Systems and methods for automated data procurement
The present disclosure provides a system for generating customized messages to a prospect. The system comprises: a data input module configured to receive prospect data, wherein the prospect data comprises data related to a prospect and data gathered from a plurality of sources; an input data processing module configured to select a subset of the data to create an input data; a first model trained to generate one or more sentences based on the input data; and a second model trained to determine whether the one or more sentences are unacceptable, wherein upon determining the one or more sentences are unacceptable, directing the input data processing module to select another subset of the data to create a new input data.
1 . A system for generating customized opener sentences, the system comprising:
a data input module configured to receive prospect data, wherein the prospect data comprises data related to a prospect and data obtained from a plurality of sources;
an input data processing module configured to select a first subset of the prospect data to create an input data;
a first model trained to generate one or more opener sentences based on the input data; and
a second model trained to determine whether the one or more opener sentences are unacceptable, wherein upon determining the one or more opener sentences are unacceptable, directing the input data processing module to select a second subset of the prospect data to create a new input data.
2 . The system of claim 1 , wherein the data related to the prospect is received via a graphical user interface (GUI) of the system.
3 . The system of claim 2 , wherein the plurality of sources is determined based at least in part on the data received via the GUI.
4 . The system of claim 1 , wherein another subset of the prospect data is selected until the one or more opener sentences are acceptable.
5 . The system of claim 1 , wherein the input data processing module comprises a text selection model trained to select the first subset of the prospect data and the second subset of the prospect data.
6 . The system of claim 5 , wherein the text selection model is trained by fine-tuning a pre-trained transformer model using a user feedback data.
7 . The system of claim 6 , wherein the user feedback data comprises a user input indicative a rejection of an opener sentence among the one or more opener sentences or an edit to an opener sentence among the one or more opener sentences.
8 . The system of claim 1 , wherein the first model or the second model is trained by fine-tuning a pre-trained transformer model using private data.
9 . The system of claim 8 , wherein the private data comprises user feedback data received in response to the one or more opener sentences and human curated sentences.
10 . The system of claim 1 , wherein the first subset of the prospect data and the second subset of the prospect data are from different sources from the plurality of sources.
11 . The system of claim 10 , wherein when the second subset of the prospect data is selected, a processing path corresponding to the source of the second subset of the prospect data is selected for generating one or more new opener sentences.
12 . The system of claim 11 , wherein the processing path corresponding to the source of the first subset of the prospect data and the processing path corresponding to the source of the second subset of the prospect data are different in at least a pre-determined rule for generating the one or more opener sentences.
13 . The system of claim 1 , further comprising a third model trained to process a response received from the prospect in response to a message comprising the one or more opener sentences and generate an analysis result.
14 . The system of claim 13 , wherein the analysis result comprises extracted meeting information or personality tag of the prospect.
15 . The system of claim 14 , wherein the analysis result is used to generate a response handling messaging comprising a meeting time, wherein the meeting time is determined based at least in part on availability information obtained from one or more sources referencing availability of a user and the personality tag of the prospect.