Unlocking the Power of Unstructured Data: How AI is Revolutionizing Business Intelligence

In the ever-evolving landscape of digital business, data has become the new currency. Yet, not all data is created equal. While structured data has long been the cornerstone of business analytics, an ocean of unstructured data remains largely hidden. This is where Artificial Intelligence (AI) is making a difference, offering new ways to extract value from this digital goldmine.

The Unstructured Data Difficulty

Imagine for a moment the volume of information that flows through a modern enterprise: countless emails, social media interactions, customer service calls, images, videos, and documents. This is the realm of unstructured data, and it’s growing at an astronomical rate. According to Gartner, a staggering 80% to 90% of data generated and collected by organizations is unstructured, with its volume expanding many times faster than structured data.

The challenge lies not just in the volume, but in the nature of this data. Unlike structured data that neatly fits into predefined database fields, unstructured data is a mix of formats and sources. It’s the difference between a meticulously organized filing cabinet and a room full of scattered papers, photos, and recordings. This lack of inherent organization has historically made unstructured data a huge challenge for traditional analytics tools.

Enter Artificial Intelligence

This is where AI emerges as a game-changer. Advanced machine learning algorithms and natural language processing capabilities are enabling businesses to sift through vast quantities of unstructured data, uncovering patterns, insights, and actionable information that were previously hidden from view.

Altilia is pioneering this field, offering solutions that transform how businesses handle unstructured data. Altilia’s platform represents a leap forward in our ability to extract meaningful information from unstructured documents. It’s not just about converting text to digital format; Altilia’s IDP solution can understand context, categorize information, and even make inferences based on the content they process.

Solving the Unstructured Data problem

Altilia’s platform combines various AI technologies to tackle complex document workflows. This innovative approach allows businesses to automate the ingestion and analysis of various document types, extracting relevant information and even flagging potential issues or inconsistencies.

What sets Altilia apart is its focus on accessibility and continuous improvement. Their no-code platform democratizes AI technology, making it accessible to users without technical backgrounds. Furthermore, their “human-in-the-loop” continuous learning ensures that the system keeps improving over time, adapting to new document types and evolving business needs.

The Power of IDP in Action

Imagine a banking institution processing loan applications. Traditionally, this would involve manual review of numerous documents, a time-consuming and subject to errors process. With Altilia’s IDP solution, the bank can automate this process, handling a wide range of document formats and integrating seamlessly with existing enterprise systems. This not only speeds up the process but also enhances accuracy and compliance.

But the applications go far beyond banking. In the legal sector, Altilia’s AI can filter through vast databases of case law, identifying relevant precedents. In customer service, it can analyze call transcripts and chat logs to identify common issues and measure customer sentiment. The possibilities are virtually endless.

The Business Impact

The impact of these capabilities on business operations and decision-making cannot be overstated. By leveraging Altilia’s IDP platform, businesses can:

  1. Enhance Efficiency: Automating complex document workflows dramatically reduces manual labor and accelerates decision-making processes.
  2. Improve Accuracy: AI-powered document processing minimizes human error, ensuring more reliable data extraction and analysis.
  3. Scale Operations: Altilia’s platform can handle large volumes of documents efficiently, allowing businesses to scale their operations without proportionally increasing costs.
  4. Drive Innovation: By uncovering hidden patterns and correlations in unstructured data, businesses can spark new ideas for products, services, or process improvements.

The Road Ahead

While the potential of AI in managing unstructured data is immense, implementation requires careful consideration. This is where Altilia’s ethical AI practices come into play, ensuring that businesses can harness the power of AI responsibly and sustainably.

As we stand on the brink of a new era in data analytics, one thing is clear: the ability to effectively harness unstructured data will be a key differentiator for businesses in the coming years. Altilia’s IDP platform is not just a tool for efficiency; it’s a gateway to a new world of business intelligence.


The organizations that can successfully navigate this new landscape – leveraging AI to turn the chaos of unstructured data into a wellspring of actionable insights – will be well-positioned to lead in their respective industries. As the volume and variety of unstructured data continue to grow, so too will the importance of advanced IDP solutions like Altilia’s.

The future of business intelligence is here, and it’s powered by AI. With Altilia’s innovative approach to IDP, businesses have a powerful ally in their quest to unlock the full potential of their unstructured data. By embracing these technologies, companies can not only keep pace with the data revolution but stay ahead of the curve, turning information into insight, and insight into action.


Altilia Recognized as Major Player in Unstructured Intelligent Document Processing Software by IDC

We are thrilled to announce our recognition as a Major Player in the IDC MarketScape for Unstructured Intelligent Document Processing (IDP) software 

This recognition highlights our software’s ability to streamline business operations by effectively managing complex unstructured document workflows, particularly in sectors like finance and public administration.

From the beginning, we have focused on enhancing organizational knowledge management with a solution that processes both structured and unstructured data, acting as a co-assistant to employees. Our Human-in-the-Loop (HITL) AI approach ensures continuous model training and transparency, providing highly accurate and reliable results.

With a dedicated team of over 50 professionals, including scientists, researchers, and software engineers, we are committed to democratizing the use of AI to help enterprises automate document-intensive business processes. This acknowledgment by IDC MarketScape reaffirms our position as a leader in the ever-evolving landscape of Intelligent Document Processing technology.

Our platform is designed for quick deployment and intuitive workflow design, making it suitable for organizations of all sizes and across various industry verticals looking to implement unstructured IDP. 

As we celebrate this recognition, we remain dedicated to shaping the future of document processing by bringing cutting-edge solutions to the forefront of the IDP market. Our goal is to offer organizations unparalleled efficiency, automation, and knowledge management capabilities.


What is Unstructured IDP ?

Unstructured Intelligent Document Processing (IDP) refers to a class of software technologies that leverage a combination of traditional and generative AI (GenAI), advanced analytics, and business rules to automate the classification, extraction, analysis, and validation of data from unstructured, semi-structured, and structured document formats. These technologies are designed to handle the high variability, inconsistent formats, and mixed elements (e.g., text, tables, charts) characteristic of unstructured documents, making the data within these documents actionable and integrated into business workflows.


About IDC MarketScape:

IDC MarketScape vendor assessment model is designed to provide an overview of the competitive fitness of ICT (information and communications technology) suppliers in a given market. The research methodology utilizes a rigorous scoring methodology based on both qualitative and quantitative criteria that results in a single graphical illustration of each vendor’s position within a given market. IDC MarketScape provides a clear framework in which the product and service offerings, capabilities and strategies, and current and future market success factors of IT and telecommunications vendors can be meaningfully compared. The framework also provides technology buyers with a 360-degree assessment of the strengths and weaknesses of current and prospective vendors.


AI implications on the workplace


Artificial Intelligence (AI) is no longer a futuristic concept; it’s a present reality reshaping the modern workplace. Generative AI (GenAI), in particular, holds the promise of augmenting human work by automating routine tasks and enabling employees to focus on more impactful activities. This transformation is supported by recent research from Deloitte, which highlights the profound impact AI can have on workforce dynamics.

The Role of GenAI in the Workplace

Generative AI (GenAI) refers to advanced algorithms capable of creating content, analyzing data, and automating repetitive tasks. Unlike traditional AI, which makes predictions based on data to perform specific tasks and rules-based decisions, GenAI can generate new content and insights by learning patterns from data, making it a more versatile tool in a wider range of industries and use case applications.

Current Applications

In recent years, artificial intelligence has evolved into a suite of powerful technologies that offer significant competitive advantages to businesses across various industries. As companies rapidly adopt AI to meet their business objectives and stay ahead of competitors, many are uncertain about the outcomes and the level of acceptance these tools will receive from their employees. While employers are enthusiastic about the opportunities AI presents, the potential impacts on employee experience and trust cannot be overlooked.

Benefits of GenAI for Employees

  • Increased Efficiency

One of the primary benefits of GenAI is its ability to streamline processes, reducing the time employees spend on repetitive tasks. This increased efficiency allows businesses to achieve more in less time, freeing up resources for strategic initiatives.

  • Focus on High-Impact Tasks

With GenAI handling routine tasks, employees can dedicate more time to high-impact activities such as strategic planning, creative problem-solving, and innovation. For instance, customer service representatives can focus on complex queries that require a human touch, while automated systems handle routine inquiries.

By automating routine activities such as data entry, report generation, and basic customer service inquiries, GenAI allows businesses to operate more efficiently. GenAI not only accelerates workflows but also minimizes human error, ensuring more consistent and reliable outcomes

Case Studies

HSBC has leveraged AI to identify potential money laundering activities by analyzing transactional patterns, customer behavior, and risk indicators. This AI-driven approach has enabled the bank to flag suspicious transactions more effectively, reducing the number of alerts that require investigation by over 60%

Bank of America has utilized AI to forecast the likelihood of companies defaulting by analyzing diverse data sources, including financial statements, credit histories, and market trends. This AI-powered model has improved the accuracy of lending decisions and enhanced the bank’s ability to manage credit risk effectively

Trade-offs in the Use of GenAI

A recent report of Deloitte points out that while the benefits of GenAI are significant, implementing these systems is not without challenges. Organizations must invest in the necessary infrastructure, train employees to work with AI systems, and manage the change process effectively.

1 – Creative Inspiration vs. Diligence

As human beings, we value original thinking to solve problems, to understand each other better, and ultimately to improve our society. Without new ideas, we would settle for the status quo and abandon a core part of our human identity: the pursuit of progress. 

AI as an ‘idea sparker’ could enable employees to create multiple versions of their work in parallel and uncover perspectives they may not have thought of themselves. In the Deloitte’s report, over 69% of executives said they believe AI will improve employee creativity to some extent, with AI sparking new ideas and inspiration that will improve the quality of work. However, there is also a fear that an over-reliance on AI will sacrifice accuracy and thoroughness, with 42% of conversations citing concerns about a decline in work quality. From this place of uncertainty, leaders have an opportunity to redefine creativity in the workplace while maintaining human rigor by setting boundaries for the use of AI.

At Altilia, we agree GenAI can be a catalyst to spark new ideas. However, we believe a potential decline in work quality can only be a concern if AI is seen as a replacement for human effort. In our approach, GenAI is meant to complement and assist human work, not to replace it.

Successful GenAI implementations should foster a “collaborative” approach, where AI generates intermediate inputs that are meant to be verified, expanded and enhanced by human experts.

When used correctly, Generative AI organizes data and information, enabling humans to make informed decisions more efficiently. This partnership enhances work quality and fosters an environment where creativity and diligence thrive.

2 – Efficiency vs. Inclusivity

Businesses are eager to leverage AI to expedite routine tasks and remove the administrative burden for their employees. While leaders are optimistic about efficiency gains, nearly a third of conversations cited in the report concern the bias and inclusion challenges of AI, suggesting that the risk of further embedding systemic bias tempers their excitement. Biases emerge wherever humans go. Unchecked, our narrow reference of personal experience will build unconscious bias into everything we create. Given AI’s pace of evolution and “black box” decisioning processes, leaders are concerned that AI will entrench existing biases with no opportunity to reroute:

To mitigate the risk of bias, Businesses should focus on empowering employees to effectively use AI and identify bias while also promoting open dialogue on how AI is used within their organizations.

Secondly, there is a need for transparency and trustworthiness in the AI output. This can be translated in the concept of “explainability”—the ability for end users to understand how and why an AI model generated a particular response.

Explainability enables the implementation of human oversight mechanisms to verify the correctness and impartiality of AI outputs. Therefore, ensuring explainability should be a primary driver in the design of GenAI-based applications.

In the context of Intelligent Document Processing, where Altilia operates, we achieve explainability by allowing users to easily trace back to the sources of information underpinning the AI model’s responses. This approach ensures that users can trust the outputs and maintain control over the decision-making process, ultimately enhancing both the reliability and the acceptance of GenAI solutions in the workplace.

3 – Personalization vs. Data Privacy

GenAI enables highly personalized experiences for customers and employees by analyzing vast amounts of data to tailor interactions and recommendations. However, this level of personalization often requires extensive data collection, raising concerns about data privacy and the potential misuse of sensitive information. 

At Altilia, we prioritize data privacy by ensuring our AI models operate within a closed and protected environment. This means that the data used is fully compliant with data processing regulations, as it is not accessed or shared through third-party APIs. Our approach guarantees that both input and output data remain within the company’s control, avoiding the risk of customer data being processed by external entities (for example 3rd API-based GenAI services like OpenAI.)

This closed environment enhances compliance with various data protection regulations and ensures that only company-generated data is used. By relying solely on internal data, our models deliver more accurate and consistent responses, further strengthening data security and regulatory adherence.

Future Outlook 

The workplace implications of AI are profound, offering both opportunities and challenges. GenAI has the potential to enhance productivity, allowing employees to concentrate on high-impact tasks. According to the Deloitte report, AI integration in the workplace will continue to grow, with more businesses adopting GenAI to stay competitive. This trend will likely lead to new job roles focused on managing and enhancing AI systems.

At Altilia, we believe that successful AI applications in the near future will not only focus on efficiency and process automation but also on enriching companies’ informational assets and stimulating creativity. We see GenAI as an assistant that increasingly supports knowledge workers by taking on more operational roles, such as information reprocessing and synthesis. Meanwhile, human knowledge workers will remain at the forefront of decision-making and strategic activities.

This collaborative relationship will help companies build greater trust and exercise more control over AI-enhanced processes, while also making it easier for the workforce to accept GenAI as an opportunity rather than a threat. By positioning AI as a supportive tool, we can foster an environment where AI drives innovation and efficiency, ultimately leading to more dynamic and successful business operations.

If you’re eager to find a solution for your business to streamline processes, reduce the time spent on tasks, and drive growth, we’re here to guide you through every step. Book a free consultation with an Altilia expert today, and let’s embark on this journey together, unlocking the potential of GenAI to bring your business into the future.


The Current State of Generative AI: Insights and Implications

Generative AI (GenAI) has rapidly evolved from a futuristic concept to a transformative technology with significant implications across various industries. From automating complex workflows to creating new content, GenAI’s capabilities are expanding, promising to enhance productivity and drive economic growth. This article explores the current state of GenAI, its economic potential, and the hype surrounding it, providing a comprehensive view of its future prospects and the implications for businesses.

The Rise of Generative AI

Generative AI refers to a class of artificial intelligence systems capable of creating new content, such as text, images, and even music, based on input data. Unlike traditional AI models designed for specific tasks, GenAI systems learn patterns from vast datasets to generate original outputs. This versatility makes GenAI a powerful tool in various applications, from content creation to complex problem-solving.

The State of AI: Current Capabilities and Limitations

The current state of AI is marked by rapid advancements and expanding capabilities, particularly in generative AI. In 2023, global private investment in AI, especially generative AI, reached unprecedented levels. The U.S. led with $67.2 billion in investments, demonstrating its dominant position in the AI landscape. This surge was driven by significant interest in GenAI, which alone attracted $25.2 billion, nearly nine times the investment of the previous year​ (Stanford HAI)​.

AI technologies, including GenAI, have shown remarkable abilities in natural language processing, image recognition, and automated decision-making. These advancements have led to significant improvements in industries ranging from healthcare to finance. For instance, generative AI has been successfully integrated into various applications, enhancing productivity and driving economic growth across sectors​.

However, there are still several challenges that need to be addressed. One major limitation is the explainability of AI systems. Many AI models, especially deep learning ones, operate as “black boxes,” making it difficult to understand how they arrive at specific decisions. This lack of transparency can be problematic in critical applications, such as lending decisions or criminal justice, where understanding the rationale behind an AI’s decision is crucial.

Another significant challenge is the potential for bias in AI systems. Since AI models learn from existing data, they can inadvertently perpetuate and even amplify biases present in the training data. Ensuring fairness and mitigating bias in AI outputs is an ongoing area of research and development.

Additionally, the computational demands of AI, particularly the need for powerful hardware like GPUs, can be a barrier for widespread adoption. According to AI Index estimates, the training costs of state-of-the-art AI models have reached unprecedented levels. OpenAI’s GPT-4 used an estimated $78 million worth of compute to train, while Google’s Gemini Ultra cost $191 million for compute. As the demand for AI capabilities grows, so does the pressure on hardware resources and amount of new data to train more advanced models, leading to higher costs and potential bottlenecks in development and deployment.

Navigating the Hype

Generative AI is currently at the peak of inflated expectations, attracting significant attention and investment. In 2023, investment in generative AI reached $25.2 billion, more than 9 times the amount invested in the previous year ($2.85 billion) and almost 30 times the amount invested in 2019 ($0.84 billion). This growth is driven by advancements in AI models, infrastructure, and AI-enabled applications across various sectors. (Stanford HAI)

This phase highlights the excitement surrounding the technology but also suggests that there may be a period of disillusionment before GenAI reaches its full potential. The concept of the Hype Cycle helps us understand this journey: initially, new technologies often experience a surge of enthusiasm and inflated expectations, followed by a trough of disillusionment as challenges and limitations become apparent. Eventually, as the technology matures and its true potential is realized, it enters a phase of enlightenment and stable productivity.

Gartner’s analysis suggests that GenAI will reach maturity in 5 to 10 years, indicating that we are now at the peak of expectations. This timeline underscores the importance of managing expectations and preparing for the inevitable challenges that come with integrating new technologies. Understanding the current capabilities and limitations of AI, particularly generative AI, is crucial for navigating this landscape effectively and leveraging its potential for long-term benefits​.

The Importance of Employee Education in AI Integration

As new GenAI applications continue to be released and new AI capabilities are implemented in existing applications, businesses will increasingly need to integrate Generative AI into their workflows, so it’s vital that employees are well trained to use these advanced technologies effectively. Educating employees about AI not only enhances their understanding and acceptance but also maximizes the benefits of AI integration. Proper training helps employees understand how to leverage AI tools to improve their productivity, make data-driven decisions, and automate routine tasks. This leads to a more efficient workflow, reduced operational costs, and a competitive edge in the market. Moreover, continuous education and upskilling initiatives foster a culture of innovation and adaptability, preparing the workforce to keep pace with rapid technological advancements. 

At Altilia, we prioritize user-friendly solutions and comprehensive training programs to empower our clients to fully harness the power of GenAI in their operations. Our goal is to create solutions that are user-friendly and accessible even to “line-of-business” users—those who are experts in their business domain but may not have specific technical training in IT and machine learning. This approach enables even less structured companies in the AI field, which might lack in-house expertise for developing and maintaining models, to fully exploit the potential of GenAI​​ without the need of investing a huge amount in new know-how or softwares.

Generative AI in the Finance and Banking Sector: Applications and Altilia’s Solutions

Generative AI holds significant promise for the finance and banking industry, offering numerous applications that can support the work of consultants and transform operations, enhance customer experiences, and ensure compliance. Here’s a look at the key applications of GenAI in this sector and how Altilia’s solutions align with these opportunities:

  1.  Automated customer service: GenAI-powered chatbots and virtual assistants can handle customer queries, manage accounts and provide financial advice, improving customer service efficiency and satisfaction 24/7. Altilia’s GenAI-powered virtual assistants deliver accurate, personalised responses to a wide range of customer queries, improving service levels while reducing operational costs. At the same time, Altilia’s solution supports employees in answering questions about documents and data.
  2. Due Diligence, Regulatory and Compliance:  Altilia’s solution , powered by GenAI, can support financial institutions in various regulatory and compliance activities. Here are some examples:
    1. Review of Financial Statements: Analyzing balance sheets, income statements, and cash flow statements to assess financial health.
    2. Know Your Customer (KYC) Procedures: Verifying the identity of clients and assessing the potential risks of illegal intentions.
    3. Regulatory Reporting: Preparing and submitting reports required by regulatory bodies such as the SEC, FINRA, or the FCA.
    4. Audit and Inspection Readiness: Maintaining readiness for audits and inspections by regulatory agencies.
    5. Assessment of Internal Controls: Evaluating the effectiveness of internal controls related to financial reporting and operational processes.
    6. Anti-Money Laundering (AML) Compliance: Ensuring adherence to AML regulations through transaction monitoring and reporting suspicious activities.
    7. Risk Management Processes: Examining the systems in place to identify, measure, monitor, and control various risks (credit, market, operational, etc.).
  1. Document Processing and Analysis: GenAI can automate the processing and analysis of various financial documents, such as loan applications, financial statements, and compliance reports, improving accuracy and speed. For example, banks handle thousands of loan and mortgage applications daily, a process that is time-consuming and error-prone due to manual data entry. Altilia’s platform automates document classification and data extraction, feeding this information into systems to support credit scoring. This reduces processing time by 80% and results in cuts of manual effort by over 80%.
  2. Personalized Financial Services: GenAI can analyze customer data to offer personalized financial advice and services, such as tailored investment recommendations, savings plans, and credit risk assessments. Altilia leverages GenAI to deliver customized financial services, analyzing individual customer data to provide personalized advice and solutions, thereby enhancing customer engagement and satisfaction.

By addressing these critical areas, Altilia’s GenAI-powered solutions help financial institutions streamline operations, enhance security, comply with regulations, and deliver personalized services, driving growth and innovation in the industry


Generative AI is at the forefront of technological innovation, with the potential to transform industries and drive economic growth. By understanding its capabilities, addressing challenges, and adopting ethical practices, businesses can harness the power of GenAI to stay competitive and innovative. At Altilia, we are committed to leveraging GenAI to enhance document processing and deliver value to our clients.

If you’re eager to find a solution for your business to streamline processes, reduce the time spent on tasks, and drive growth, we’re here to guide you through every step. Book a free consultation with an Altilia expert today, and let’s embark on this transformative journey together, unlocking the potential of GenAI to propel your business into the future


How generative AI is revolutionising business + 7 prompts to streamline your daily work

Imagine a tool that can create almost anything you ask it to: text, images, music and even programming code. Generative AI (GenAI) has emerged not just as a concept, but as a transformative force in the business landscape.


What is Generative AI and why is it so special?

Generative AI acts like a super-assistant that doesn’t just perform tasks but does so with a note of creativity. It’s an innovation that creates content, designs and solutions, effectively turning ideas into reality with little more than a prompt.

What sets Generative AI (GenAI) apart is its ability to be a super-assistant in support of human creativity. Unlike traditional AI, which might suggest the next logical data point in a series, Generative AI could imagine an entirely new series. This is because GenAI works by understanding and replicating the complex patterns that make up human-like creations, from artwork to prose. For example, if you ask it to write a poem in the style of Shakespeare, it doesn’t just find existing poems; it generates a new poem that feels like it could have been written by the Bard himself.

In business, this means that Generative AI doesn’t just optimise existing processes; It’s an invaluable tool for streamlining complex tasks and problem solving, unlocking the ability to explore countless scenarios and variations that a human alone could never conceive.


Why AI is a Game-Changer for Businesses?

Generative AI is transforming business operations by streamlining tasks and reducing costs. It simplifies knowledge management by using AI to quickly answer employee questions and retrieve information. It automates document creation by using templates to generate contracts and reports with less effort. AI also improves data entry by extracting information from documents and populating databases more accurately and quickly. These improvements enable organisations to do more with fewer resources and less time.


The Value Potential

The figures are staggering—generative AI could add between $2.6 and $4.4 trillion to the global economy annually. That’s more than the GDP of many countries, and as GenAI technology advances, its impact is set to increase (source: McKinsey 2023)


Which Areas Will Benefit?

GenAI will significantly advance areas like finance, procurement, HR, legal, and customer service. It will also boost data analysis, business intelligence, and office support. These areas, often heavy with documents, will benefit. The solutions offered by Altilia, for example, automate tedious tasks, thereby increasing efficiency. The impact will also be felt across sectors, from banking to technology to healthcare.


AI helps complete tasks without replacing jobs

GenAI doesn’t replace jobs. It transforms and enhances them. In fact, AI is really powerful when it performs specific tasks, not entire jobs. At Altilia, we believe that AI can enhance the skills of workers. This is especially true for knowledge workers. It also increases the quality of work and productivity. AI targets repetitive, low-value manual tasks. These tasks provide little value to the organisation. Altilia’s solutions aim to empower employees by streamlining repetitive document-based processing tasks. They free employees from tedious tasks. This leaves more time for creative and social activities.


Looking Ahead

The rapid changes brought by GenAI are part of the larger evolution of our world. Companies’ adept at leveraging this technology could gain a considerable edge. However, there are challenges to be addressed, like ensuring responsible AI use and preparing people for the changes it will bring.

Generative AI is opening a realm of possibilities for companies, making it easier to accomplish tasks that were once time-consuming or even impossible. With every technological advancement comes challenges, but the potential to enhance how we work and live is immense.


7 Prompts to Streamline Work with AI

In the ever-evolving landscape of professional environments, the role of Generative AI (GenAI) is becoming increasingly integral. From automating dull tasks to fostering creativity, GenAI is not just a futuristic concept but a present reality enhancing our working life. Here’s a closer look at the practical applications of GenAI that are transforming businesses today, exemplifying its versatility and power across various domains:

– Content Creation and Enhancement: GenAI can draft text in any desired style and length.

Example prompt:Generate a blog post draft on the impact of interest rate hikes on the real estate market, including key statistics and industry expert quotes.


– Query Resolution in Business Operations: Find precise answers to specific business questions.

Example prompt: “Explain the recent amendments to HR policies regarding hybrid work environments and their implications for employees.”


– Communication Tone Adjustment: Tailor the tone of workplace communications to fit the intended audience.

Example prompt: “Revise this performance review email feedback to make it more constructive and motivating, focusing on growth and development opportunities.


– Information Summarization: Distil complex documents into easy-to-digest formats.

Example prompt: “Summarise the annual financial report into a concise executive summary for the upcoming board meeting, highlighting the five most significant financial KPIs using bullet points.”


– Complex Information Simplification: Break down intricate documents for broader accessibility.

Example prompt: “Simplify the complex legal terms in our customer service agreements for non-specialist comprehension, and include a section with bullet points summarising the most common questions about our customer service.”


– Customer Feedback Analysis: Analyse customer interactions for service improvements.

Example prompt: “Analyse and classify the latest batch of customer service tickets into categories of complaint types, and identify the top three areas for service enhancement.


– Software Coding: Generate, translate, and verify code.

Example prompt: “I need a spending tracker app prototype. Can you generate a basic code that works on both iOS and Android using natural language descriptions?


Emerging Applications:

As Generative AI (GenAI) continues to evolve, it’s opening the door to a host of new and compelling applications. These emerging technologies harness the creative power of GenAI to address complex problems and organise unstructured data. A few of the most promising and impactful applications of GenAI for businesses are:

  • Intelligent Document Processing (IDP): This involves using AI to read and understand documents. Imagine you have a huge pile of Complex documents like contracts, financial statements, reports as well. IDP acts like a super-smart assistant that can quickly sift through all these documents, figure out what’s important, and even extract the specific information you need. For example, it can help a bank process loan applications faster by automatically extracting applicant details, saving hours of manual work.
  • Intelligent Process Automation (IPA): This takes the idea of robotic process automation (RPA) a step further. Initially, RPA was like teaching a robot to do repetitive tasks, such as entering data into a system. But IPA adds a layer of intelligence to these robots. Now they can not only perform tasks, but also make intelligent decisions based on the data they encounter. For example, an IPA system can automatically handle customer service requests by understanding the customer’s problem and either solving it directly or routing it to the right department.
  • Managing unstructured data: This is about organising and understanding data that doesn’t fit neatly into tables or databases – think of it as the messy, handwritten notes scattered across your desk. Most of the information we deal with every day, from emails and PDF documents to images and videos, is unstructured. Managing this type of data means using tools and technologies to sift through the clutter, identify the important information and organise it in a way that makes sense for future use. Consider a scenario where a lawyer needs to find specific evidence in thousands of pages of legal documents. Unstructured data management tools can help by quickly finding relevant information and organising it in an easily accessible way, saving hours of manual searching.


To Conclude:

In conclusion, Generative AI represents not just a leap forward in technological capabilities, but a transformative shift in how businesses operate, innovate, and compete. As we navigate this exciting landscape, the potential for GenAI to redefine roles, streamline processes, and unlock new realms of creativity is unparalleled. Whether it’s improving customer interactions, streamlining document management, or process automation, the implications for efficiency, productivity, and innovation are vast.

However, harnessing the full power of GenAI requires more than just technology; it demands expertise and vision to integrate these capabilities into the business strategy effectively.

If you’re eager to find a solution for your business to streamline processes, reduce the time spent on tasks, and drive growth, we’re here to guide you through every step. Book a free consultation with an Altilia expert today, and let’s embark on this transformative journey together, unlocking the potential of GenAI to propel your business into the future.

Data visualization

RAG: a transformative approach that overcomes the limitations of traditional LLM systems

In the evolving landscape of AI, the rapid strides made by Large Language Models (LLMs) bring forth the promise of enhanced efficiency and effectiveness. However, a critical query arises: can these models, despite their impressive advancements, truly excel in business settings without tailored training for business-domain specific tasks?

The quandary lies in the limitations faced by these generic models, particularly in highly specialized domains like finance or healthcare, where expert knowledge and nuanced understanding of organizational intricacies are paramount. Utilizing LLMs in these environments pose some challenges regarding their functionality, particularly in their generation of text responses to user queries. These models, while impressive, encounter certain undesired behaviours that pose significant issues.

One prevalent issue arises when the answer provided by the LLM lacks credibility or stems from outdated sources, ultimately hindering the end-user’s ability to discern the accuracy of the response. In fact, conventional LLMs are trained to swiftly produce a response to a user query, based on its internal knowledge, even if its reference sources are unreputable or out of date.

Another problem stems from their lack of transparency, which makes it more difficult for end users to verify the answers. This could lead a propagation of misinformation or unreliable content, potentially eroding trust, and credibility of the generative AI application. 

In the fast-evolving landscape of natural language processing (NLP), Retrieval-Augmented Generation (RAG) has emerged as a groundbreaking paradigm, redefining the capabilities of Large Language Models. Within many sectors where data-driven insights are critical for decision-making, RAG presents a transformative approach, surpassing the limitations of traditional Large Language Model (LLM) systems. 

RAG represents a hybrid architecture that marries the strengths of both retrieval and generative models. Departing from the conventional approach of relying solely on pre-trained patterns, RAG incorporates an explicit retrieval mechanism, enabling the model to access and leverage information from external knowledge repositories.

In a business setting, personalized responses to queries are often essential. RAG allows LLMs to pull personalized information from specific sources pertinent to individual queries. For instance, in HR-related questions, RAG can extract and synthesize information from an employee’s records, company policies, or other relevant documents to provide tailored and accurate responses. This way any company can easily index its own documents into RAG and get answers that come directly from its own indexed documents. In essence, the key to the RAG effectiveness lies precisely in the fact that it eases the use of LLMs for company-specific documents and data.

Key Components of RAG

Retrieval Component

The Retrieval Component in RAG is a fundamental aspect of the model’s architecture, responsible for accessing and incorporating information from external knowledge sources. This component distinguishes RAG from traditional LLMs by allowing the model to dynamically retrieve relevant data during the generation process. The Retrieval Component is designed to access diverse external knowledge sources, that can include databases, knowledge bases, text corpora, or any repository of information that is relevant to the task at hand.

Retrieval Component is initiated by an input query or context provided to the model. This query serves as the basis for retrieving information relevant to the specific task or user prompt. Advanced algorithms are employed to determine the most pertinent information related to the input query, thus enabling a more contextually aware retrieval process.

Generation Component

The Generation Component in RAG is responsible for synthesizing responses based on the information retrieved by the Retrieval Component and the model’s internal knowledge. This component utilizes advanced language generation techniques to produce coherent, contextually relevant, and task-specific outputs.

The Generation Component is capable of producing creative and diverse language outputs, going beyond mere regurgitation of retrieved information. This capability is particularly beneficial in generating nuanced and informative responses in various applications. In situations where the input query is ambiguous or requires clarification, the Generation Component can leverage its language generation capabilities to provide informative responses, seeking further clarification if needed.

Key Benefits of RAG

RAG offers several key benefits that make it a powerful and versatile tool in many industries. Here are some of the key advantages of using RAG:

Contextual Relevance

RAG excels at providing contextually relevant responses by integrating information retrieved from external sources. This contextual awareness is crucial in understanding and addressing specific queries, making RAG highly effective in tasks that require a deep understanding of context.

Enhanced Knowledge Integration

RAG seamlessly integrates external knowledge sources into the generation process. This feature is especially valuable in industries where regulations, policies, and market trends are dynamic. By incorporating up-to-date information, RAG enhances decision-making processes and ensures a more accurate and comprehensive understanding of complex scenarios.

Improved Accuracy in Responses

The retrieval mechanism in RAG enables the model to access precise and relevant information from external databases or knowledge bases. This results in more accurate and reliable responses compared to traditional LLMs that rely solely on pre-existing patterns learned during training.

Efficient Data Processing

RAG optimizes data processing by efficiently retrieving relevant information. This not only accelerates response times but also reduces the computational resources required for exhaustive searches within large datasets. In tasks where timely decision-making is critical, this efficiency is a significant advantage.

Tailored Responses to Specific Queries

RAG’s ability to retrieve and incorporate information specific to a given query allows it to generate responses that are highly tailored to the user’s requirements. This, for example, is particularly beneficial in insurance-related tasks such as policy inquiries, claims assessments and customer interactions, where precision is paramount.

Improved Decision Support

By combining information retrieval with language generation, RAG provides enhanced decision support. You can leverage the model’s capabilities to access relevant data, analyze complex scenarios and make more informed decisions. This contributes to the overall effectiveness of decision-making processes within an organization.

Facilitation of Complex Workflows

RAG’s integration of retrieval and generation components streamlines complex workflows by providing a more seamless transition between accessing external knowledge and generating responses. This facilitates smoother interactions in tasks such as document analysis, legal compliance and risk evaluation.

Competitive Edge

Organizations adopting RAG gain a competitive edge by harnessing advanced natural language processing capabilities. The model’s ability to deliver more contextually relevant and accurate information positions companies to make data-driven decisions with greater confidence, ultimately enhancing their market competitiveness.

Customer Engagement and Satisfaction

In customer-facing applications, such as virtual assistants or chatbots, RAG can provide more accurate and helpful responses to customer queries. This contributes to improved customer satisfaction and a more positive user experience.

How Altilia leverages RAG to enhance its IDP capabilities

Altilia has been pioneering the use of LLMs for IDP applications, to read and understand documents automatically, with the ultimate goal of automating processes that require manual information extraction from unstructured data and documents (discriminative AI), and to allow customers to “talk” with their internal document knowledge base, through an easy-to-use conversational UI that answers natural language questions (generative AI).

With the limitations of LLMs already highlighted in the article, the next step for Altilia is integrating RAG technology to extend its current IDP capabilities and provide a state-of-the-art IDP platform.
RAG allows us to obtain more accurate results and up-to-date answers, based on external knowledge sources rather than just on the LLM’s own internal knowledge (in our context an external knowledge source is the customer’s own document knowledge base).
This minimizes the “hallucination” problems that are typical of generative AI applications, based on LLM algorithms, leading to a substantial set of benefits to the client, as already highlighted in the article.

We are implementing RAG with a plug’n’play and accurate approach in Altilia Intelligent Automation (AIA), our next-generation AI assistant and automator, to improve results and extend the capabilities of Discriminative AI algorithms, retrieve pertinent passages where to find relevant data, let users to talk with documents by prompts and allow users to generate new documents on the base of extracted data and document contents. Novel RAG capabilities extend current Intelligent Document Processing (IDP) features of the AIA Platform and make Altilia a new challenger player in the Generative AI market.

For more information on how Altilia can support your business, schedule a demo here.

Data visualization

How can Intelligent Document Processing serve your organization?

Intelligent Document Processing (IDP) refers to the use of advanced technologies, such as artificial intelligence (AI) and machine learning (ML), to automate the extraction, understanding, and processing of information from various types of documents. IDP aims to streamline and enhance document-centric workflows by leveraging cognitive capabilities to interpret unstructured data found in documents such as invoices, contracts, and forms.

Through the application of natural language processing (NLP), advanced computer vision and other intelligent algorithms, IDP systems can recognize patterns, extract relevant data, and even comprehend the context of information within documents. This enables organizations to automate tedious and error-prone manual document processing tasks, improving efficiency, accuracy and overall productivity.

The more advanced Intelligent Document Processing (IDP) solutions, utilise a combination of AI technologies, including Computer Vision and Natural Language Processing (NLP).

In document capture, computer vision digitizes physical documents and employs Optical Character Recognition (OCR) to extract text from images or scans. Computer vision algorithms also recognize patterns and structures within documents, aiding in context understanding and data extraction from unstructured formats.

Natural Language Processing (NLP), on the other hand, breaks down texts for identifying key information and relationships. It enables IDP systems to comprehend natural language, ensuring context-aware interpretation. Additionally, NLP techniques facilitate document summarization, extracting key insights. When combined with rule-based processing, NLP enhances IDP systems’ adaptability to various document types and formats.

What makes Altilia different from other players in the IDP space?

Altilia Intelligent Automation (AIA) is an IDP platform that utilizes cutting-edge technologies to take IDP to the next level, including:

  • Large Language Models (LLMs) with RAG (Retreival Augmmented Generation) to enhance its natural language processing capabilities
  • Models fine-tuning adapt language model to suit specific use case tailored and customer tailored applications
  • Knowledge graphs to represent data and information alongside with the interrelations between them, making the models better understand the documents and simplifying the access to informations
  • Advanced Computer Vision technology, including OCR, HTR (Handwritten Text Recognition) and patented DAR (Document Analysis and Recognition), to better interpret the layout and the spatial information of the documents

What can your organization achieve with IDP?

  • Reduce the manual workload and associated costs
  • Speed up operational and decision-making processes (shorter lead time / time to answer)
  • Increase the capability of processes that require documents analysis and data retrieval (more operations managed in a given time-frame)
  • Higher accuracy of extracted data by eliminating human errors in the process
  • Improved accessibility to information contained in large documents datasets / knowledge bases utilized by the organization

Real world application examples of Altilia Intelligent Automation

Although with a focus in banking and finance industry, AIA is a general-purpose platform that can process any kind of documents for any industry. Here are some examples:

Banking and insurance sectors

  • Increase the ability and speed of reading and analyzing financial statements for profiling the bank’s corporate customers and selling derivative products
  • Analyze Non-Performing Loans (NPL) file documents to support the evaluation of NPL portfolios and to estimate the likelihood of credit recovery
  • Increase capacity and speed data extraction from documents related to claims and/or court documents for insurance companies.
  • Increase the ability to search for information found within pension regulations and documents related to products offered to clients; the ultimate goal is to provide an AI assistant (similar to ChatGPT) that can respond to personal pension advisors

Manufacturing and utilities sectors

  • Automatic reading of documents sent by suppliers to simplify supplier evaluation and selection for the procurement office
  • Reading and analyzing CVs with automatic matching of sought skills to expedite the HR department’s recruitment activities
  • Automatic reading and classification of action reports (activations, modifications and plant decommissioning) to simplify asset management operations for companies in the utilities sector
  • Automatic reading of bills, invoices and photos of meter self-readings to simplify the administrative management of energy and gas distribution companies
  • Training of conversational AI assistant (similar to ChatGPT) to provide real-time support regarding technical intervention procedures to support asset management operations for companies in the utilities sector

Thanks to its capabilities in 2022 Altilia has been recognized by Gartner a Representative Vendor in Market Guide on Iintelligent Document Processing. In 2023, IDC MarketScape hase recognized Altilia as a “Major Player” in its Worldwide IDP Vendor Assessment.

Why not schedule a demo with Altilia to learn more about how we can help transform your organization? Click here to register. 



Altilia is recognized as Major Player in the 2023-2024 IDC MarketScape Worldwide Intelligent Document Processing Vendor Assessment

Altilia, as a leading innovator in the field of Intelligent Document Processing (IDP), is proud to announce it has been recognized as a Major Player in the IDC MarketScape: Worldwide Intelligent Document Processing Software 2023–2024 Vendor Assessment (doc # US49988723, November 2023).

We believe this acknowledgment represents yet another milestone for Altilia, reaffirming its position as a leader in the ever-evolving landscape of Intelligent Document Processing technology.

With a dedicated team of over 50 highly experienced AI professionals, including scientists, researchers, and software engineers, Altilia aims to democratize the use of AI to help enterprises automate document-intensive business processes.

As we celebrate this recognition from the IDC MarketScape, Altilia will continue its efforts to shape the future of document processing, bringing cutting-edge solutions to the forefront of the IDP market, and offering organizations unparalleled efficiency, automation, and knowledge management capabilities.

About IDC MarketScape:
IDC MarketScape vendor assessment model is designed to provide an overview of the competitive fitness of ICT (information and communications technology) suppliers in a given market. The research methodology utilizes a rigorous scoring methodology based on both qualitative and quantitative criteria that results in a single graphical illustration of each vendor’s position within a given market. IDC MarketScape provides a clear framework in which the product and service offerings, capabilities and strategies, and current and future market success factors of IT and telecommunications vendors can be meaningfully compared. The framework also provides technology buyers with a 360-degree assessment of the strengths and weaknesses of current and prospective vendors.


How the technology behind Chat GPT can work for your organization

The explosion of interest and publicity in Artificial Intelligence in recent months has come from the advent of Large Language Models, specifically OpenAI’s ChatGPT, which set the record for the fastest-growing user base in January.

Suddenly it seems like everyone is fascinated by the coming surge of AI with new applications, creating excitement and fear for the future.

When Google’s so-called “Godfather of AI” Dr Geoffrey Hinton warned about “quite scary” dangers, it made headlines around the world.

Behind the hype

So, it is important to understand what is behind the hype and see how it works and what your organization can use to build future value.

This blog is split into two: first we learn about Natural Language Processing, the branch of computer science concerned with giving machines the ability to understand text and spoken words in much the same way humans can.

And then we will go deeper on Large Language Models (LLMs), which is what ChatGPT and others like Google’s Bard are using.

NLP combines computational linguistics with statistical, machine learning, and deep learning models to enable computers to process human language in the form of text or voice data and to ‘understand’ its full meaning, complete with the speaker or writer’s intent and sentiment.

NLP drives computer programs that translate text from one language to another, respond to spoken commands, and summarize large volumes of text rapidly—even in real time.

There’s a good chance you’ve interacted with NLP in the form of voice-operated GPS systems, digital assistants, speech-to-text dictation software, customer service chatbots, and other consumer conveniences.

But NLP also plays a growing role in enterprise solutions that help streamline business operations, increase employee productivity, and simplify mission-critical business processes.

There are two sub-fields of NLP:

  • Natural Language Understanding (NLU) uses syntactic and semantic analysis of text and speech to determine the meaning of a sentence, similarly to how humans do it naturally. Altilia uses Large Language Models for this.
  • Natural Language Generation (NLG) enables computers to write a human language text response based on data input. ChatGPT uses LLMs for NLG.

Large Language Models (LLMs)

LLMs are a relatively new approach where massive amounts of text are fed into the AI algorithm using unsupervised learning to create a “foundation” model, which can use transfer learning to continually learn new tasks.

The key is using huge volumes of data. The training data for ChatGPT comes from a diverse set of text sources, including billions of web pages from the internet, a huge number of books from different genres, articles from news websites, magazines and academic journals and social media platforms such as Twitter, Reddit and Facebook to learn about informal language and the nuances of social interactions.

The model is then able to predict the next word in a sentence and generate coherent text in a wide range of language tasks.

Altilia does exactly the same, but uses this capability to provide enterprise tools for specific business use cases.

Technology breakthrough

Overall, NLP is the core technology to understand the content of documents. LLMs are a breakthrough in the field as they allow a shift from where an NLP model had to be trained in silos for a specific task to one where LLMs can leverage accumulated knowledge with transfer learning.

In practice, this means we can apply a pre-trained LLM and fine-tune it with a relatively small dataset to allow the model to learn new customer-specific or use-case specific tasks.

We are then able to scale up more effectively, it can be applied more easily for different use cases, leading to a higher ROI.

For more information on how Altilia Intelligent Automation can support your organization to see radical improvements in accuracy and efficiency, schedule a demo here.


Leveraging GPT and Large Language Models to enhance Intelligent Document Processing

The rise of Artificial Intelligence has been the talk of the business world since the emergence of ChatGPT earlier this year.

Now executives around the world find themselves in need of understanding the importance and power of Large Language Models in delivering potentially ground-breaking use cases that can bring greater efficiency and accuracy to mundane tasks.

Natural Language Generation (NLG) enables computers to write a human language text response based on human generated prompts.

What few understand is that there is still a deep flaw in the ChatGPT technology: up to 20-30% of all results have inaccuracies, according to Gartner.

What Gartner have found is that ChatGPT is “susceptible to hallucinations and sometimes provides incorrect answers to prompts. It also reflects the deficiencies of its training corpus, which can lead to biased or inappropriate responses as well as algorithmic bias.”

To better understand this, it’s key to consider how LLMs work: hundreds of billions of pieces of training data are fed into the model, enabling it to learn patterns, associations, and linguistic structures. This massive amount of data allows the model to capture a wide range of language patterns and generate responses based on its learned knowledge.

However, as vast training data can be, the model can only generate responses as reliable as the information it has been exposed to. If it encounters a question or topic that falls outside the training data or knowledge cutoff, responses may be incomplete or inaccurate.

For this reason, and to better understand how best to use LLMs in enterprise environments, Gartner outlined a set of AI Design Patterns and ranked them by difficulty of each implementation.

We are delighted to share that Altilia Intelligent Automation already implements in its platform two of the most complex design patterns:

  • LLM with Document Retrieval or Search
    • This provides the potential to link LLMs with internal document databases, unlocking key insights from internal data with LLM capabilities
    • This provides much more accurate and relevant information, reducing the potential for inaccuracies due to the ability to the use of retrieval.
  • Fine-tuning LLM
    • The LLM foundation model is fine-tuned using transfer learning with an enterprise’s own documents or particular training dataset, which updates the underlying LLM parameters.
    • LLMs can then be customized to specific use cases, providing bespoke results and improved accuracy.

So, while the business and technology world has been getting excited by the emergence of ChatGPT and LLMs, Altilia has already been providing tools to enterprises to leverage these generative AI models to their full potential.

And by doing so, thanks to its model’s fine-tuning capabilities, we are able to overcome the main limitation of a system like OpenAI’s ChatGPT, which is the lack of accuracy of its answers.

For more information on how Altilia Intelligent Automation can help your organization, schedule a free demo here.