Natural Language Processing
Let your computers speak your language
Typical challenges
Thanks to our linguistics and computer science expertise, we’re able to overcome functional and technical challenges NLP challenges such as:
Model complexity
NLP models are complex and require technical expertise and computational resources to tune many layers and parameters for optimal performance.
Performance trade-offs
NLP systems balance accuracy and speed. This means that more complex models are usually more accurate but slower, while simpler models are faster but less accurate.
Ambiguity
Natural language is subjective and ambiguous, which makes it difficult for machines to accurately process language due to multiple meanings that can depend on the context and speaker.
Data quality and quantity
Machine learning models, including NLP, need large amounts of high-quality data, such as diverse, well-labeled, and clean data, to reduce bias and increase accuracy and reliability.
End-to-end AI Solutions for Manufacturing
Our experts are equipped to tackle the diverse challenges of the manufacturing sector with our industry expertise and AI solutions tailored to your needs.
Semantic search
Semantic search improves search accuracy by analyzing the relationships between words and concepts to deliver search results that better match the user's intent, rather than just matching keywords.
Examples
Process steering & optimisation
Predicitive maintenance
Root cause analysis
Anomaly detection
Text classification and clustering
Text classification and clustering automatically categorize text based on its content, such as news articles or customer feedback, into predefined categories like topics or sentiment for better analysis and organization.
Examples
In line quality inspection
Waste reduction
Defect removal
Pseudonymisation
Risk identification begins early in our sales process by identifying high-risk use cases under the AI Act that necessitate legal compliance measures.
Examples
Inventory management
Production planning & simulation
Inventory Management
Purchasing Admin automation
Sentiment analysis
Sentiment analysis involves automatically determining the sentiment of a piece of text, such as whether it is positive, negative, or neutral. This can be used to analyze customer feedback, social media posts, or product reviews.
Examples
Ai assisted lab testing
Microscopy
adversarial attack defence
Document processing
Document processing is the conversion of analog documents into digital format, which involves analyzing the layout, extracting information, and creating digital images for archiving or further use.
Examples
Dynamic pricing
Sales forecasting
Demand forecasting
Competitor & Market analysis
Text generation
Text generation uses large language models to create new and understandable natural language output, such as weather reports, patient reports, image captions, or chatbots.
Examples
Dynamic pricing
Sales forecasting
Demand forecasting
Competitor & Market analysis
High level outline of the solution
The first step in using NLP is to define the problem that needs to be solved. This involves identifying the data to be analyzed, the questions to be answered, or the business objectives to be met.
After defining the problem, data needs to be collected and prepared for analysis or model training. This involves tasks such as tokenization, stemming, and removing stopwords.
Choose the appropriate NLP technique for the specific problem, such as sentiment analysis, topic modelling, or entity recognition.
Train model(s) on data using machine learning algorithms like Naive Bayes, Support Vector Machines, or Transformers depending on the problem. Not all solutions require a custom trained model.
Test and evaluate the performance of the chosen NLP technique(s) by measuring metrics such as accuracy, precision, and recall to determine how well the approach is working.
Based on the client's requirements, the deployment of the NLP solution must be optimized for cost and inference time. Continuous monitoring is needed to detect performance changes that call for action.
Success Stories
We design AI solutions with transparency and fairness, aligning with EU AI Act guidelines and evolving regulations.
Increasing the sales hit rate to 81% at Randstad with data driven sales
We developed a scalable AI solution that allows their sales consultants to focus their time on contacting companies who have real potential for Randstad, and enabling them to build conversations based on accurate and relevant information
Transforming Bolt’s customer service with a Large Language Model
With the AI solution built by ML6, the customer service team now has a accurate indicator for the language and category of an incoming customer service ticket. This enables the team to focus on what’s really important, instead of sorting the tickets first, resulting in a time and efficiency gain.
AI co-worker shortens innovation lead time by 40%
Semi-autonomous product innovation for a global FMCG manufacturer, where product concepts are developed with the help of multiple digital AI colleagues. Combined with human experts, it shortens the innovation lead time by 35-40%.
Blog Posts and Insights
Read our case studies and blog posts to learn more about NLP.
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