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One prevalent issue arises when the answer provided by the LLM lacks credibility or stems from outdated sources, ultimately hindering the end-user’s ability to discern the accuracy of the response.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produce a response to a user query, based on its internal knowledge, even if its reference sources are unreputable or out of date.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

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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.


How to use AI to discover the hidden meaning in complex documents

Welcome to our third blog of a series uncovering the key components of Artificial Intelligence to provide greater understanding for business leaders who may currently have FOMO (Fear Of Missing Out) from the blizzard of acronyms and hype.

Here, we look at Computer Vision, one of the main applications of AI where computers can be made to gain high-level of understanding from digital images or videos.

Critically, Computer Vision is concerned with automatic extraction of data, enabling documents that have handwriting and random layouts to become machine-readable.

Huge data volumes

Computer Vision needs a lot of data to be able to distinguish and recognize images.

In a way, it looks like a jigsaw puzzle where you assemble all the scattered tiles to make an image. Neural networks for CV work on the same principle.

Yet the computer does not have the final image, but it is fed hundreds, if not thousands of related images that train it to recognize specific objects.

To identify a cat, the computer would not be shown individual elements such as ears, whiskers, tail etc, but millions of pictures of cats so that it can model the features of our feline friends.

CV is used for visual surveillance, medical image processing for patient diagnosis and navigation by autonomous vehicles.

But in Altilia’s development of Intelligent Document Processing (IDP), CV has several key roles to play.

With Optical Character Recognition (OCR) and Intelligent Character Recognition (ICR), we are able to convert scanned documents into machine-readable PDFs and with Handwritten Text Recognition (HTR) are incorporate items such as signatures.

End goal

The end goal of an IDP solution is to extract meaningful information that are “hidden” in unstructured texts and documents, so we need to first break words down in a way that a machine can understand.

This is especially relevant when the documents that need to be processed are (low quality) scans such as contracts, forms, invoices or ID cards.

We then need to apply OCR to recognize both printed and handwritten text, using smaller units called tokens. To each token is added metadata, which is useful later in a search engine.

In IDP, it is useful to distinguish a photo from text and to tag elements such as signatures, stamps and markings, saving human labor time by automating checks such as whether a contract is signed and marked.

Finally, we focus on document layout analysis so that unsorted documents can be classified and then we can apply different machine learning algorithms and branch out different ML pipelines.

These core capabilities allow Altilia’s solution to work as a general purpose platform, rather than a point solution for specific document types and formats. We have also developed a patented solution for document layout analysis.

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


How Machine Learning works – and what it means for your organization

In our second blog of this series, where we unlock the lexicon of Artificial Intelligence for business leaders currently being overwhelmed by the hype of ChatGPT, we will focus on Machine Learning (ML).

What is Machine Learning?

People throw the terms machine learning and AI together and interchangeably, but they don’t mean the same thing. ML is a subset of AI that uses computers to learn or improve performance based on the data they use.

It’s a fascinating concept, straight out of science fiction: a computer uses algorithms to learn from the data provided. The more it develops, the more it learns: the more data it is fed, the better it gets.

It is where the concerns come that computers can become “more intelligent” than their human masters.

The reason ML has become more successful and prominent in the past decade, is the growth in volume, variety and quality of both public and privately-owned data, the availability of cheaper and more powerful data processing and storage capabilities.

Essentially ML models look for patterns in data and draw conclusions, which is then applied to new sets of data. They are not explicitly directed by people, as the machine learning capabilities develop from the data provided, particularly with large data sets. The more data used, the better the results will be.

So, where AI is the umbrella concept of enabling a machine to sense, reason or act like a human, ML is an AI application that allows computers to extract knowledge from data and learn from it autonomously.

How to train ML models

The key to machine learning (as much else in life) is training. ML computers need to be trained with new data and algorithms to obtain results.

Three training models are used in machine learning:

  • Supervised learning maps in a specific input to an output using labelled/structured training data. Simply, to train the algorithm to recognize pictures of cats, it feeds it labelled pictures of cats.
  • Unsupervised learning is based on unstructured (unlabelled) data, so that the end result is not known in advance. This is good for pattern matching and descriptive modelling. For example, Altilia uses Large Language Models (LLMs) as its foundation, which are trained on huge datasets using unsupervised learning.
  • Reinforcement learning can be described as “learn by doing”. An “agent” learns to perform a task by feedback loop trial and error until it performs within the desired range, receiving positive and negative reinforcement depending on its success. Altilia often uses Human-in-the-Loop (HITL) reinforced learning in its Altilia Review module.
  • Transfer learning enables data scientists to benefit from knowledge gained from a previous model for a similar task, in the same way that humans can transfer their knowledge on one topic to a similar one. It can shorten ML training time and rely on fewer data points. Altilia uses this technique to fine-tune pre-trained Large Language Models (LLMs) on a dataset provided by the client. We will focus on LLMs in a future blog.

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


The lexicon of Artificial Intelligence – and how it can transform your organization

The emergence of ChatGPT in recent months has prompted an unprecedented explosion of public hype and interest in Artificial Intelligence and its potential future uses.

Following industry luminaries such as Bill Gates describing AI as the most important technological advance in decades, there has been a torrent of predictions on how it will change the world of work.

Here at Altilia we have been building an AI-based platform, Altilia Intelligent Automation, that is at the forefront of a new revolution based around Intelligent Document Processing (IDP).

It is clear, however, that industry leaders anxious to understand this new phenomenon, are overwhelmed by the language, acronyms and blizzard of nascent technologies which leave them baffled and unsure on how to get started.

Our aim over a series of blogs is to unpack the lexicon of AI and explain what it all means, a useful, insightful and simplified guide for organizations interested in how AI will affect them.

What is Artificial Intelligence?

The term artificial intelligence (AI) is used loosely to refer to applications that perform complex tasks that previously required human intervention, such as communicating with customers online or playing chess.

The term is often used interchangeably with the terms machine learning (ML) and deep learning, although they (as we will discover) have different meanings.

Many companies are investing significantly in data science teams to take full advantage of AI, combining statistics, computer science and business knowledge to extract value from various data sources and enable problem-solving. Algorithms seek to create expert systems which make predictions or classifications based on input data.

Advanced functions can include the ability to see, understand and translate spoken and written language, analyze data, make recommendations and classify both structured and unstructured data.

Computers and machines are developed with the ability to reason, learn and act in a way that would normally require human intelligence, often at a scale that exceeds or speeds up what humans can deal with.

Benefits of AI:

  • Automation. AI can automate workflows and processes or work independently and autonomously from a human team.
  • Reduce human error. AI can eliminate manual errors in data processing, analytics, assembly in manufacturing, and other tasks through automation and algorithms that follow the same processes every single time.
  • Eliminate repetitive tasks. AI can be used to perform repetitive tasks, freeing human capital to work on higher impact problems.
  • Fast and accurate. AI can process more information more quickly than a human, finding patterns and discovering relationships in data that a human may miss.
  • Accelerated research and development. The ability to analyze vast amounts of data quickly can lead to accelerated breakthroughs in research and development.

Altilia are world experts in Intelligent Document Processing (IDP), so let’s have a look at how it builds on AI capabilities and developments to enhance organizational efficiency and accuracy.

What is Intelligent Document Processing?

At its most simplistic level, Intelligent Document Processing (IDP) converts unstructured and semi-structured data into structured usable information, thus enabling layers of automation to document-centric business processes.

As an example, many mortgage forms may be filled in with (unstructured) hand-written answers by an applicant, which would need human intervention to input that information into a financial services company’s systems.

IDP uses AI (and other technologies) to extract that information in a usable form, thus reducing time and manual labor.

Which AI fields are relevant for IDP?

A human reading and understanding a document needs to:

  • Have visual perception to recognize images, symbols and writing
  • Have a comprehensive understanding of the document’s language
  • Be able to understand new information and learn concepts
  • Be able to memorize concepts and the relations between them.

AI, therefore, needs to emulate these abilities to be capable of reading like a human – and for IDP, these are the most relevant application fields:

  • Computer Vision is the AI field that emulates human visual perception
  • Natural Language Processing (NLP) focuses on developing algorithms for general understanding of a language
  • Machine Learning (ML) is focused on training a machine to learn and conceptualise information as models
  • Knowledge Representation is focused on representing information in a way that is functional, not just to memorize the content, but relations between them.

We will continue this series explaining the lexicon of AI in the coming weeks. For more information on how Altilia can support your business, schedule a demo here .


How Intelligent Automation can impact the workplace

Intelligent Automation is being embraced by many organizations to boost revenues, increase efficiency, and improve customer experiences.

As an evolution of Robotic Process Automation (RPA), Intelligent Automation utilizes a Composite AI approach to leverage multiple techniques, such as machine learning (ML), computer vision (CV), and natural language processing (NLP) to handle complex processes and support business decision making.

Intelligent Automation has the potential to revolutionize the workplace, not only by freeing up time human workers dedicate to repetitive tasks but also helping companies to set new strategies and to develop new innovative products and services.

Unfortunately, this potential is still largely untapped, as most companies have yet to fully embrace it. Many opportunities are still left on the table.

Some of the best candidates for automation are business processes dealing with structured data (both digital and non-digital) with clearly defined stages; processes with seasonal spikes that cannot be easily met by a manual workforce (such as policy renewals, premium adjustments, claims payments); processes with strict Service Level Agreements (SLAs), requiring fast turnaround times (like transaction registration, order fulfilment, etc.).

Some notable use case applications also include:

  • Contract management: Intelligent automation can help to simplify all the steps needed to draft, send, redline, and execute contracts.
  • Sales processes: Here automation can provide first level assistance to prospects by providing direct answers to FAQs.
  • Quality and security compliance procedures: Intelligent automation can be leveraged to collect and pre-process data to perform initial assessments before involving a human analyst.
  • Finance: It can be used to streamline the procure-to-pay (P2P) process.
  • HR: Many tasks in the Hire-To-Retire (H2R) process can be automated.
  • Customer service: Many contact center tasks can be automated, as virtual agents can handle routine tasks, freeing up human agents to handle more complex customer inquiries, with great benefits for the customer experience.

Intelligent Automation has the potential to change the architecture of work by breaking it into smaller tasks and making it more events driven.

In the future, fewer tasks will need to be handled manually, leaving workers with more time to focus on digital learning and knowledge work, including learning how to proactively develop solutions using low-code platforms and tools.

The use of intelligent automation can boost the efficiency and effectiveness of business processes, enabling companies to meet higher standards of both customer and employee satisfaction.

However, as organizations increasingly adopt intelligent automation technologies, they need to be aware of the potential risks and hazards to avoid.

One of the main pitfalls is ignoring the importance of change management.

This could result in long-term issues if organizations overlook the importance of having their people aligned with their overall goals.

Additionally, employees may push back when automating processes that were previously carried out by a team of people. It is important to understand and take into account the concern and perceived lack of control when processes are suddenly automated.

Lastly, in order for any automation project to be successful it necessary to set clear goals in advance and to establish key metrics to evaluate the impact of the initiative and to measure the ROI.

Biggest challenges

Scaling intelligent automation is one of the biggest challenges for organizations: therefore, it is crucial for companies to be clear about the strategic intent behind their initiatives.

Success often depends on the ability of the organization to put “people’s needs” first: this means introducing new technologies in a way that is helpful and involves minimal disruption, and addressing real issues related to skills, roles and job content.

In other worlds, companies should not approach the problem like a race to be the first to introduce the latest technology and neither as a way to substitute people in managing operations.

Intelligent Automation can rather realize its full potential when it implemented as a means to support and augment human employees.

This allows a correct balance to be found between automation and manual control, leading to the best results when determining the success of a project.

The right approach

If organizations take the right approach, employees can also positively embrace automation and the opportunities it creates, rather than resist it.

In fact, automation can free up time on repetitive work, helping employees to focus on high-priority tasks and allowing them to move from administrative management to higher-value contributions.

Forward thinking organizations will try to shift workers into new roles as the current ones get progressively automated; this way employees can learn about new technologies and how they can be leverage them.

With this vision, upskilling programs, both internal and external, will be more and more a differentiating factor for companies to attract and retain the best talents.

For more information on how Altilia can help you gain the full benefits of Intelligent Automation, contact us here.


Taking on the challenge of managing complex Machine Learning Operations

As we explored in our previous blog, demand for AI-based solutions is growing year on year as organizations adopt and experiment with artificial intelligence to drive competitiveness and efficiency in a tough business environment.

We examined the key talent and skills gap challenge that companies, particularly small and medium enterprises, are facing as the explosion of AI projects outstrips the supply of qualified data experts required.

Now we want to go deep on another significant issue that has been identified by business leaders and consultants as a blocker to growth. And also show how Altilia can offset this challenge.

Managing Machine Learning (ML) Operations

When companies consider introducing new business process automation solutions, they often underestimate the complexity of managing the lifecycle of an AI project.

ML Ops are defined as a set of practices that combine the implementation of Machine Learning, DevOps, and Data Engineering models, with the goal of developing, releasing, monitoring, and the scaling into production high quality ML systems. This is done by leveraging organizational, cultural, and technological aspects that support the governance and automation of the ML model lifecycle.

Properly managing ML Ops is a major challenge even for large and structured companies.

In fact, according to a recent survey by the Artificial Intelligence Observatory of the Polytechnic University of Milan, out of a sample of 80 large companies operating in Italy that have already started AI projects, 71% say they have not yet introduced ML Ops as a structured practice.

Among the stated reasons for this delay stands out the lack of time they devote to AI initiatives compared to traditional business activities (43%) and the lack of internal expertise to manage ML Ops (33%), an issue which brings us back to the challenge discussed in our last blog.

Deloitte identified this ML Ops issue in their State of AI report: “Despite evidence that establishing clear processes and redefining roles to deliver quality AI results in improved outcomes, there has been little growth in the market in terms of adopting such practices, according to survey respondents.

In both the fourth and fifth editions, just one-third of respondents reported that their companies are always following MP Ops, redesigning workflows and documenting AI model life cycles.”

The ML Ops cycle in its complexity could be broken down in three distinctive parts:

  • The Business process analysis/understanding part refers to the mapping of internal processes, systems, and any other key element to understand the “problem” to be solved: such as defining requirements, objectives, key outcomes, and formulating use case hypotheses for ML applications.
  • The Data & Model Preparation part concerns the collection and refinement of data to train the ML model; the selection of the ML model with the best fit to solve the problem; the testing of different algorithms, features and hyperparameters; keeping track of all experiments, and maximizing code reusability.
  • Continuous Operations & Improvement refers to the continuous update of the solution with each product release; the continuous training of the algorithm, with the storage and processing of new data to update the model; the continuous monitoring of the model to keep track of changes in performance and accuracy.

How can Altilia help to overcome the ML Ops challenge?

Altilia’s approach is offering a comprehensive platform to manage the whole AI implementation lifecycle with an end-to-end approach.

The platform is built overtime and improved upon experience by Altilia, to hide the complexity of managing ML Ops, and to simplify the training and the optimization of AI models over time.

This eliminates the need to dedicate an internal team (with limited experience) just to maintain an AI implementation project, to monitor its performance over time and to manage its infrastructure.

Additionally, the automation challenges faced by businesses are rarely completely unique. Using a platform approach, Altilia can replicate pre-tested use case specific solutions and adapt them to perfectly fit the needs of the customer.

This greatly simplifies the process analysis, reducing the uncertainty when defining implementation goals, a leading to a greater confidence about the expected outcomes.

Altilia simplifies data and model preparation by giving access to library of generalized pre-built AI models, including Large Language Models (LLMs) that can be trained and adapted, according to the customer’s specific requirements.

The training of models is simplified, thanks to our document annotation interface, that allows users to easily generate examples for the system to process.

Lastly, Altilia facilitates the monitoring and fine-tuning of AI models to increase their accuracy over time and prevent data drift.

Contact Altilia here to learn how our unique AI technology platform can help your organization overcome these challenges.