If you face a challenging problem, and you suspect that the solution can be found in the textual data that you already have or can be acquired (e.g., through scraping websites or downloading PDFs), then a natural language processing (NLP) application is surely your choice. To implement it quickly and efficiently, you have to find a company that provides NLP services suitable for your goals. Plenty of NLP companies are out there in the market, so it’s a challenge to choose. Let’s figure out how to do it!
What is natural language processing?
Let’s start with the basics. Natural language processing (NLP) is a branch of artificial intelligence (AI) and machine learning (ML) that deals with the interaction between computers and human language. It focuses on the development of algorithms and techniques that enable machines to understand, analyze, and generate human language.
In simpler terms, NLP allows computers to interpret and make sense of the spoken and written language used by humans. This includes tasks such as translation, sentiment analysis, text classification, and question-answering.
NLP has a wide range of applications, including virtual assistants like Siri and Alexa, language translation tools like Google Translate, and spam filtering in email systems. It has become an increasingly important technology as more organizations seek to leverage the power of language to better serve their customers and stakeholders.
Compared to other AI implementation fields, finding areas in your business for a natural language processing application is a piece of cake. You just have to find the processes where text is being processed by people or by people and machines in collaboration. The complexity of the processes may significantly vary, for example, copying a phone number from an email signature vs. deciding if legal conflicts exist between two contracts. Thus, the main question becomes whether the process under consideration is worth automation using artificial intelligence. To answer, you have to discuss the four main questions from the picture below; after getting positive answers for the first one, you can move to the next one and so on. In the end, you should have a positive response for each (read more in the AI life cycle guide).
To sum up, like all AI-based solutions, NLP-based projects are often built to handle the most time-consuming, mundane, or routine tasks, such as:
- Answering repetitive questions
- Moderating comments
- Acting as customer support
- Monitoring for references to a company or its services/products on social media
- Scanning documents for keywords and filtering them (for example, CVs)
- Classifying emails (e.g., as spam)
- Spell checks
What is an NLP company?
An NLP company is a business that provides natural language processing (NLP) technology and services to other businesses and organizations. These companies typically have teams of NLP experts who develop and implement advanced algorithms and techniques to enable machines to understand, analyze, and generate human language.
Natural language processing companies offer a range of services, including language translation, sentiment analysis, speech recognition, text classification, and question-answering. They work with businesses and organizations in a variety of industries, including healthcare, finance, retail, and customer service, helping them leverage the power of language to improve their operations and customer experiences.
Such natural language processing firms may offer a range of technology solutions, including software-as-a-service (SaaS) platforms, APIs, and custom development services. They may also provide ongoing support and maintenance services to ensure that their clients’ NLP systems continue to operate smoothly over time.
Examples of well-known NLP companies include Google, Microsoft, IBM, Amazon, and OpenAI, as well as a range of smaller companies that specialize in specific NLP services or applications.
What to prepare before you contact an NLP company
The journey toward beneficial natural language processing implementation starts with a business problem. However, since machines speak their own language, business leaders naturally find it a challenge to describe their problem in a way that is clear enough for the AI.
To transform a business case into an AI use case, business leaders will need the help of an AI team, who determine whether and how to proceed with the problem enhancement; the problem owner who knows the most about the issue; and technical specialists who understand how the problem described can be addressed in the technical world.
Prepare the ground for machine learning implementation
Analyze the business problem. The first step is using gap analysis to define the pain point within the business process you want to enhance with natural language processing.
To transform the business case into an AI use case, answer the following questions:
- What is the value proposition of the use case?
Gather all the relevant information about the issue. You can think of this as the What+Why+Who+How:
- What are we trying to do?
- Why is it important?
- Who are the users?
- How can success be measured?
Company X wants to improve the auditing process. It is important because it will reduce the number of mistakes and speed up the process. The users are auditors inside the company. Success can be measured by comparing the number of checked cases and the percentage of mistakes
- What is AI’s role in solving this challenge?
This helps to reveal if the business case you are analyzing is specific and realistic. At this stage, it is important to understand what exact change will improve the process: e.g., “improve the auditing process” is not suitable, but “helping to flag suspicious cases for auditors” is more realistic.
- Who is the business owner?
The name and position of the responsible people will help other team members identify the right person to ask a question.
- Define the business processes gap
This step is important to understand the goal of the natural language processing implementation and to determine whether the improvement target is significant enough to consider changes in existing processes.
Answer the following questions:
- What does the process look like at the moment?
- What should the process look like in the future?
The more detailed description, the better: this will help to communicate the task to the AI team.
At company X, auditors check all the cases manually: it takes a lot of time, and the human factor causes mistakes.
After NLP implementation, if the AI is highly confident about a case, it will be processed by AI automatically; auditors will review cases with medium confidence; and low confidence cases, unlikely to warrant an auditor’s attention, are only reviewed at random. The process should be at least 25% faster, resulting in 10% fewer mistakes.
Get ready to communicate with natural language processing companies using the same terms
You might have already contacted your AI experts, but If you haven’t already contacted the AI experts, this might be the last opportunity. After finishing the gap analysis (there are six steps more), you’ve defined the project (or projects) most suitable for your business goals and worth implementing (no point in trying to solve a problem with AI if it can be solved more easily) after refinement in the machine learning canvas. The best version of the machine learning canvas results from collaboration with an outsourced machine learning partner specializing in natural language processing.
The machine learning canvas lets you lay out your vision for your machine learning system and communicate it with natural language processing companies.
This tool can be updated during the project and can help when the team needs to refresh their heads about the project; in addition, it is very useful for the new members joining the machine learning project.
Note that you can already use the answers you received from the gap analysis. If you encounter difficulties defining something or don’t know how to fill the machine learning canvas out, no worries: just talk to experts in artificial intelligence and machine learning.
What are the key things to think about when choosing an artificial intelligence partner?
Choosing the right partner can be a critical decision for businesses seeking to implement NLP technology. Here are some key factors to consider when selecting an NLP company:
- Expertise and experience: Look for a company with deep expertise in NLP and a proven track record of delivering successful projects. The company should have a team of experienced NLP professionals who understand the nuances of human language and can apply the latest techniques and algorithms to solve complex language problems.
- The services offered: Consider what specific NLP services the company offers and whether they align with your business needs. For example, if you need language translation services, look for a company with expertise in that area. However, this point could be tricky, because, when it comes to AI projects, business people and NLP experts may have differing perspectives on what constitutes similarities between aligning projects.
- Problem-oriented: do they focus on the problem, not the solution (if the focus is too much on the solution, it can mean that they are trying to fit their pre-existing solution to this problem that might not be optimal“)?
- Integration capabilities with existing systems: Consider how well the NLP company can integrate its technology with your existing systems and processes. The company should be able to work with your IT team to integrate NLP technology into your existing workflows seamlessly.
- Customer support and maintenance services: Look for a company that provides ongoing support and maintenance services to ensure that your NLP system continues to operate
smoothly over time.
- Good chemistry: even with AI, it’s about people working together
Best practices and tips for businesses looking for natural language (NLP) companies
Having delivered over 100 artificial intelligence projects including successful AI upgrades of customer service departments, MindTitan experts are convinced that NLP-enhanced data processing provides significant efficiency, productivity, and profitability growth. Here are some of the best practices:
- Trust your guts: good chemistry/trust correlates clearly with project success
- Check if the company focuses on actual development to get to results as soon as possible
- Don’t be afraid of custom solutions if an API does not fit
- Check if the company can deliver the AI development from start to finish
- Don’t follow the anti-patterns of AI implementation. For example, if a simpler approach will do the job, don’t implement deep learning just for vanity’s sake