AI and market insights generation Today, numerous organizations seek new technologies to enhance the quality of their marketing research. According to Deloitte , one in four companies has already initiated the inclusion of Generative AI into marketing operations, showing a turn towards more innovative data methodologies. While most early adopters are still at the initial stage of using Gen AI primarily as a collector of data, this first wave of organizations is finding that AI is effective at processing collected info for high-quality conclusions about market patterns. A BCG report  showed that 70% of CMOs were using generative AI, and the top two use cases were personalization at 67%, followed by insight generation at 51%. Amazingly, insight generation came second, ranking even higher than content creation. Transforming marketing research with Generative AI. A case study in pharmaceutical insights Marketing research often involves two broad types of surveys to understand consumer behavior: quantitative and qualitative. Quantitative surveys  involve data that can be measured, such as rating a customer's experience in a store based on a scale of one to five. These bounded responses allow for KPI calculations and present insights in a data-driven manner. Qualitative studies , in turn, research consumer motivations and whys by asking open questions. It may be in-depth interviews that center around the main topic but use many angles to delve into meaningful insights. KHMARKA  developed a generative AI solution for a qualitative market study on digestive health conducted by a pharmaceutical company. The goal was to understand how people deal with indigestion, what remedies they use, and why such solutions were chosen. Classic qualitative studies use up many resources: targeting groups, conducting one-on-one interviews, recording conversations, transcribing data, and performing detailed analyses. Our AI-driven platform changed this by carrying out these interviews through an interface with chatbots, asking open-ended questions that could lead to meaningful discussions. Addressing research pain points with Generative AI Our solution directly addresses the major pain points for traditional market research methods. Scheduling and conducting interviews individually is extremely time-consuming and is often plagued by logistical problems, like no-shows or rescheduling conflicts. The transcribing of hours, and then reviewing recorded interviews manually, can be extremely cumbersome and, if done by junior and inexperienced staff, will often be subject to potential oversights or biases. The following are the biggest pain points that were successfully removed by KHMARKA’s solution: Pain point 1:  Qualitative surveys are extremely time-consuming and therefore very resource-intensive. Solution:  The generative AI solution improves this process by automating online interviews with an AI-powered chatbot. No more manual scheduling of interviews and no more in-person interviews; instead, hundreds of interviews can be done at the same time. AI transcription and automatic organization of the data decrease the manual effort for insights derivation. The whole process accelerates. Pain point 2:  Traditional surveys are bound by the number of interviews a human team can carry out. Solution:  AI-driven interviewing will have hundreds and thousands of interviews carried out at scale, where the traditional method is bound. Because AI handles conversations with efficiency and consistency, for the same amount of time taken to interview just a very few people using traditional methods, significantly larger sample sizes can be reached with maintained quality of data collection. Pain point 3:  Manual transcription, coding, and analysis of survey responses can take weeks or even months, which postpones the time to insight. Solution:  The AI system will transcribe and process this data in real-time. It can identify patterns quickly, segment responses, and generate detailed reports. Companies can derive actionable insight in a fraction of the time it would take using traditional methods by automating such processes. With AI-driven automation, these pain points disappear. Scale interviews are conducted by the chatbot, and the AI model performs transcriptions and insight extractions uniformly more accurately, and speedily. This frees researchers to focus their talents on strategic and creative work rather than administrative burdens. Better still, AI-driven solutions learn and improve with each additional wave of training, so they can surface valuable insights even better and deliver business-critical intelligence. Combining AI and LLMs for market research Our solution deploys AI powered by large language models (LLMs) to conduct in-depth qualitative research by engaging users in online interviews. In a virtual session, this AI-driven chatbot simulates the natural flow and depth that such conversations would have if guided by a human: asking open-ended questions, elaborating on them, and adapting its responses based on user inputs. For example, in our case study, the chatbot studied users on practices related to digestive health without suggesting any particular end to a conclusion so as not to prejudge any biases during data collection . It was told about the reasons why people employ activated charcoal treatments, from whom it was suggested, and the emotional and belief-related experiences associated with such a treatment. It fine-tunes the asking of questions: precise, relevant, rephrased for clarity, and which keeps the engagement going. Such adaptive questioning emulates the way experienced interviewers delve deeper into a response without human fatigue, bias, or inconsistency. The generative AI, therefore, conducted consistent, scalable, high-quality interviews whose rich data were analyzed. Getting insight in seconds, not weeks Traditionally, qualitative would be constrained by resource issues, usually capping interviews at about 20 participants. This number, marketers argue, is sufficient to identify broad patterns, although it may overlook important nuances. Our AI solution flips this paradigm on its head by allowing hundreds or thousands of interviews in the same amount of time. The true innovation really lies not just in scalability but in the depth of analysis and speed of insight.  It performs the transcription of data, analysis of responses, and organization of insights into comprehensive, easy-to-digest formats, such as tables and reports after every interview. Instead of weeks of researchers’ work, our solution makes the entire process happen in mere seconds. This capability gives organizations unparalleled access  to rich consumer insights. Marketers love presenting data in some order because it simplifies decision-making and helps formulate an effective messaging strategy. The AI will pick up commonalities, divide users based on behaviors, and outline patterns that might be very hard for a human to see. Such insights might be the system picking up that a large proportion of users rely on certain remedies based on the advice of family members, which shows key emotional and cultural associations. Expanding benefits across industries This solution goes beyond the benefits of pharmaceuticals by serving any business with a high level of marketing involvement. For example, in the highly competitive world of probiotics, where products are almost similar in nature, it all comes down to product differentiation. Marketers are constantly on the lookout for new channels and messages to connect better with consumers or for pain points to further address. Utilizing generative AI qualitative research, businesses are allowed to develop nuanced consumer preferences, craft messaging strategies, and overcome competition. See a few examples of industries that benefit from employing Gen AI for market research: The solution is beneficial to businesses in the technology sector , in attaining the perspective of how consumers view new products and technologies where innovation is a serious competitive factor. Understanding how consumers make buying decisions is of the essence in the retail and e-commerce  space. Such an AI-enabled approach may provide an identification of key touchpoints within the customer journey, whether online or in-store. Other big beneficiaries of this technology are industries that provide complex services, financial services, or telecommunications  because it enables large-scale qualitative studies to be done at a fraction of the cost and time. In each of these industries, the generative AI solution gives businesses the power to transcend generic market surveys and consider a far more in-depth and comprehensive view of one's customers. The future of market research with AI Our generative AI solution represents a sea change in the fast-moving market research landscape. It empowers companies to make data-driven decisions more efficiently and precisely by interviewing side-by-side with pattern extraction, delivering actionable insights at unprecedented speeds. This opens an entirely new frontier for compelling insight into consumer behavior and is redefining the way businesses relate to their audiences and position themselves for success.

Let’s build a secure, easy-to-use tech solution for your financial institution. Our experience includes developing solutions that fit right into your existing ecosystem, such as white-label solutions, mobile apps, and chatbots. We develop QR and NFC solutions that are used across various regions and industries to deliver a seamless payment experience for end users. Partner with us to build solutions that leverage global payment services such as Mastercard or Visa, as well as regional payment systems.  NFC Payment Solutions: Tap-and-Go for Emerging Markets NFC payments allow customers to make secure, contactless transactions by tapping their mobile device, smartwatch, or other device near a payment terminal. As early adopters, we were one of the first companies in Europe to launch NFC payment based on MDES (Mastercard services). We’ve already created similar projects for banks and tech companies from Europe and the MENA region. Our focus is on helping you deliver reliable, cashless payments regardless of location. We bring NFC payment solutions integrated with services from global or regional payment systems, including mainstream wallets like Apple Pay and Google Pay where available. QR Payments: Fast and Reliable We provide solutions for both merchants, enabling them to generate payment QR codes, and customers, enabling them to scan those QR codes and make payments. For the customer solution, we offer several implementation options, including a chatbot, a mobile app, or a dedicated payment SDK. Our solutions are built with Mastercard’s MasterPass technology, as this system protects customer data by storing sensitive information securely on Mastercard’s cloud.  Simple payment system for film festival merchants For a film festival project, we designed a QR-based payment experience within chatbots in messengers. Festival attendees could send a photo of a payment QR code generated by the merchant to the bot, which handled the payment confirmation and processing in seconds. This approach is not only fast but offers customers the freedom to pay through the platforms they already use. FinTech Solutions for Financial Institutions Mobile app for utility bill payments We created an app for everyday payments like utility bills. This was a white-label solution that fit into our customer’s ecosystem and allowed their clients to pay their utility bills within their app. This made managing bills much easier. People could skip trips to the bank and just pay directly from their phones. Chatbot for opening a bank account This solution was proudly recognized as a winner in a corporate fintech accelerator for its innovation and impact. With this technology, bank customers could easily open accounts through a simple, guided chatbot on their phone. Additionally, bank employees could efficiently manage the account opening process and quickly have all the necessary information at their fingertips. Credit history report bot  A chatbot was designed to let users quickly order and pay for their credit history report in just a few clicks. This solution simplifies the process, allowing users to get their credit history through the chatbot, without needing to navigate complex websites or make phone calls.

Extracting structured data from scanned PDFs or images can be a challenge. Existing tools often fail to recognize table formats. They misattribute data to incorrect columns. Teams must then correct the errors manually, which is costly and time-consuming. As big data, cloud computing, and artificial intelligence (AI) emerge as top technologies, about 75%  of companies plan to adopt them by 2027. Our table extraction tool embraces this shift. It uses AI to categorize data and output it in formats required by the customer's database.  Challenges in Data Extraction Inputting data from PDF tables manually or fixing errors from old tools is a slow and error-prone process. Between 2016 and 2030 , data input and processing tasks are expected to decline by 19% in the U.S. and 23% in Europe as automation takes over. As machines handle more basic tasks, it’s crucial for businesses to start automating manual data entry now. Manual entry can also increase the risk of errors. Existing tools often extract raw, unorganized data that requires additional sorting and correction. Many solutions also lack the flexibility to adapt to specific needs. They have trouble recognizing industry-specific terms or categorizing data correctly. These inefficiencies slow down workflows and divert resources from more valuable tasks. The AI-Powered Solution With our solution you can join 51%  of companies using automation to reduce operational costs drastically. For example, paper-based invoice processing costs about $2.7 trillion a year, according to Goldman Sachs. Automated systems like ours can cut expenses by at least 50%, and in some cases, as much as 80%, saving companies tens of thousands of dollars annually. Our tool uses AI to extract and structure data from tables automatically. It categorizes the content to meet specific business needs. For example, if a retail company uses different formats for the same product (e.g., mascara, Mascara, mascara item), the tool can learn to categorize them consistently into the same category, reducing the need for manual adjustments. The tool outputs data in formats like JSON, ready to integrate into any system, reducing the need for manual intervention. Industry Applications Retail & E-commerce Retailers handle large product inventories that frequently change. Automating product info extraction from vendor invoices or inventory lists streamlines updates to the inventory database and product listings. Our tool saves hours of effort by avoiding manual entry for thousands of SKUs. Accounting & Finance Financial documents such as invoices, balance sheets, and expense reports often contain tables. Our tool can extract and organize data from these scanned documents. It then automates data entry into financial software. This ensures that financial reporting is accurate and up-to-date. Supply Chain & Logistics Automating data extraction from reports and tables speeds up stock management. We assist logistics teams by integrating this data into their systems. Manufacturing Manufacturers rely on accurate data to manage resources, production schedules, and supplier reports. Extracting and organizing data from scanned tables helps businesses. They can optimize operations and reduce downtime from outdated info. The tables contain materials, production times, and equipment availability. Healthcare Automating the extraction of large volumes of data from medical records, patient billing information, and treatment plans into electronic health record (EHR) systems or billing software saves medical staff time. This way, they can focus more on patient care rather than administrative tasks. Conclusion and Next Steps Our AI-powered table extraction tool provides a solution for businesses with data-heavy workflows. By automating table extraction from scanned PDFs and other documents, companies can greatly speed up data processing and optimize their operations. According to the latest   McKinsey Global Survey  on AI in 2024, 65 percent of those surveyed say their organizations are regularly using Gen AI. We believe it’s high time you joined their ranks.   Interested in learning more? Contact us today to see how our solution can be customized for your specific needs and start saving time and resources.

Intro. Automation in recruitment The life of a headhunter is immensely competitive. They are in a race against time, having to get to the best candidate before any other competitor. They cannot afford to waste a lot of time on things like manual tasks or people who don't fit. With growing pressure to free their time up, hiring managers look at better ways of trying to find the right candidates. Automated tools, powered by AI, therefore, come in handy and speed up the sourcing of candidates, reducing the manual workload that recruiters and candidates have to put up with. The need for chatbots in mass hiring Technology can make recruiters' lives much easier. Among other AI-powered hiring tools, chatbots solve problems by performing many recruitment-related tasks: sourcing candidates, screening applications, scheduling interviews, etc. According to a study by Intelion Systems , up to 45% of companies already use some AI recruitment tools, while 99% of Fortune 500 HR departments work on the implementation of these solutions. As Forbes  clarifies, the main problem that chatbots resolve for mass hiring is the repetitive nature of the recruitment process . For those positions that don't require some specific skill, for instance, entry-level or part-time jobs, recruiters have to ask simple and repetitive questions: whether applicants have a driver's license or can work this or that shift. The chatbot can automate all these interactions, immediately collecting from applicants all the required information. Also, chatbots overcome yet another important obstacle that comes with mass recruiting: applicant data quality. Lots of candidates for low-skilled posts may not have extensive resumes or any at all. That is where chatbots step in gathering relevant information from the candidates themselves and making sure recruiters get a complete profile without incomplete resumes. Who benefits from chatbots in recruitment? Businesses that hire en masse usually have to fill an immense number of positions quickly and regularly. Very high turnover rates and seasonal spikes in demand for hiring are common in companies of many verticals. Automation through chatbots is a savior in such industries, as recruiters cannot plow through thousands of applications manually. Let's take a closer look at some of the dozens of industries benefiting from chatbots in mass hiring and the needs being solved:  Retail and e-commerce:  constant needs for cashiers, sales associates, and warehouse staff. Hospitality:  turnover tends to be quite high among hotel staff, waiters, and cleaners, among other groups. Customer Service:  usually accepts numerous customer service representatives to call centers;  Healthcare:  one of the most needed sectors in nursing assistants, caregivers, and administrative people. Logistics:  thousands of seasonal and full-time warehouse workers are needed at Amazon, FedEx, and similar companies.  Finance and Banking:  consistent demand for customer service representatives, credit consultants, and loan officers. A few years ago, IKEA, the Swedish furniture giant, invested in a recruitment chatbot  as its candidate conversion rate dropped to a minimum in Eastern Europe. This chatbot helps an organization find the right people better and scan candidates in real-time. Since embracing the chatbot, IKEA has seen an enhancement in candidate conversion rate by 10%, with a doubling in the volume of applications every month. Key advantages of chatbots in mass hiring: Time efficiency: the chatbots screen and schedule interviews, among other mundane tasks that eat into a recruiter's time. Improved candidate experience:  quick responses from the chatbot raise engagement and make the application process much smoother. Cost-effectiveness: since the need for human intervention is minimal in these early-stage tasks, the business will save on its recruitment costs. Scalability:  chatbots allow for variances in demand for hiring; hence, they are optimal for businesses having ongoing or seasonal recruitment needs. In that case, organizations free themselves to focus on other more strategic work they try to do, such as improving employer branding or making holistic improvements in the candidate experience. With chatbots, there will be no variability in performance in high-turnover industries, enabling recruiters to stay in front of demand and keep a smoothed-out pipeline. Challenges of chatbots in mass hiring Overall, there are a few challenges in the chatbot solution for mass hiring, and most of these relate to organizational factors rather than technological ones . A major issue is the need for an alignment between HR and IT departments. Human resource professionals may not be comfortable with the technicalities of a solution or may be unwilling, upon revisions in job descriptions/requirements, to handle changes to the solution. Also, IT usually favors those projects that directly result in revenue generation, making HR tech solutions, like chatbots, low on the list. It creates friction and uncertainty regarding who maintains the system or provides a budget for support. Other challenges are the complexity of the establishment and management of these systems. The development of chatbots requires a lot of time and resources, with their continual updating to make them relevant. A poorly configured chatbot can fail to provide the expected outcomes and may irk the HR teams and candidates alike. Besides, the absence of a human touch at early recruitment may discourage a few candidates, which again brings up the topic of striking a balance between automation and a personalized approach. Khmarka’s chatbot solution for mass hiring Based on the advantages and challenges above, a Ukrainian engineering company called Khmarka has elaborated an advanced chatbot solution for mass hiring. It is conceived to improve such a process without demanding advanced technical skills from an HR specialist. Its interface allows HR teams to easily change the chatbot's interactions, add new vacancies, and update processes on the go. Khmarka's chatbot automates all the key tasks of filtering the CVs and responses of candidates, providing a single platform for storage and tracking applicants at every stage of the recruitment funnel. It makes it easy to track the progress of the candidate for efficient hiring in cases of mass recruitment. The solution already works successfully in the market and changes the way Khmarka’s clients recruit. Their experience is often described as "before" and "after" chatbot implementation. Most notably, the company has successfully integrated the solution in the banking sector and pharmaceutical retail. The time for processing candidate applications has been reduced; this has cut down the time spent on searching for candidates by 24%. Several features of Khmarka’s chatbot make the recruitment process much easier: Easy-to-use chatbot editor  that modifies text flows, adds vacancies and updates interactions without technical skills. Automated CV generation  based on candidate responses. Centralized admin panel  to store and track candidate information, filter applications, and monitor progress. A candidate funnel that tracks each stage from application to hire. Statistics  on how candidates are sourced and applied to allow for improved decision-making. Khmarka's solution has already answered the challenges of implementation and further maintenance, as it allows recruiters to manage the system themselves without depending on IT. A chatbot editor and an admin panel allow for fast and effective adaptation of the recruitment process by HR teams. This is one of many development points of AI technologies for deeper interviews with candidates, along with feedback that will be very valuable in the process of hiring. By addressing these operational challenges, the chatbot helps recruiters save time and resources, improves candidate management, and facilitates faster hiring, particularly in mass recruitment scenarios.   Our solutions already work successfully in the market and change the way our clients recruit. Their experience is often described as "before" and "after" chatbot implementation. Most notably, we have successfully integrated these solutions in the banking sector and pharmaceutical retail. The time for processing candidate applications has been reduced; this has cut down the time spent on searching for candidates by 24%. Conclusion. The future of chatbots in recruitment Increased demand for automation in mass hiring opens up vast opportunities for more innovative AI-driven solutions, such as chatbots. For example, traditional hiring typically involves a lot of wasted time and inefficiency, given that hundreds of candidates often pass through the process. Chatbots, therefore, help reduce repetitive tasks from screening to scheduling and delivering necessary information, freeing some time for recruiters and improving the candidate experience. However, technical complexity and concerns about maintenance are challenges that, until now, have throttled the wide adoption of chatbot solutions. Overcoming such challenges, Khmarka's chatbot solution will be exemplary for the future in mass hiring and solve major challenges like scalability, time efficiency, and ease of use. Automating critical stages in the recruitment process and simplifying candidate tracking, this solution by Khmarka effectively meets the operational requirements and challenges of large-scale hiring.