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Lleed & Partners will not use your data for marketing purposes.</2>\":\"I hereby allow Lleed & Partners to process the data in this form.<br/><2>Lleed & Partners will not distribute your data to third parties. Lleed & Partners will not use your data for marketing purposes.</2>\",\"I need support\":\"I need support\",\"I prefer to be contacted by email\":\"I prefer to be contacted by email\",\"I prefer to be contacted by phone\":\"I prefer to be contacted by phone\",\"If you believe this is an error, <2>please contact us.</2>\":\"If you believe this is an error, <2>please contact us.</2>\",\"If you need help regarding a technical or administrative aspect of an existing Lleed & Partners contract, we will make sure that your request is triaged to the right hands as fast as possible.\":\"If you need help regarding a technical or administrative aspect of an existing Lleed & Partners contract, we will make sure that your request is triaged to the right hands as fast as possible.\",\"If you would like to discuss some aspect of your business, get more information about our operations or for press purposes. We will get back to you in the promptest fasion.\":\"If you would like to discuss some aspect of your business, get more information about our operations or for press purposes. We will get back to you in the promptest fasion.\",\"If you would like to discuss your needs with our business experts. Lleed & Partners strive to fulfill your needs with comprehensive and appropriate solutions.\":\"If you would like to discuss your needs with our business experts. Lleed & Partners strive to fulfill your needs with comprehensive and appropriate solutions.\",\"Industries\":\"Industries\",\"Information about the issue\":\"Information about the issue\",\"Issue\":\"Issue\",\"John Doe\":\"John Doe\",\"Join us\":\"Join us\",\"Learn more\":\"Learn more\",\"Legal information\":\"Legal information\",\"Let us know more about the issue you are encountering.\":\"Let us know more about the issue you are encountering.\",\"Let us know more about what you would like to discuss.\":\"Let us know more about what you would like to discuss.\",\"Let us know more about your needs, and any solutions that you envision and that we could discuss.\":\"Let us know more about your needs, and any solutions that you envision and that we could discuss.\",\"Limited liability company with share capital of CHF 20,000\":\"Limited liability company with share capital of CHF 20,000\",\"List of all pages available in your current language.\":\"List of all pages available in your current language.\",\"Lleed & Partners S.à.R.L.\":\"Lleed & Partners S.à.R.L.\",\"Lleed & Partners audits your digital usage for free. We then discuss with you which solutions, if any, would prove useful to improve your day-to-day operations.\":\"Lleed & Partners audits your digital usage for free. We then discuss with you which solutions, if any, would prove useful to improve your day-to-day operations.\",\"Lleed & Partners is expanding accross the world.<br/><br/>We are looking for talents with various profiles, who would like to direct their career path. Feeling ambitious? See our current job openings on LinkedIn.\":\"Lleed & Partners is expanding accross the world.<br/><br/>We are looking for talents with various profiles, who would like to direct their career path. Feeling ambitious? See our current job openings on LinkedIn.\",\"Online form\":\"Online form\",\"Open positions\":\"Open positions\",\"Opening hours\":\"Opening hours\",\"Other contact options\":\"Other contact options\",\"Phone\":\"Phone\",\"Please give us more information about the impacted project.\":\"Please give us more information about the impacted project.\",\"Please give us more information about you and your company, so that we can best fit your needs and get back to you.\":\"Please give us more information about you and your company, so that we can best fit your needs and get back to you.\",\"Please give us more information about you and your company, so that we can best understand your request.\":\"Please give us more information about you and your company, so that we can best understand your request.\",\"Please provide either a contract number or company name and contact information. If you provide us with a contract number, we will use the contact information on the contract or our most recent point of contact for this project.\":\"Please provide either a contract number or company name and contact information. If you provide us with a contract number, we will use the contact information on the contract or our most recent point of contact for this project.\",\"Please, give us more information about your contact here.\":\"Please, give us more information about your contact here.\",\"Please, let us know more about your business and needs, and how we can reach back to you.\":\"Please, let us know more about your business and needs, and how we can reach back to you.\",\"Please, let us know what are the issues we can help you with today.\":\"Please, let us know what are the issues we can help you with today.\",\"Please, let us know what subject you would like to discuss. Our teams will happily answer any question.\":\"Please, let us know what subject you would like to discuss. Our teams will happily answer any question.\",\"Published on {{date}}\":\"Published on {{date}}\",\"Quick access\":\"Quick access\",\"Raise ticket online\":\"Raise ticket online\",\"Read more\":\"Read more\",\"Registered in Geneva as CHE–279.374.453\":\"Registered in Geneva as CHE–279.374.453\",\"Related insights\":\"Related insights\",\"Related services\":\"Related services\",\"Rue de la cité 1, 1204 Geneva, Switzerland\":\"Rue de la cité 1, 1204 Geneva, Switzerland\",\"Services\":\"Services\",\"Sitemap\":\"Sitemap\",\"Social\":\"Social\",\"Start a discussion\":\"Start a discussion\",\"Submit this form\":\"Submit this form\",\"Support\":\"Support\",\"Support request\":\"Support request\",\"Talk to an advisor\":\"Talk to an advisor\",\"Tell us your issue...\":\"Tell us your issue...\",\"Tell us your needs...\":\"Tell us your needs...\",\"Thank you\":\"Thank you\",\"Thank your for reaching out to us today. We will make all our possible to get back to you in a timely manner.\":\"Thank your for reaching out to us today. We will make all our possible to get back to you in a timely manner.\",\"This page might have been deleted or may not be available in your region.\":\"This page might have been deleted or may not be available in your region.\",\"To make sure we connect you with the most relevant team at Lleed & Partners, please select your main reason for contacting us today.\":\"To make sure we connect you with the most relevant team at Lleed & Partners, please select your main reason for contacting us today.\",\"Updated on {{date}}\":\"Updated on {{date}}\",\"We are committed to open source\":\"We are committed to open source\",\"We believe that open-sourcing our researches will benefit a greater audience. From new business workflows to leading edge artificial intelligence technologies, Lleed & Partners is part of something bigger.\":\"We believe that open-sourcing our researches will benefit a greater audience. From new business workflows to leading edge artificial intelligence technologies, Lleed & Partners is part of something bigger.\",\"We could not find what you are looking for.\":\"We could not find what you are looking for.\",\"Whether it is for a business enquiry, a need for help or just to discuss some topics, we will make sure that one of our business experts responds as soon as possible.\":\"Whether it is for a business enquiry, a need for help or just to discuss some topics, we will make sure that one of our business experts responds as soon as possible.\",\"Would you like to audit your current digital efficiency?\":\"Would you like to audit your current digital efficiency?\",\"You are seconds away from getting help.\":\"You are seconds away from getting help.\",\"Your business needs\":\"Your business needs\",\"Your message\":\"Your message\",\"Your needs\":\"Your needs\",\"john.doe@acme.com\":\"john.doe@acme.com\",\"lleedpartners.contact-description\":\"Get in touch with our experts for business, support or general matters.\",\"lleedpartners.contact-description-business-enquiry\":\"Get in touch with our business experts to discuss your needs or schedule an audit.\",\"lleedpartners.contact-description-general-enquiry\":\"Get in touch with our team to discuss a subject or for press matters.\",\"lleedpartners.contact-description-support\":\"Get in touch with our operation teams for technical and administrative support.\",\"lleedpartners.contact-meta-thank-you\":\"Thanks for getting in touch with Lleed & Partners.\",\"lleedpartners.meta-description\":\"We are a digital consulting firm, creating state-of-the-art solutions in the domains of business automation, data science and machine learning. We provide software solutions that match your needs.\",\"Accept fair cookies\":\"Accept fair cookies\",\"All insights\":\"All insights\",\"Audit your digital presence\":\"Audit your digital presence\",\"Cancel\":\"Cancel\",\"Case Studies & Insights\":\"Case Studies & Insights\",\"Cookies\":\"Cookies\",\"Customize\":\"Customize\",\"Discover all our insights from newest to oldest\":\"Discover all our insights from newest to oldest\",\"Discover our latest case studies and insights on the business world\":\"Discover our latest case studies and insights on the business world\",\"Discover our latest insights into the digital world and selected projects that emphasize digitalization in businesses.\":\"Discover our latest insights into the digital world and selected projects that emphasize digitalization in businesses.\",\"Enable\":\"Enable\",\"External\":\"External\",\"I need help\":\"I need help\",\"Insights\":\"Insights\",\"Insights and case studies\":\"Insights and case studies\",\"Latest insight\":\"Latest insight\",\"Save choices\":\"Save choices\",\"We have worked with top companies worldwide to satisfy their most demanding needs.\":\"We have worked with top companies worldwide to satisfy their most demanding needs.\",\"We use Google Tag Manager to analyze popular pages and common user interactions on our website.\":\"We use Google Tag Manager to analyze popular pages and common user interactions on our website.\",\"We use anonymous cookies to improve your experience on our website. We do not use your data for advertising or marketing purposes.\":\"We use anonymous cookies to improve your experience on our website. We do not use your data for advertising or marketing purposes.\",\"Cookie consent\":\"Cookie consent\",\"Discover our latest insights\":\"Discover our latest insights\",\"Legal\":\"Legal\",\"Privacy policy\":\"Privacy policy\",\"Digital transformation isn't going paperless anymore, it has become our century's business challenge. Here's how you can overcome it.\":\"Digital transformation isn't going paperless anymore, it has become our century's business challenge. Here's how you can overcome it.\",\"Free digital audit\":\"Free digital audit\",\"Be ahead of your time.\":\"Be ahead of your time.\",\"Discover how we can help you take advantage of newer technologies\":\"Discover how we can help you take advantage of newer technologies\",\"Start your digital transformation now\":\"Start your digital transformation now\",\"We leverage state-of-the-art technologies to improve your business.\":\"We leverage state-of-the-art technologies to improve your business.\",\"We solve business challenges with technology\":\"We solve business challenges with technology\",\"Want to know more about your digital performance? Lleed & Partners offers a free digital audit program to help you know more.\":\"Want to know more about your digital performance? Lleed & Partners offers a free digital audit program to help you know more.\",\"Automating metals sales processes:\":\"Automating metals sales processes:\",\"Discover how we leveraged teams' expertise along with next generation technologies to automate metals sales processes, increasing efficiency and ensuring compliance.\":\"Discover how we leveraged teams' expertise along with next generation technologies to automate metals sales processes, increasing efficiency and ensuring compliance.\",\"Download the brochure\":\"Download the brochure\",\"Enter your work email\":\"Enter your work email\",\"Learn how you can automate your processes,\":\"Learn how you can automate your processes,\",\"Our services\":\"Our services\",\"The company is a multinational mining group, particularly involved in aluminum. As of today, the firm has more than 40,000 employees worldwide. We collaborate with their freight, aluminum and copper trading teams around the world to design and developed a tailor-made web solution in collaboration with the firm's trading and digital teams.\":\"The company is a multinational mining group, particularly involved in aluminum. As of today, the firm has more than 40,000 employees worldwide. We collaborate with their freight, aluminum and copper trading teams around the world to design and developed a tailor-made web solution in collaboration with the firm's trading and digital teams.\",\"We believe that open-sourcing our researches will benefit a greater audience. From new business workflows to leading edge artificial intelligence technologies, Lleed & Partners makes its tools available to build a better future.\":\"We believe that open-sourcing our researches will benefit a greater audience. From new business workflows to leading edge artificial intelligence technologies, Lleed & Partners makes its tools available to build a better future.\",\"leveraging your own expertise.\":\"leveraging your own expertise.\",\"reducing friction and increasing efficiency.\":\"reducing friction and increasing efficiency.\",\"Automating metals sales processes\":\"Automating metals sales processes\",\"Download\":\"Download\",\"Downloading your file...\":\"Downloading your file...\",\"Your download should start automatically. If it doesn't, please click the button below.\":\"Your download should start automatically. If it doesn't, please click the button below.\",\"Lleed & Partners experts are here to help you in the digitalisation process, from strategy to implementation. Let's discuss your current needs, and how we can help you achieve your goals.\":\"Lleed & Partners experts are here to help you in the digitalisation process, from strategy to implementation. Let's discuss your current needs, and how we can help you achieve your goals.\",\"Elevate your FFA intraday data\":\"Elevate your FFA intraday data\",\"FFA Intraday Data\":\"FFA Intraday Data\",\"FFA Intraday Data:\":\"FFA Intraday Data:\",\"Get a free sample of our FFA intraday data as CSV and see how it can help you make better trading decisions. The download also includes a short brochure with more information about the data.\":\"Get a free sample of our FFA intraday data as CSV and see how it can help you make better trading decisions. The download also includes a short brochure with more information about the data.\",\"Leverage over-the-counter FFA market data to improve your trading strategies.\":\"Leverage over-the-counter FFA market data to improve your trading strategies.\",\"parsing unstructured data for live insights\":\"parsing unstructured data for live insights\"}","language":"en"}}]},"post":{"body":"var _excluded = [\"components\"];\n\nfunction _extends() { _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); }\n\nfunction _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; }\n\nfunction _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; }\n\n/* @jsxRuntime classic */\n\n/* @jsx mdx */\nvar _frontmatter = {\n  \"title\": \"Software development & DevOps\",\n  \"subtitle\": \"Placing agile tools at the heart of your projects\",\n  \"description\": \"Leverage the newest technological tools and agile methods to make your projects successful. 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Thanks to agile and devops methods, we guarantee continuous feature development and integration.","icon":"Application32","slug":"/services/software/web-app"}}}]},"relatedInsights":{"search":{"took":6,"timed_out":false,"_shards":{"total":9,"successful":9,"skipped":0,"failed":0},"hits":{"total":{"value":3,"relation":"eq"},"max_score":30.003471,"hits":[{"_index":"lleedpartners-mdx","_type":"_doc","_id":"79431640-36fe-54cc-86e7-2f0077328dcd","_score":30.003471,"_ignored":["body.keyword"],"_source":{"fields":{"langKey":"en"},"frontmatter":{"title":"Structuring data using artificial intelligence","subtitle":null,"slug":"/insights/structuring-data-using-ai","category":"insight","headerImage":null,"localHeaderImage":{"childImageSharp":{"gatsbyImageData":{"layout":"constrained","backgroundColor":"#182898","images":{"fallback":{"src":"/static/46f5be4a2e4475a51fff3c08e5373c46/4f402/5.jpg","srcSet":"/static/46f5be4a2e4475a51fff3c08e5373c46/4e705/5.jpg 864w,\n/static/46f5be4a2e4475a51fff3c08e5373c46/b28e0/5.jpg 1728w,\n/static/46f5be4a2e4475a51fff3c08e5373c46/4f402/5.jpg 3456w","sizes":"(min-width: 3456px) 3456px, 100vw"},"sources":[{"srcSet":"/static/46f5be4a2e4475a51fff3c08e5373c46/e639c/5.webp 864w,\n/static/46f5be4a2e4475a51fff3c08e5373c46/2ce30/5.webp 1728w,\n/static/46f5be4a2e4475a51fff3c08e5373c46/c865f/5.webp 3456w","type":"image/webp","sizes":"(min-width: 3456px) 3456px, 100vw"}]},"width":3456,"height":4343}}},"excerpt":"Unstructured data such as chats or email contains valuable information that can be used to improve business processes. We explain how to structure this data using latest AI technologies.","description":null,"icon":null,"published":null},"body":"Introduction Unstructured data is common in the business world. It can be found in emails, chats, documents, and many other sources. It can also be found in news outlets, or other textual sources. This data is often not used to its full potential, as it is difficult to extract information from it. Some companies may resort to human labor to extract information from this data, but this is both time-consuming and prone to errors. In this article, we will explain how you can leverage AI capabilities to structure this data and extract valuable information from it. Converting unstructured data to a readily usable format is a complex task, however it can be broken down into three main steps: Preparing for structuring : understanding the data and defining the structure Structuring : using AI to structure the data Working towards production : understanding if the solution is viable, and if so, how to take it one step further Preparing for structuring For the rest of this article, we will assume that we are working with a simple trading conversation, where two parties discuss closed deals. The goal is to extract the information from the conversation and store it in a structured format. Data collection The first step is to collect data. This can usually be done by compiling a list of emails, or exporting a chat history. In our case, we will use a chat history, which contains messages that look like these: Hi John, We confirm that we will buy 1000 shares of Apple at 100$ per share. Have a good day, Jane Or that could also be Dear John, The transaction is confirmed for 1000 shares of AAPL@100$. Best regards Make assumptions The next step is to make assumptions about the data. This is a crucial step, as it will determine the structure of the data. In our case, we know the following: The data is in a chat format The data is about trading The data is about closed deals (NB: this is an assumption, and may not be true, the chat could also be about a deal that is still in progress or chit-chat) We also make some assumptions about the information contained when talking about closed deals: The number of shares, company and price must always be present. There should usually be a currency symbol (e.g. $, €, £, ¥, etc.) or information. There may be some info about the buyer, but it is not always the case or might be inferred from the sender of the message. Define the structure Based on the assumptions we made, we can define the structure of the data. In our case, we will define the following structure in YAML format: trade : buyer : Name of the buyer (optional) stock : Name of the bought stock currency : Three letter code of the currency price : Price of the stock (per share) quantity : Number of shares, is an integer Here is the same model in a Pydantic format, where we have added some validation rules. This will be useful later on. To install Pydantic, run pip install pydantic in your terminal, or other package manager. from typing import Optional from pydantic import BaseModel , Field , field_validator class Trade ( BaseModel ): buyer : Optional [ str ] = Field ( description =\" Name of the buyer (optional) \") stock : str = Field ( description =\" Name of the bought stock \") currency : str = Field ( description =\" Three letter code of the currency \") price : float = Field ( description =\" Price of the stock (per share) \") quantity : int = Field ( description =\" Number of shares, is an integer \") @ field_validator (\" currency \") def currency_length ( cls , v ): if len ( v ) != 3 : raise ValueError (\" Currency must be a three letter code \") return v This structure is kept simple for the sake of the example, but it could be extended to include more information, such as the date of the transaction, the seller, etc. Structuring We will now try to use AI to structure the data. There are many ways to do this, but we will focus on an LLM-based approach, as it is the most flexible and can be used for many different tasks. It is also the most readily available, and cheapest to implement for small datasets. Setting-up OpenAI API To use LLMs, we will use OpenAI's API . OpenAI is a company that specializes in AI research, and has developed a very powerful LLM. They have made it available through an API, which we will use in this example. To get started, head over to https://platform.openai.com/ and register a new account. Then, create a new API key, and copy it somewhere safe. We will use it later. Important : you will also need to install the OpenAI Python package. You can do this by running pip install openai in your terminal, or other package manager. Using Langchain Large Language Models work similarly to typeahead, where they try to predict the next word based on the previous words. This can be used to generate text, but given that the models have also been trained on structured data such as JSON or XML, they can also be used to generate - somewhat - structured data. The very nature of LLMs prevent them from reliably generating structured data, as they may miss some information or generate plainly incorrect syntax. However, there exists some tooling, such as Langchain that aims to solve this problem, by pointing the LLM in the right direction. One major feature of Langchain is that it also leverages LLMs ability to correct incorrect syntax. This essentially means that if the first attempt at generating structured data fails, it will ask the LLM to correct itself, yielding very good results. To install Langchain, run pip install langchain in your terminal, or other package manager. For our example, a basic setup using OpenAI's API and Langchain would look like this, added to the code we wrote earlier: from langchain . output_parsers import PydanticOutputParser # Import the Pydantic output parser from langchain . llms import OpenAI # Import the OpenAI LLM from langchain . prompts import PromptTemplate # Import the prompt template import os # Import the os module to set the environment API key # ... messages = [ \" Hi John, \\n We confirm that we will buy 1000 shares of Apple at 100$ per share. \\n Have a good day, \\n Jane \", \" Dear John, \\n The transaction is confirmed for 1000 shares of AAPL@100$. \\n Best regards \", ] # Define the messages os . environ [' OPENAI_API_KEY '] = ' xxxxxxx ' # Set the API key, replacing xxxxxxx with your API key. model_name = \" text-davinci-003 \" # Set the model name temperature = 0.0 # Set the temperature. A higher temperature will yield more diverse results, but is not recommended for structured data. It can be seen as \"freedom\" given to the model to generate whatever it wants. model = OpenAI ( model_name = model_name , temperature = temperature ) parser = PydanticOutputParser ( pydantic_object = Trade ) # Create the output parser prompt = PromptTemplate ( template =\" If this message is about a closed deal, please fillin the information in the given format. \\n {format_instructions} \\n {message} \\n\", input_variables =[\" message \"], partial_variables ={\" format_instructions \": parser . get_format_instructions ()}, ) # Create a prompt template _input = prompt . format_prompt ( message = messages [ 0 ]) # Feed the first message to the model output = model ( _input . to_string ()) # Generate the output structured_data = parser . parse ( output ) # Parse the output print ( structured_data . model_dump_json ()) # Print the structured data Running the complete file should yield the following output: { \" buyer \": \" John \", \" stock \": \" Apple \", \" currency \": \" USD \", \" price \": 100 , \" quantity \": 1000 } Trying it on the second message is as simple as: _input = prompt . format_prompt ( message = messages [ 0 ]) _input = prompt . format_prompt ( message = messages [ 1 ]) This also yields the same input, which is correct. Congrats, you have successfully structured your first messages! You can get the full code on Replit: NB: trying the above Replit will not work, as you need to provide your own API key. Few-shot learning to the rescue As you see above, the model was able to correctly parse the first message, with no previous understanding of the data nor training. In just a few minutes, we were able to structure a message that would have taken a human a few minutes to parse, filling in the information in a structured format. However, things are not always that easy. The model picked up John as the buyer, which we - given the context - can infer as false. The buyer is most likely Jane, as she is the one sending the message. This is a common problem with LLMs, as they are not able to understand the context of the message in a chat conversation. For most of parsing errors, we can take advantage of the few-shot learning technique. To put it simply, few-shot learning is a technique that allows us to train a model on a small dataset, and then use it to perform a task, by giving it a few additional indications or examples to run the task. The idea is that the LLM will be able to generalize the few examples that we give it to perform the task. In our case, we can use few-shot learning to tell the model that the buyer is most likely the sender of the message. This can be done by adding a few examples to the prompt template, adding context, or being more verbose in the field descriptors. Here, we will chose to add context to the prompt template, as well as an example. prompt = PromptTemplate ( template =\" If this message is about a closed deal, please fillin the information in the given format. \\n {format_instructions} \\n {message} \\n\", template =\"\"\" If this message is about a closed deal, please fillin the information in the given format. Note that we are passing you chats, where the buyer is most likely the person who wrote the chat, and not the recipient. If the sender did not tell his name in the message, we can't tell who the buyer is. {format_instructions} {message} \"\"\", input_variables =[\" message \"], partial_variables ={\" format_instructions \": parser . get_format_instructions ()}, ) We now have the following output for message 1: { \" buyer \": \" Jane \", \" stock \": \" Apple \", \" currency \": \" USD \", \" price \": 100 , \" quantity \": 1000 } And the following output for message 2: { \" buyer \": null , \" stock \": \" AAPL \", \" currency \": \" USD \", \" price \": 100 , \" quantity \": 1000 } These outputs are correct, as the buyer is not specified in the second message. Working towards production While the above solution gets good results, it is not production-ready. It is yet to be verified that the model is able to generalize to other messages, and that it is able to parse messages that are not in the training set. There are also other elements that should be taken into account, for example the fact that the stock name was provided in various ways (AAPL, Apple, etc.). This can be solved by adding more examples to the training set, or by adding other correctors down the line. Monitoring The key to a successful production-ready solution is monitoring. It is important to monitor the model's performance, and to understand when it is failing. This can be done by logging the model's output, and then manually checking the output to see if it is correct. If it is not, then the model should be retrained on the new data. This process needs to be kept up-to-date, as input data may evolve over time. Enterprise-ready AI providers Finally, while the above solution is a good starting point, its use on third party AI providers might not be the best fit for your company. Although there exists specific AI providers for enterprise, such as Azure or AWS, you may also want to take the extra step to deploy your own AI solution. Lleed & Partners can help you deploy your own AI solution, and integrate it with your existing infrastructure. We can also help you build a custom solution, tailored to your needs. .grvsc-container { overflow: auto; position: relative; -webkit-overflow-scrolling: touch; padding-top: 1rem; padding-top: var(--grvsc-padding-top, var(--grvsc-padding-v, 1rem)); padding-bottom: 1rem; padding-bottom: var(--grvsc-padding-bottom, var(--grvsc-padding-v, 1rem)); border-radius: 8px; border-radius: var(--grvsc-border-radius, 8px); font-feature-settings: normal; line-height: 1.4; } .grvsc-code { display: table; } .grvsc-line { display: table-row; box-sizing: border-box; width: 100%; position: relative; } .grvsc-line > * { position: relative; } .grvsc-gutter-pad { display: table-cell; padding-left: 0.75rem; padding-left: calc(var(--grvsc-padding-left, var(--grvsc-padding-h, 1.5rem)) / 2); } .grvsc-gutter { display: table-cell; -webkit-user-select: none; -moz-user-select: none; user-select: none; } .grvsc-gutter::before { content: attr(data-content); } .grvsc-source { display: table-cell; padding-left: 1.5rem; padding-left: var(--grvsc-padding-left, var(--grvsc-padding-h, 1.5rem)); padding-right: 1.5rem; padding-right: var(--grvsc-padding-right, var(--grvsc-padding-h, 1.5rem)); } .grvsc-source:empty::after { content: ' '; -webkit-user-select: none; -moz-user-select: none; user-select: none; } .grvsc-gutter + .grvsc-source { padding-left: 0.75rem; padding-left: calc(var(--grvsc-padding-left, var(--grvsc-padding-h, 1.5rem)) / 2); } /* Line transformer styles */ .grvsc-has-line-highlighting > .grvsc-code > .grvsc-line::before { content: ' '; position: absolute; width: 100%; } .grvsc-line-diff-add::before { background-color: var(--grvsc-line-diff-add-background-color, rgba(0, 255, 60, 0.2)); } .grvsc-line-diff-del::before { background-color: var(--grvsc-line-diff-del-background-color, rgba(255, 0, 20, 0.2)); } .grvsc-line-number { padding: 0 2px; text-align: right; opacity: 0.7; } .quiet-light { background-color: #F5F5F5; } .quiet-light .mtki { font-style: italic; } .quiet-light .mtkb { font-weight: bold; } .quiet-light .mtk10 { color: #4B69C6; } .quiet-light .mtk6 { color: #777777; } .quiet-light .mtk1 { color: #333333; } .quiet-light .mtk4 { color: #448C27; } .quiet-light .mtk7 { color: #7A3E9D; } .quiet-light .mtk9 { color: #AA3731; } .quiet-light .mtk5 { color: #9C5D27; } .quiet-light .mtk3 { color: #AAAAAA; } .quiet-light .grvsc-line-highlighted::before { background-color: var(--grvsc-line-highlighted-background-color, rgba(0, 0, 0, 0.05)); box-shadow: inset var(--grvsc-line-highlighted-border-width, 4px) 0 0 0 var(--grvsc-line-highlighted-border-color, rgba(0, 0, 0, 0.2)); }","id":"79431640-36fe-54cc-86e7-2f0077328dcd","headerImage":null,"localHeaderImage":"/static/46f5be4a2e4475a51fff3c08e5373c46/4f402/5.jpg"}},{"_index":"lleedpartners-mdx","_type":"_doc","_id":"6af90167-a845-5918-9226-37d21fce2025","_score":28.41036,"_ignored":["body.keyword","frontmatter.excerpt.keyword"],"_source":{"fields":{"langKey":"en"},"frontmatter":{"title":"Automating document generation","subtitle":null,"slug":"/insights/automating-document-generation","category":"insight","headerImage":null,"localHeaderImage":{"childImageSharp":{"gatsbyImageData":{"layout":"constrained","backgroundColor":"#080808","images":{"fallback":{"src":"/static/6279660a34fd13d61a24d09a02ea1b37/9fb18/header.png","srcSet":"/static/6279660a34fd13d61a24d09a02ea1b37/201c8/header.png 496w,\n/static/6279660a34fd13d61a24d09a02ea1b37/7a1d0/header.png 992w,\n/static/6279660a34fd13d61a24d09a02ea1b37/9fb18/header.png 1984w","sizes":"(min-width: 1984px) 1984px, 100vw"},"sources":[{"srcSet":"/static/6279660a34fd13d61a24d09a02ea1b37/05a29/header.webp 496w,\n/static/6279660a34fd13d61a24d09a02ea1b37/449fa/header.webp 992w,\n/static/6279660a34fd13d61a24d09a02ea1b37/4f870/header.webp 1984w","type":"image/webp","sizes":"(min-width: 1984px) 1984px, 100vw"}]},"width":1984,"height":1024}}},"excerpt":"Automated document generation has the potential to revolutionize how businesses create and disseminate important documents. By automating the creation process, businesses can not only save time, but also improve the quality and coherence of their documents.","description":null,"icon":null,"published":null},"body":"Introduction Automated document generation refers to the use of software and technology to automatically generate documents based on pre-defined templates and content. This can include contracts, reports, marketing materials, and more. By automating the document creation process, businesses can save time and resources, improve the consistency and quality of their documents, and enhance brand perception. Main Gains to Automated Document Generation Focus on creating high-quality content without worrying about formatting, leading to more efficient document creation and maintenance, greater flexibility in layout and formatting, and improved document quality and coherence. Create on-brand templates, ensuring all documents have a consistent look and can easily be updated over time: no documents fall out of date. Generate on-the-fly documents with the most up-to-date information and custom content, while also minimizing storage needs and the need for manual updates. There are several key benefits to automated document generation. One of the main advantages is the ability to take a content-first approach. With automated document generation, the layout and styling of a document can be defined as a templates, while the content can be easily versioned and updated. This allows collaborators to focus on creating high-quality content, without worrying about formatting, which can lead to more efficient document creation and maintenance, as well as reduced friction in the content sourcing process. Another key benefit of automated document generation is the ability to create reproducible and consistent documents. By using templates that can be either provided or created, businesses can ensure that all of their documents have a consistent look and feel, which can help to improve brand perception. When needed, these templates can be updated to reflect changes in branding or design, and applied to all documents. This can help to ensure that all documents are up-to-date and that no documents fall out of date. Finally, automated document generation allows businesses to generate documents on demand, as needed. This can be especially useful for businesses that need to generate large numbers of documents on a regular basis, such as invoices or reports, but also to generate documents with custom content, such as marketing materials. Using user specific data, the documents can be adapted to better suit the needs of the user. Key Elements to Successful Automated Document Generation There are several key elements to consider when implementing automated document generation in your business. One of the most important factors is the use of easily usable exchange formats for file content. Markdown or its extensions (e.g. mdx ) are widely used, as these formats have a very lightweight syntax, that is already well-known to most collaborators. Being content-first, they also allow easy versioning, collaboration and indexation of content. Another important element is the use of easily customizable templates. Templates will usually be provided by the service provider or by a third-party, however they must remain easy and flexible to work with, so that brand updates and layout changes can be made without requiring elaborate intervention. Finally, it's important to have tools and technologies that allow for on-the-fly document generation and previewing. This will allow businesses to quickly and easily generate documents and ensure that they meet their desired specifications. Use simple file formats, such as .txt or .md, to store content, which can then be easily versioned, indexed and collaborated on. This reduces friction at the content sourcing stage. Use templates that can be easily modified to reflect changes in branding or design, and applied to all documents. This ensures that all documents are up-to-date and that no documents fall out of date. Generate documents on demand, as needed, using user or time-specific data, driving engagement and improving user experience. Existing Implementations and Toolings There are several existing solutions and toolings available for automated document generation. One approach is to use no or low-code platforms, such as automated invoices or Formstack , which allow businesses to create documents without the need for coding expertise. Another option is to use HTML printing using web browsers, although this can be tedious and time-consuming. A more versatile and suitable option for generating high-quality and data-heavy documents, such as McKinsey reports , is to use HTML to PDF tools such as PDFReactor or Prince . Working with Custom Solutions If you're considering implementing automated document generation in your business, it's important to be prepared and to start by automating heavily used document pipelines, such as invoices, trade confirmations, and reports. This will allow for easier user transitioning and will help to identify and address any bottlenecks in the document generation process. Once these pipelines are successfully automated, businesses can then expand the use of automated document generation to other document production pipelines, even those with heavily customized content and layout. .grvsc-container { overflow: auto; position: relative; -webkit-overflow-scrolling: touch; padding-top: 1rem; padding-top: var(--grvsc-padding-top, var(--grvsc-padding-v, 1rem)); padding-bottom: 1rem; padding-bottom: var(--grvsc-padding-bottom, var(--grvsc-padding-v, 1rem)); border-radius: 8px; border-radius: var(--grvsc-border-radius, 8px); font-feature-settings: normal; line-height: 1.4; } .grvsc-code { display: table; } .grvsc-line { display: table-row; box-sizing: border-box; width: 100%; position: relative; } .grvsc-line > * { position: relative; } .grvsc-gutter-pad { display: table-cell; padding-left: 0.75rem; padding-left: calc(var(--grvsc-padding-left, var(--grvsc-padding-h, 1.5rem)) / 2); } .grvsc-gutter { display: table-cell; -webkit-user-select: none; -moz-user-select: none; user-select: none; } .grvsc-gutter::before { content: attr(data-content); } .grvsc-source { display: table-cell; padding-left: 1.5rem; padding-left: var(--grvsc-padding-left, var(--grvsc-padding-h, 1.5rem)); padding-right: 1.5rem; padding-right: var(--grvsc-padding-right, var(--grvsc-padding-h, 1.5rem)); } .grvsc-source:empty::after { content: ' '; -webkit-user-select: none; -moz-user-select: none; user-select: none; } .grvsc-gutter + .grvsc-source { padding-left: 0.75rem; padding-left: calc(var(--grvsc-padding-left, var(--grvsc-padding-h, 1.5rem)) / 2); } /* Line transformer styles */ .grvsc-has-line-highlighting > .grvsc-code > .grvsc-line::before { content: ' '; position: absolute; width: 100%; } .grvsc-line-diff-add::before { background-color: var(--grvsc-line-diff-add-background-color, rgba(0, 255, 60, 0.2)); } .grvsc-line-diff-del::before { background-color: var(--grvsc-line-diff-del-background-color, rgba(255, 0, 20, 0.2)); } .grvsc-line-number { padding: 0 2px; text-align: right; opacity: 0.7; }","id":"6af90167-a845-5918-9226-37d21fce2025","headerImage":null,"localHeaderImage":"/static/6279660a34fd13d61a24d09a02ea1b37/9fb18/header.png"}},{"_index":"lleedpartners-mdx","_type":"_doc","_id":"80d8e69d-45b8-544b-8083-2b1b06c2ac90","_score":19.353945,"_ignored":["body.keyword"],"_source":{"fields":{"langKey":"en"},"frontmatter":{"title":"Intelligent Document Processing","subtitle":null,"slug":"/insights/intelligent-document-parsing","category":"insight","headerImage":{"childImageSharp":{"gatsbyImageData":{"layout":"constrained","backgroundColor":"#080808","images":{"fallback":{"src":"/static/8d980383e32353991eb6160e43b819cb/ea2c7/1*ejjcgvzsai4e1OkMZb4Gow.png","srcSet":"/static/8d980383e32353991eb6160e43b819cb/8c11c/1*ejjcgvzsai4e1OkMZb4Gow.png 250w,\n/static/8d980383e32353991eb6160e43b819cb/471f5/1*ejjcgvzsai4e1OkMZb4Gow.png 500w,\n/static/8d980383e32353991eb6160e43b819cb/ea2c7/1*ejjcgvzsai4e1OkMZb4Gow.png 1000w","sizes":"(min-width: 1000px) 1000px, 100vw"},"sources":[{"srcSet":"/static/8d980383e32353991eb6160e43b819cb/8f027/1*ejjcgvzsai4e1OkMZb4Gow.webp 250w,\n/static/8d980383e32353991eb6160e43b819cb/af253/1*ejjcgvzsai4e1OkMZb4Gow.webp 500w,\n/static/8d980383e32353991eb6160e43b819cb/4538d/1*ejjcgvzsai4e1OkMZb4Gow.webp 1000w","type":"image/webp","sizes":"(min-width: 1000px) 1000px, 100vw"}]},"width":1000,"height":600}}},"localHeaderImage":null,"excerpt":"A business study on Intelligent Document Processing and its strategical benefits to your workflows","description":null,"icon":null,"published":null},"body":"Document processing is inherent to any enterprise workflow. The numerous existing document formats, whether they are physical or digital, allow employees to efficiently share ideas and communicate both inside and outside their working environment. However, this vast and diversified world of commonly used formats (PDF, images, scans) does not always offer the flexibility needed by their users to fulfill their computer daily tasks. The data is being constrained by the very medium which contains it These tasks involve manual document classification (file organization, centralization across several computers, etc.), data extraction, excel conversion, document scanning, etc. They are often bottlenecks in the enterprise's productivity. In fact, every interaction with a document increases processing time and is error-prone. IDC, sponsored by Adobe , has carried out research on the operations of more than 1'500 line-of-business leaders to assess how document processing impacts business processes. Some striking figures can be found below IDC concludes that disconnected document processes adversely impact revenue, create audit issues, reduce business agility, employee productivity and increase operating costs while having a strong negative impact on customer experience. Nevertheless, recent developments in Artificial Intelligence and Machine Learning provide the necessary tools to address some of these issues. Towards “Intelligent” Document Processing Intelligent Document Processing (IDP) aims to provide end-to-end automation to document business processes. IDP is at the junction of Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP). It works best in companies dealing with large volumes of heterogeneous data. However, every company can benefit from IDE to speed up its processes, reduce the number of errors and dependency on human intervention. Intelligent Document Processing is an integral part of Digital Transformation A key step to grasp how IDP works is to distinguish between different structures of documents. The categories below are ordered by increasing amount of time required to extract information from documents. Structured documents : the content of these documents is well-organized. They share a common digital structure, such as an Excel spreadsheet. Computer systems can directly benefit from it to extract/query information and even automatically store them into a database. JSON is also a widely-used structured format in web-systems to store data. Semi-structured documents : we are referring to order forms, invoices or, more generally, any document generated from a template, but not bound to specific data fields. Unstructured documents : if you can freely choose the design/template of a document, you are most likely dealing with an unstructured document. Examples are contracts, articles or letters. The goal of any IDP system is to convert any document (invoice, form, report, scans, etc.) into the first category, aka a structured document. The following picture illustrates how an invoice can be transformed in a tabular file such as excel. In the output format, the invoice can easily be sent to an invoice database, without the need of any human intervention. That's the power of IDP. If you want to dive into the technical implementation details, please check our technical insight on document tagging and classification , which presents an open-source framework to perform IDP tasks in Python. References The Ultimate Guide to Document Automation 2021 Intelligent Document Processing with AI Why AI Is the Next Step in Document Processing ? 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