Strategies for analyzing huge amounts of text with AI: the use of chunking and vector databases
Table of contents
In the age of digital transformation, large language models (LLMs) are opening up new ways of analyzing huge volumes of text stored in databases. These advanced AI systems deliver on the promise of gaining deep insights and competitive advantages from the wealth of data. However, they reach their limits due to the so-called token limitation, a technical restriction on the amount of data that can be processed. This becomes a particularly challenging hurdle when trying to analyze millions of text documents in depth or gain new insights:
Suppose a company wants to analyze 1,000,000 text documents. If special methods are not used, it faces major problems: Due to token limitation, an LLM can only analyze parts of the documents per run, which leads to a loss of information. Without a breakdown into manageable units and without a semantically intelligent database, there is a considerable loss of context. Documents have to be viewed in isolation, which makes it difficult to gain deeper insights. The analysis is also extremely time-consuming and resource-intensive, as each document has to be processed in full individually.
Chunking & vector data: easily analyze 1,000,000+ documents
An effective method of circumventing this limitation is the combination of smart chunking and the use of vector databases. By breaking down complex texts into smaller sections that can be handled by LLMs (chunking), it is possible to analyze large volumes of data without the restrictions imposed by token limits. In addition, vector databases make it much easier to access and analyze relevant information thanks to their ability to process and query semantic vector representations quickly and efficiently. This combination significantly increases the processing capacity and precision of LLMs and opens up the possibility of using the full power of the technology to gain valuable insights from the flood of data.
Download AI knowledge management one-pager
When analyzing large amounts of data, such as 1,000,000 text documents, the analysis process changes significantly:
- Efficient data processing:Splitting documents into smaller units (chunking) makes them easier for LLMs to process, as token limitations are bypassed.
- Advanced contextualization: Vector databases enable a deeper context analysis by quickly assigning semantically similar text parts. This significantly improves the understanding and classification of information.
- Time efficiency and scalability: The documents are broken down into smaller parts and information is retrieved efficiently using vector databases. This significantly speeds up processing, optimizes analysis and saves resources.
Real-world examples
Example for the legal department of a private equity fund
A private equity fund uses LLMs to check the compliance of its extensive and transnational contract database. The challenge lies in the enormous amount of data and the need to efficiently identify specific regulatory requirements in different countries.
- Chunking application: Before the analysis, all documents are divided into thematically relevant sections. This enables the LLM to apply its analysis skills specifically to relevant text segments and significantly improve the accuracy of the results.
- Vector database integration: Relevant sections and legal provisions are stored in the vector database. The LLM uses these to retrieve the most relevant legal texts and compliance requirements for specific legal issues.
Download AI knowledge management one-pager
The results are a much more efficient and in-depth analysis of compliance, minimizing regulatory risks and facilitating adaptation to international laws.
Example for the market research department of a large company
A market research department uses LLMs to derive trends and patterns from millions of consumer feedbacks, market reports and social media posts.
- Chunking application: Splitting the data into smaller, thematically focused segments allows LLM to work more precisely and in a controlled context, improving the accuracy of trend analysis.
- Vector database integration: By storing thematic vectors from the analyzed text chunks in the vector database, LLM can consistently and efficiently track relevant topics and trends across a comprehensive and diverse data set.
Download AI knowledge management one-pager
This strategy enables the company to react quickly to changing market conditions and develop customized marketing strategies based on in-depth, data-driven insights.
In both cases, chunking and vector databases prove to be indispensable tools for fully exploiting the strengths of LLMs. Through these techniques, companies can increase the power of AI in text analytics, allowing them to gain deeper insights and make more accurate decisions.
Efficiently manage information floods with AI
In the age of information overload, it is more important than ever for companies not only to manage their data, but also to use it intelligently. With its chunking technology developed in Germany and integration into vector databases, Tucan.ai offers a pioneering solution that emphasizes precision, efficiency, and data protection. Whether it's analyzing complex contracts, identifying market trends or making privacy-compliant decisions, Tucan.ai enables companies to revolutionize their data processing and make informed decisions based on verifiable and accurate data. Discover the transformative power of Tucan.ai and ensure your organization is at the forefront of data-driven decision making.
Manage your knowledge in a precise, scalable and GDPR-compliant way!
The AI Divide: How European Firms Can Harness Their Regulatory Strengths and Learn from US Innovation
Table of contents
European Companies' Headstart
Regulatory Alignment and Ethics
At the heart of European innovation is a rigorously defined regulatory framework highlighted by the General Data Protection Regulation (GDPR) and the anticipated EU AI Act. These regulations have initiated an era of heightened ethical awareness, where considerations around bias and fairness are not just afterthoughts but foundational components of AI strategies. This compliance-focused approach means European companies are not only generating AI solutions that respect individual rights but also pioneering models that could set global benchmarks.
As conversations around AI ethics become more prevalent, Europe’s early emphasis on these concerns places it in a thought leadership position capable of steering international policy making. A stringent regulatory environment also forces European businesses to be inventive within constraints, potentially driving more robust and universally acceptable AI solutions.
Governance and Board Involvement
The governance of AI in Europe is not left to IT departments alone; it has become a matter for boardrooms. With top-tier executives often at the helm of AI initiatives, tech adoption is closely wedded to the broader business strategy and operational objectives. This level of executive involvement ensures that AI investments are more than just experimental; they are strategic, mission-critical decisions tied to long-term visions.
By leveraging high-level oversight, European companies can better anticipate future challenges and strategically leverage AI to meet them. This approach significantly reduces the risk of misaligned tech ventures and ensures AI efforts are commensurate with the company’s trajectory and stakeholders' expectations.
Confidence in Data and AI Control
European firms exude confidence in their ability to manage and control AI systems, a direct outcome of adhering to strict data governance standards. This is a critical advantage in a world where data is not only ubiquitous but also a prime target for misuse. The assurance of data integrity and controlled AI application instills trust amongst consumers and stakeholders alike, positioning European companies as dependable and responsible technology providers.
As AI becomes more deeply integrated into every aspect of business operations, this inherent trust will become invaluable. It grants a level of user assurance that is often lacking in less-regulated markets and ensures European AI solutions meet the highest standards of data quality and security.
Learning Opportunities from US Companies
Speed of Adoption and Experimentation
American corporations have taken the lead in the practical implementation of AI, with a key focus on speed and agility. US businesses tend to experiment extensively, rapidly prototyping and iterating to discover valuable and innovative use cases. This willingness to adopt and adapt quickly enables American firms to stay ahead of the technological curve.
European companies could benefit by injecting this same level of dynamism into their AI strategies. While being mindful of ethical considerations and regulatory frameworks, there is room for more immediacy in the deployment and trial of emerging AI tech. Doing so could shorten the innovation cycle and help European businesses not only stay competitive but also become trendsetters in the global market.
Realizing Business Value
The practical impact of generative AI on a company's bottom line is unmistakable in the case of US firms, where a higher percentage already report tangible business benefits from their AI pursuits. This direct correlation between AI deployment and enterprise value is a blueprint for European companies, highlighting the importance of deploying AI not just for the sake of innovation but for clear-cut business enhancement.
To optimize their AI-related return on investment, European businesses should look closely at American strategies—namely, the integration of AI into core business processes from the outset. This ensures that AI initiatives are not just technologically advanced but also acutely business-oriented, ultimately contributing to overall enterprise growth.
External Partnerships for Skills
In contrast to Europe’s emphasis on cultivating in-house AI talent, American companies often turn to external partnerships to accelerate their AI proficiency. This collaborative approach can bring complementary skills to the table quickly, adding to the company's arsenal without the long lead times associated with internal training and recruitment.
European firms could embrace this external resourcefulness, broadening their AI capabilities rapidly without diluting their commitment to internal development. This hybrid model of sourcing AI skills can be the catalyst for accelerated innovation and more robust ecosystem partnerships.
Investment and Resource Allocation
Finally, the levels of investment that US companies funnel into AI far exceed those in Europe, showing a more aggressive stance towards capturing industry leadership in AI. European companies can take a leaf from the American book on strategic investment and resource allocation, putting more financial weight behind their AI ambitions.
By learning from the American approach of prioritizing AI investment, European businesses could more confidently venture into new market opportunities, investing not just capital but also a strategic vision into their AI endeavors. This intensification of resources would catalyze innovation and solidify a competitive stance on the world stage.
Charting a Balanced Future: The Convergence of European Ethics and American Agility in AI Leadership
While European businesses naturally lean towards a compliance-oriented, ethically grounded, and governance-focused AI strategy, adopting some of the bolder, more agile traits of the US approach could invigorate their AI practices. By marrying the European penchant for ethical AI with the American zest for rapid innovation and adoption, European companies could unlock unprecedented AI value and global influence. As the digital world continues to evolve, those firms that learn to effectively blend these contrasting paradigms of AI development will emerge as the true leaders in the artificial intelligence age.
Disclaimer: This blog article is grounded on insights and data from the "Generative AI Radar 2023 Europe" by Infosys. The referenced report provides a comprehensive analysis of the state of generative AI adoption among European companies, contrasting it with developments in North America. By interpreting and extending the findings of the Infosys study, this article explores the unique positions and opportunities European firms face within the burgeoning AI industry. For a thorough understanding of the original research, readers are encouraged to consult the complete Infosys Generative AI Radar 2023 Europe report.
Looking for an enterprise solution?
Whether custom data model training, custom workflow integrations, cloud or on-premise, or advanced security features, anything is possible with Tucan.ai. Tailor your package to your needs together with your personal Tucan.ai advisor.
Book a free consultation call!
Missing information from last weeks meeting? Ask Tucan!
Learn here how you can use Tucan.ai to automate your meetings and create a smart knowledge archive for conversational content:
Step 1: Connect your conferencing apps and other tools
Tucan.ai integrates with popular calendar and video conferencing apps such as Google Calendar, Zoom, Microsoft Teams, Google Meet and more. You can easily connect your accounts and invite our bot to join your meetings as a participant. Tucan.ai will automatically record the audio of your meetings and upload them to its secure cloud platform. Alternatively, you can upload any audio or video file to your account yourself. Tucan.ai will take care of the rest.
Step 2: Review, edit and share the content provided by Tucan.ai
Right after your meeting Tucan.ai generates a transcript and a summary using its own speech recognition algorithms. You can easily edit, annotate, highlight and share content with your team members, followers or clients. Furthermore, it is now even possible to ask questions about past meetings and get answers based on the gradually improving speech recognition and natural language understanding capabilities of our AI.
Step 3: Keep track of and manage your conversations with ease
Tucan.ai also provides you with data and insights from your meetings, such as talking times, sentiments, action items, keywords and topics. These metrics may be used to improve your communication skills, track your progress, identify gaps or opportunities and optimise your task management. You can also integrate Tucan.ai with other tools like CRM systems, project management and collaboration platforms to automate your entire workflow.
Step 4: Gain deeper qualitative insights through automated encoding
Tucan.ai also offers a smart feature which allows you to get your interviews and focus groups encoded automatically on predefined categories or themes. This function is particularly well-suited for market research, opinion polling and similar fields that rely on qualitative data analysis. You can use Tucan.ai to extract relevant insights more quickly and accurately from your conversations without spending hours on manual encoding. Learn more in our factsheet "AI-powered encoding with Tucan.ai":
Step 5: Use our new prompt feature for swift inquiries and analyses
We are constantly developing new functionalities to enhance Tucan.ai’s capabilities. One of our most exciting new releases allows you to prompt various kinds of data extractions and summaries from your conversations. For example, you can ask Tucan.ai – almost like a company-internal GPT – to provide you with the contents needed for a SWOT analysis from strategy meetings or customer persona from sales calls, and it will get back to you with customised outputs based on your prompts.
Outpace your competition - Book a free consultation call with our CEO Florian!
In case you wish to learn more about Tucan.ai's solutions for teams and enterprises, please schedule a short online call with our founder and CEO, Florian Polak.
CUSTOME VOICES
What they say about us
"We at Axel Springer have been using Tucan.ai for already over two years now, and we continue to be very satisfied with the performance of the software and the development process as a whole."
Lars
Axel Springer SE
"I have known the founding team for over a year. At Porsche, we are very satisfied with their work so far. I have recommended the use of Tucan.ai to my colleagues and business partners and I have been getting highly positive feedback back across the board - both on the service and the software."
Oliver
Porsche AG
"Tucan.ai has been a game-changer for our team. The software is incredibly intuitive and easy to use. It has saved us countless hours of work and has allowed us to focus on what really matters - our clients. I would highly recommend Tucan.ai to anyone looking for an AI-powered productivity tool."
Alex
Docu Tools
