When it comes to NLP implementation considerations, one of the key questions is whether to keep the project in-house or outsource it. While it’s possible to develop an NLP system internally, the decision should be based on the organization’s talent, resources, and overall business goals.

If an organization relies on no-code tools all the time or has never had AI or data science in its team, it may not have the necessary talent to implement an NLP system in-house. However, if they already have these functions within the organization, it’s possible to do some or most of the work. Nonetheless, the results may not be as good as buying a pre-built system off the shelf, which can provide the necessary support and eliminates the need to worry about talent recruitment for this specific task.

The implementation process itself involves collecting a large amount of text data and cleaning it up, which includes recognizing characters via Optical Character Recognition (OCR) and extracting data from scanned or PDF documents. Data labeling is crucial, as it helps identify the meaning of each piece of text and helps classify them accordingly. Once the data is labeled, it can be loaded into a machine learning and AI program to train a model, which will identify patterns of text and help classify content. The model is then tested, validated, and deployed, which can take some time.

If an organization decides to purchase a pre-built system, it will still have to source most of the data, but the system will do most of the other work, such as cleaning up and parsing the data. They may still have to label the data or have a labeling service do it for them, or they may use unsupervised models that don’t require labeling but still cluster the data in certain ways.

Timing and cost also differ depending on whether an organization chooses to implement the system in-house or purchase a pre-built system. The cost of developing an in-house system may be about equal or not quite as high as purchasing a pre-built system, but the ongoing support costs may be too high for the organization to undertake, especially if this is not a key part of their business.

Overall, implementing an NLP system requires a thorough understanding of the organization’s talent and resources, as well as its business objectives. Whether to implement the system in-house or purchase a pre-built system depends on a variety of factors, including cost, support, and the amount of time required for implementation.