What we do
We solve business problems related to natural human language and speech. This field of Artificial Intelligence is called Natural Language Processing (NLP). For example
- We've helped our clients to provide complex medical training and assessment by building chatbots that can communicate with people through speech and text
- We've built pipelines to extract complex information from documents in very specific domains such as invoicing, research grant applications, and government or policy documents.
- We've built tools to allow our clients to search through and compare their business documents in a smart way, using more than just keywords by incorporating context about the language that is used.
We use tools like spaCy, and transformers, which allow us to deploy state of the art models known for their speed and accuracy.
Strategic advice: one-off or ongoing
We can provide one-off or ongoing advice for any of your needs around Natural Language Processing.
This could look like:
- Helping you set your data strategy or sense-checking your existing strategy roadmap
- Providing expert advice about which NLP technology would work best for your specific project
- Advising on how you could improve the quality of your text data before a project starts or during an ongoing project
Ongoing consulting
We can integrate seamlessly into your ongoing project or embed during a project’s early stages to provide ongoing practical support and consulting for your Natural Language Processing projects.
This could look like:
- Helping you recruit the best NLP experts for your project
- Helping you create a data roadmap combined with regular check-ins to support its implementation
- Continuously reviewing the latest Natural Language Processing technology so you always stay ahead of the curve
Develop, build and deploy
We develop, build and deploy the most relevant Natural Language Processing and deep learning techniques to solve your problem, from battle-tested traditional algorithms, to state-of-the-art deep neural networks.
This could look like:
- Tagging documents to add missing metadata. You can use this, for example, to make informed decisions through intelligent analysis such as how much money to allocate to a project, or how to improve the grouping of items effectively for e-commerce.
