Reduce support volumes by up to 40% with NLP-powered chatbots and free up your teams' time for complex issues
Natural Language Processing helps businesses understand, interpret, and act on human language at scale, unlocking faster decisions, better customer experiences, and measurable efficiency gains.
Common Use Cases
Virtual Assistants & Chatbots
Automate customer interactions with AI-powered chatbots that handle inquiries, provide support, and free up teams to focus on higher-value work.
Machine Translation
Break down language barriers with fast, accurate translation tools that make global communication seamless.
Sentiment Analysis
Understand how customers really feel by analyzing feedback, reviews, and conversations to guide smarter decisions.
Text Summarization
Turn long reports, articles, or transcripts into clear, concise summaries that save time and boost productivity.
Speech Recognition
Convert spoken words into accurate text, enabling transcription, voice commands, and hands-free workflows.
Text Classification
Sort and categorize large volumes of text automatically to streamline workflows and uncover key patterns.
Text Mining
Extract valuable insights from unstructured text data to discover trends, opportunities, and potential risks.
Benefits of NLP
Reports backed by McKinsey & Co and Bain
Helping Businesses Like Yours Succeed
NLP Implementation Process
Start Building Your Own Custom NLP Bot in Under 7 Days
Define Use Cases and Objectives
We begin by identifying the areas where NLP can deliver the most value—whether that’s automating customer queries, analyzing documents, or extracting insights from text. Clear objectives help ensure the NLP solution ties directly to business goals.
Collect and Prepare Text Data
Text and speech data form the core of NLP. We gather, clean, and structure your data to ensure it’s accurate, relevant, and ready for training. This foundation improves the reliability of downstream NLP models.
Design and Train NLP Models
Using advanced techniques, we build and train models tailored to your use case—such as chatbots, sentiment analysis engines, or document classifiers. We also equip the models with domain-specific language understanding for higher accuracy.
Test and Optimize Accuracy
Before rollout, we test the NLP solution on real-world scenarios to validate accuracy, performance, and consistency. Iterative fine-tuning ensures the system delivers dependable results across different contexts and data inputs.
Deploy, Monitor, and Enhance
Once implemented, the NLP system is integrated into your workflows. Continuous monitoring and updates allow the model to adapt to new language patterns, business needs, and data, keeping performance sharp and effective over time.
Key Technologies We Work With
Here is what our business-driven + user-centered UX process looks like
Resarch
For development
For Cloud and infra
Let's map out a journey of success
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Caroline Aumeran
Senior Project Manager at Airvon
Frequently Ask Question
What are the top three business problems that NLP solves?
NLP primarily helps businesses with:
a) Automating customer support (chatbots, ticket routing)
b) Analyzing customer feedback (sentiment and topic analysis)
c) Sorting and finding information inside large sets of documents.
How does NLP turn unstructured text into useful data?
NLP uses techniques like tokenization (breaking sentences into words), lemmatization (finding the root form of a word), and Part-of-Speech Tagging to identify the roles of words. It the text understandable and structured for a machine.
What is Named Entity Recognition (NER)?
NER is an NLP technique that scans text and pinpoints key pieces of information like names of people, company names, dates, locations, and monetary values. This is key for fast information retrieval and data extraction.
Can NLP help us understand the emotion behind customer reviews?
Yes, this is called sentiment analysis. NLP models read text to determine if the writer’s attitude is positive, negative, or neutral. It helps companies quickly gauge customer satisfaction at scale.
How long does it take to set up a production-ready NLP system?
A simple, rule-based chatbot can be set up quickly (a few weeks). A complex system that requires custom machine learning for advanced intent recognition or highly technical documents can take 3 to 6 months or more.
Can we use NLP models to process text in multiple languages?
Yes, many modern NLP models are multilingual, meaning they are trained on vast amounts of data from many languages. This allows a single model to handle sentiment analysis, classification, and translation tasks across various global markets.
Business Insights
Improving patient care is an initiative that all healthcare stakeholders
Improving patient care is an initiative that all healthcare stakeholders
Improving patient care is an initiative that all healthcare stakeholders
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