How to Choose an NLP Vendor for Your Organization
In this article, we look at some of the key questions you need to ask before choosing an NLP vendor for your organization.
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“the thinking in ai has changed from ‘what’s possible?’ to ‘how do i do this?’” explains rafiq ajani at mckinsey educational ai forum. natural language processing (nlp), an important subfield of ai that deals with how to program computers to process and analyze large amounts of natural language data, similarly is no longer a “nice-to-have” but a “must-have” technology.
companies using natural language processing are already seeing the business impact from improved customer experience to business growth. an organization that commits to nlp can enjoy the benefits of a shared understanding of data and goals, improved decision-making, fact-based analysis that avoids guesswork and allows for refined planning and forecasting at every level of the organization.
but as important as it is to use nlp for automatic business processes, so is the decision of choosing the right nlp vendor if you have decided to outsource the nlp development. generally, it is cheaper to outsource the development of ai than to build it in-house. however, there are certain things you need to keep in mind when choosing an nlp partner to ensure your business interests are not compromised.
in this article, we look at some of the key questions you need to ask before choosing an nlp vendor for your organization.
1. what is my business use case?
does your business want to use nlp to reduce costs by automating a process or gain insights from unstructured data? a good business objective motivates every new investment. having a clear understanding of your use-case will avoid frustration in the long run and set the kpis for the project at the outset.
for instance, you may decide that you need an nlp solution to automatically triage your customer support requests, thus freeing up the time for your agent to work on other complex tasks. for such projects, it is important to work with an nlp partner who can help you set the kpis for this project based on the amount and type of data available and also account for your unique business requirement. the partner should then build a solution that can achieve those kpis.
2. how accurate is the nlp solution?
accurate data analysis is the key to making informed business decisions, especially in the case of unstructured or open-ended texts. hence, before choosing an nlp vendor, it is important to know how accurate is the current solution to your data. a lot of off-the-shelf nlp solutions may not work on your data with very high accuracy. lower accuracy may directly affect your business objective. in addition to its immediate impact on the business objective, it can also create a disconnect between what is believed to be happening and the reality.
3. can the solution be customized according to my needs?
there are almost no plug-and-play solutions in nlp — nlp architectures need to adapt to your unique data and comply with your business regulations. extending the previous point, if the standard nlp solution does not perform well on your data with high accuracy, it is important to ask the vendor if they can customize their model to perform well on your data.
a good vendor should be able to fine-tune their model on your data without having to build it from scratch. they should be able to properly understand your needs and requirements and adapt to the challenges. this would ensure that you have the most appropriate solutions in every environment. they should also allow extensive customization as per your kpi demands, regardless of how complex they may be.
4. what amount of training data would be required?
it is essential to know the requirement of training data if your vendor proposes to customize their algorithm on your data. while more the data, better is the accuracy of the ai algorithm, building a large corpus of annotated data is a task in itself. it can quickly become very expensive and can stall your project. your vendor should be able to build a good model using a lesser amount of data. also, there should be a self-learning loop in the way ai is deployed in your organization such that it improves from the human input.
5. does the model improve on continuous usage?
no nlp solution will perform at 99% accuracy from the start. therefore, it is important to ask your vendor if the model improves from continuous usage. an ideal vendor should be able to deploy their solution in a manner that it learns from human feedback, if available (human-in-the-loop).
indata lab explains this setup very well in their blogs — “humans step in when algorithms are not up to the task. when the machine isn’t sure what the answer is, it relies on a human, then adds the human’s judgment to the model. this way the algorithm learns faster and the need for future human intervention is reduced.”
for improved accuracy and optimum results, the nlp solution must improve as more and more data is fed to it from human input.
6. is the nlp solution affordable at scale?
any nlp solution with usage-based pricing should be affordable at scale. the solution itself should be scalable so it can be rolled across your entire organization. your vendor should be able to provide a solution that can be adapted to and applied in every environment as per the requirement. it should be able to maintain uniformity and consistency throughout and bring out meaningful results without breaking the bank.
7. do they provide an on-premise solution?
nlp solutions need data to process which sometime may contain sensitive information, especially in financial services and healthcare industry. your vendor should be able to propose a solution to deploy their nlp solution on your private cloud or on-premise. for such cases, their solution should be compatible to perform optimally in your infrastructure without causing many headaches to your it team.
8. what are its integration capabilities?
you should choose a vendor that offers comprehensive integration capabilities, including end-to-end integration with your crm, customer support, and business intelligence applications. a well-integrated application improves the workflow of the end users and further enhances their productivity.
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