Complete Guide to Artificial Intelligence in 2025: From Basics to Advanced Applications
Kraken Acquires Capitalise.ai As Crypto Companies Buy AI Startups
Crypto exchange Kraken has acquired Israel-based startup Capitalise.Ai, a no-code trading automation platform that turns natural-language commands into executable strategies, for an undisclosed amount. The technology will be integrated into Kraken Pro later this year, according to a blog post by Kraken on Aug. 20.
Founded in 2015, Capitalise.Ai built a platform that converts everyday text into strategies and supports execution across equities, crypto, foreign exchange markets, futures and options. Once integrated, Kraken Pro users will be able to design, backtest, and automate trades across digital and traditional markets without writing code.
"This acquisition gives Kraken Pro clients a new way to act on ideas in real time," said Shannon Kurtas, Kraken's head of exchange. She said the system aims to make advanced strategies more accessible to a broader range of users.
The move follows Kraken's $1.5 billion purchase of US futures platform NinjaTrader in March.
Major crypto exchanges, analytics companies and miners are increasingly acquiring AI companies, underscoring a trend of artificial intelligence becoming core to trading, compliance and infrastructure.
On Jan. 13, blockchain analytics firm Chainalysis acquired Alterya, an AI-powered fraud detection startup, in a deal worth about $150 million. Alterya's real-time monitoring system is designed to flag suspicious activity, strengthening compliance tools for banks and regulators.
Later that month, Web3 super-app xPortal bought Alphalink, a German startup specializing in AI-driven mobile interfaces for crypto. The acquisition brought Alphalink's team in-house to expand xPortal's AI tools for DeFi and digital identity.
Acquisition momentum has picked up in recent weeks. On Aug. 11, Tether and video platform Rumble announced a joint $1.17 billion bid to acquire Germany-based Northern Data, an AI and high-performance computing infrastructure provider. The deal would fold Northern Data's GPU cloud and data center units into Rumble, with Tether committing to multi-year GPU purchases.
The same day, Bitcoin miner MARA Holdings struck a $168 million deal to acquire a 64% stake in French AI firm Exaion, a subsidiary of state-owned utility EDF. Exaion's high-performance computing business partners with Nvidia and Deloitte, and the deal includes an option for MARA to raise its stake to 75% by 2027.
Not all companies are buying their way in. In July, Coinbase chose partnership over acquisition, teaming up with Perplexity AI to feed its COIN50 index data into the search engine — a step toward embedding crypto data into real-time AI responses.
Magazine: Your AI 'digital twin' can take meetings and comfort your loved ones
Definition Of Natural Language QueryPCMag
A query expressed by typing English, French or any other spoken language in a normal manner. For example, "how many sales reps sold more than a million dollars in any eastern state in January?" In order to allow that query to be spoken aloud, both a voice recognition system and natural language query software are required.
Not Quite the Same as a Search Engine QueryA natural language query is based on the known identification of each data element that has been previously defined in a database, which means that the result should be extremely accurate. In addition, such queries are made against proprietary data within an organization. In contrast, while a search engine query supports natural language and may provide similar results, they are not guaranteed to be 100% accurate. However, after years of scouring the trillions of pages on the Web, search engine algorithms have derived the world's largest knowledge base of public data. See search engine, GPT and ChatGPT.
Top 10 Natural Language Processing APIs To Use In 2025
Overview
GPT-5, Gemini Ultra, and Grok-3 lead as the most potent and versatile NLP APIs in 2025
Free tools like spaCy and OpenNLP remain popular for their speed, flexibility, and open-source nature
Most companies choose NLP APIs based on system compatibility, scalability, and task complexity
Natural Language Processing, or NLP, is the part of technology that helps machines understand human language. In 2025, it will be used in various places, including schools, offices, customer care, and even mobile apps.
Many companies now provide ready-to-use APIs that help developers add smart language features to websites and applications. Some tools are fast, some are more accurate, and others are simple to use. Below are the top 10 NLP APIs people are using the most this year.
OpenAI GPT- 4 API
GPT-4 is one of the most advanced language models right now. It can write answers, stories, emails, and even help with coding. It understands language well and can be used in many different areas, like education, business, and content creation.
Google Gemini Ultra API
Gemini Ultra is Google's newest language tool. It excels at solving complex tasks, such as lengthy questions and logic problems. It works smoothly with other Google services like Gmail and Docs. Many users say it is faster and more helpful than earlier versions.
xAI Grok-3 API
xAI created Grok-3. It is known for handling complex tasks, especially in maths and reasoning. It is also made to work with social media platforms. It provides smart and quick answers, making it ideal for apps that require fast replies.
Cohere API
AI Language Tools are enhancing everything from chatbots to content moderation systems. Cohere is made for businesses that want speed and control. It helps sort data, write responses, and search through text. It works well with systems like Oracle and Salesforce, which many large companies use.
Spark NLP
Best NLP APIs For Developers offer pre-trained models that simplify complex language processing tasks. Spark NLP is used by many hospitals, banks, and government offices. It can handle large amounts of text and supports many languages. It is built to work with big data tools like Apache Spark, which makes it suitable for heavy tasks.
Also Read: Courses for Learning Natural Language Processing
spaCy
spaCy is a free NLP tool. It is speedy and straightforward. It helps find names, verbs, and other parts of a sentence. Both students and developers use it. It also works with different deep learning tools, which makes it more flexible. Sentiment Analysis APIs help businesses monitor customer feedback in real time with high accuracy.
Apache OpenNLP
OpenNLP is another free option. It is written in Java and provides tools such as sentence detection, part-of-speech tagging, and more. It has been around for years and still gets regular updates. It is helpful for teams that want a stable and open-source system.
Amazon Comprehend
Amazon Comprehend helps find emotions, topics, and languages in texts. It is suitable for reading customer reviews and support chats. It fits well with other Amazon services and is used by many companies that already work with AWS.
Azure Text Analytics
Microsoft's Azure Text Analytics can look at documents, emails, and reviews. It finds main ideas, keywords, and even emotions. It supports many languages and is suitable for companies that use Microsoft tools.
IBM Watson NLU
Watson's NLP service looks at feelings, sentence roles, and the way ideas are connected. It is used in news apps, customer service, and business reports. It provides deep insights and aids in tasks that require more careful language study.
Also Read: Top Natural Language Processing Tools and Libraries for Data Scientists
Choosing the Right One
Each API has its own strengths. Some work well for creating content, while others are more suitable for reading and sorting large amounts of data. Large companies usually pick tools that match the systems they already use, such as those from Amazon or Microsoft. Smaller teams often choose options like spaCy or OpenNLP because they are free and easy to use.
NLP Integration Solutions ensure seamless deployment of language features within enterprise software. The field of Natural Language Processing is growing fast. These APIs are helping apps and websites become more advanced in 2025. They allow machines to read, write, and understand human language more accurately than before.
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