By: Lluís Ribes, Sales Engineer at Mediakind
In today’s digital age, search engines like Google have made accessing information effortless for everyday users. Similarly, natural language processing (NLP) is revolutionizing how businesses interact with data, providing an intuitive way to ask questions and receive clear, contextual answers. Taking this a step further, conversational analytics enhances NLP by enabling verbal interactions rather than just text-based queries. As Gartner describes it, this evolution allows users to engage with data in a more natural and efficient manner.
A brief history of NLP
NLP powers voice assistants used daily by nearly 25% of Americans and over 30% of users in India, Turkey, and China.
NLP traces back to 1954 when IBM and Georgetown ran early machine translation tests. Initially rule-based, the field shifted to statistical methods in the ’90s, then took a major leap with Tomáš Mikolov’s neural network models in the 2010s. Today, thanks to vast data sources and AI advancements, NLP powers voice assistants used daily by nearly 25% of Americans and over 30% of users in India, Turkey, and China, rising to 40% among 16-24-year-olds!
AI and NLP at MediaKind
At MediaKind, we have extensive experience developing AI-driven technologies that enhance media workflows. Our innovations include:
- Super-resolution upscaling: Using deep learning and neural networks, we’ve created an algorithm that converts HD content to UHD efficiently. Read more here.
- Cost-efficient encoding: Our CVQ algorithm, powered by AI, optimizes video compression for H.264 and HEVC, significantly reducing costs for our clients. Learn about our Constant Quality Strategies.
The benefits of conversational NLP in media workflows
Conversational NLP streamlines media workflows by enabling natural, voice-driven interactions for content discovery, metadata tagging, and audience engagement making it easier for both creators and consumers to interact with media using everyday language.
The next generation of engineers will expect NLP-based interactions by default.
NLP lets us deliver a more personalized, intuitive experiences end-to-end and offers:
- Shortened learning curve – Operators can interact with complex systems in a human-friendly way.
- Accelerated workflow creation – Users can generate workflows by simply describing their needs.
- Alignment with future workforce trends – The next generation of engineers will expect NLP-based interactions by default.
- Reduced operational workload – Automated reporting and system status updates without manual intervention.
NLP in action: Use cases with MediaKind’s MK.IO
MK.IO is MediaKind’s fully integrated streaming and monetization platform designed to simplify and accelerate video delivery for content owners, broadcasters, and service providers.
To showcase the power of NLP within MK.IO, let’s explore a few real-world use cases:
1. Creating a workflow from a feed
Users can verbally instruct MK.IO to create a workflow for ingesting, processing, and distributing a live feed. Instead of navigating multiple UI components, a simple command like “Create an HD contribution feed from Arena Stadium to Milano Teleport” can generate the desired setup instantly.
2. Generating custom reports
There’s no specialist role or knowledge required to launch a request on account-specific insights, such as “Show me the top-performing channels in the last 24 hours”, allowing for on-the-fly business intelligence without needing SQL queries, manual data extraction or a dedicated information dashboard.
3. Multilingual interaction
With MK.IO’s NLP capabilities, projects can be managed by offshore teams where each team member uses his or her most proficient language to interact with MK.IO. For example, a Japanese operator could ask “日本のストリーミングの状況を教えてほしい。”, and MK.IO would simply understand and execute the request at the same time a team in Poland ask “Daj mi kontrolowane zatrzymanie usług, które nie odbierają sygnału wejściowego.”
4. Calculating custom insights
Users can ask for data that doesn’t exist in predefined reports even when queries are vague or complex, such as: “Are there more live events based on ‘sport’ or ‘music’ content?” MK.IO will analyse the question and in real-time present the results instantly.
The future of NLP in media
The adoption of conversational AI in media workflows will become second nature.
As the number of NLP-based voice assistant users grows each year, the adoption of conversational AI in media workflows will become second nature. MediaKind is committed to evolving MK.IO to meet the expectations of the next generation of engineers and operators, making interactions with technology as seamless as possible.
The days of rigid interfaces and complex manual inputs are fading. With conversational analytics, businesses can interact with their data in a more intuitive, intelligent, and efficient way—unlocking new possibilities for innovation and automation.
Are you ready to embrace the future of NLP-driven media workflows? Let’s talk.
References
- [1] Large Language Models: A Deep Dive. Uday Kamath , Kevin Keenan , Garrett Somers , Sarah Sorenson – Springer
- [2] The history of machine translation in a nutshell. John Hutchins (2014) – https://aclanthology.org/www.mt-archive.info/10/Hutchins-2014.pdf
- [3] Statistical Language Models Based On Neural Networks. Tomáš Mikolov (2012)- https://www.fit.vut.cz/person/imikolov/public/rnnlm/thesis.pdf
- [4] Top 5 NLP Platforma & Comparison in 2025. Cem Dilmegani – https://research.aimultiple.com/natural-language-platforms/
- [5] What is NLP (natural language processing)?. Cole Stryker & Jim Holdsworth – https://www.ibm.com/think/topics/natural-language-processing
- [6] Voice Assistants: The Profit, Accessibility, and Speed Trifecta for Modern Businesses Tetiana Tsymbal (2025) – https://masterofcode.com/blog/voice-assistants-use-cases-examples-for-business
