a number of ways, such as Excel exports, direct database input via the API, or in RPA pipelines. To understand Patterns, refer to the following:
- The definition of the target field names and their organization into a hierarchy is known as a Pattern and serves as a way to present information to the user in Data Inbox, in our API, and in any external integrations
- Patterns have a logical grouping of items you would like to extract called Schemas
- Some schemas can exist as Subschemas, should you wish to group data into a table and have all your information in one place
- Each item you would like to extract within a schema is known as a Schema Field. These Fields can be populated using the Data Inbox copy function
- It is possible to add Automation to a Pattern, which defines how to identify and obtain data from documents
- Tools automatically pull information from uploaded documents and arrange this data in a Schema Field
- A collection of Tools, known as Kits, orchestrate efforts to extract and filter data
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