Enterprise NLP: To Build or Buy?
Leading businesses are racing to acquire and scale natural language processing (NLP) across their organisation - to reduce costs, enhance scalability and automate whole new processes.
Re:infer turns conversations into structured data for analysis and automation. We give organisations a centralised NLP capability to drive visibility, efficiency and operational scalability. Making the enterprise more agile and customer centric.
NLP is transforming the enterprise. But should you build or buy?
Building an enterprise NLP platform gives you greater control and customisation but at significant risk and cost. Purchasing an existing NLP platform enables faster deployment and rapid time to value, while offshoring the cost of development and MLOps.
Automation and transformation leaders are increasingly finding NLP to be a vital tool in making sense of their business’s operations, customers and communications. However, to create real value from the technology, NLP needs to be scaled across as many processes and functions as possible - and it has to be scaled fast.
Use the following factors to judge whether you should build or buy your own NLP platform:
Build
A custom-built platform is always unique and requires a great deal of manual work and expertise at each stage.
Buy
The market provides many tried and tested NLP platforms that can easily be plugged into your organisation.
In-house projects often fail because the wrong model choice was made, leading to insufficient accuracy or training that is too slow or expensive.
Most pre-built solutions offer the latest transformer-based models, constantly updated for accuracy and performance.
When performed in-house, MLOps has strenuous and expensive talent requirements that may be out of reach for most organisations.
In cloud-based, SaaS solutions MLOps is handled externally, with no internal talent requirements necessary.
Creating an intuitive training interface for SMEs requires additional development time and cost. Corners are often cut here, leading to slow and difficult model training that can last for months.
Pre-built solutions tend to be low or no code and have intuitive training interfaces that have been steadily improved over the years. Guided user journeys transport users from training to deployment in hours rather than months.
Self-builds tend to lack detailed user guides and materials due to time and cost constraints. Such assets also need to be constantly updated as alterations to the system are made.
Pre-built platforms are usually coupled with a regularly updated training resource and knowledge centre. SaaS-based solutions usually provide customer success teams to help users get the most from the platform.
Real-time alerts and monitoring are usually considered a low priority or gradual add-on for a self-built solution.
Real-time monitoring and alerts are usually available through configurable dashboards, email alerts, and reports tracking specific themes.
Self-builds provide complete visibility and control over company data, but resource and security expertise is needed to maintain and update these policies.
Established NLP platforms deliver strong end-user security, infrastructure security, data storage, encryption, and framework controls.
A series of point solutions are likely required to connect a self-built platform to the rest of the enterprise. The process tends to be gradual and uneven.
Most existing NLP platforms provide tested, pre-built connectors to the most popular business systems. Strong API support makes it easy to connect these platforms to custom systems within the business.
Introducing Re:infer NLP
The Re:infer Conversational Data Intelligence platform provides a centralised NLP capability for the enterprise. It gives customers all the tools they need to train powerful AI models quickly and easily, enabling them to mine, monitor and automate their business communications. And this is without any need for expensive data science and AI development expertise.
Re:infer is the most mature and sophisticated Conversational Data Intelligence solution on the market. It is underpinned by the latest transformer-based machine learning models, delivering the highest accuracy and data efficiency available. Our research team tracks emerging trends in machine learning and NLP, ensuring useful innovations are implemented across all models.
Model development and governance are fully managed by Re:infer, freeing our customers to focus on extracting value from their communications data.
Re:infer also provides a fully no-code, user-friendly platform. No technical experience is required to build machine learning or language models in Re:infer. Users receive a fully guided experience from discovery to deployment, with prompts and alerts to advise the next best training actions.
Learn more about Re:infer’s enterprise NLP platform.
Learn more about Enterprise NLP
