Why Natural Language Processing

Natural language processing (NLP) sits at the intersection of linguistics, computer science, information engineering, and artificial intelligence.

Applying NLP improves interactions between computer and human languages allowing better decision-making based on the meaning of immense amounts of verbal or written communication. It can also transform unstructured language data into valuable structured data allowing for a wide variety of applications and analyses.

Typical problems

We encounter a variety of reasons why clients want natural language processing solutions.

Some common problems include

    • Understanding and acting upon customer sentiment
    • Coaching sales teams based on their customer conversations
    • Automating processes based on rules defined in documents
    • Using chatbots for support

How we help

Synaptiq’s expert data scientists and business strategists help identify opportunities for and deploy natural language processing solutions.

We partner closely with clients to determine if NLP is an optimal approach given defined business goals. Once we devise a strategy, we architect a solution and create models for testing and refining. This allows us to create models for understanding and acting upon intentions and meanings derived from documents, audio content, pictures, or videos.

Who benefits

A wide variety of industries and organizational departments can benefit from applying NLP solutions.

Sectors where we see some of the biggest impacts from deploying this AI approach include legal, healthcare, small and medium-sized financial services companies, and companies focused on supply chain and logistics.

Machine Learning & NLP for Vehicle Book Matching

Read about how we helped a Chief Technology Officer and Head of Product for the automotive industry reduce hardcoded rules in their app by automating the classification of matches between sources. The solution we partnered to build personalizes user experiences and recommendations by identifying patterns in data. We used machine learning and natural language processing…

Read more

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