AI Applications for Corporate Services

According to several studies, teams in corporate areas can spend between 10 and 40% of their time dedicated to necessary but routine and repetitive tasks. At PredictLand, we design AI-based automation applications that free these teams for projects of more strategic value.

We employ Machine Learning algorithms for document classification tasks, information extraction and data analysis, and generative AI models for automatic content generation. Here are some examples of applications:

• Categorization of administrative, legal, accounting documents
• Extraction of information from unstructured data (contracts, invoices, etc.)
• Reconciliation of receipts
• Automatic approval of timesheets and payroll calculations
• Generation of expense reports from scanned receipts
• Generation of periodic reports (financials, etc.)
• Automatic drafting of purchase orders
• Automatic generation of job descriptions and contracts
• Conversion of recordings and meeting notes into formal, summarized documents

AI-powered chatbots are revolutionizing the IT helpdesk experience and efficiency. Our application acts as the first point of contact, automating ticket management and task assignment. With innovative generative AI capabilities, it also provides personalized responses that previously required human intervention and extensive resolution times.

• 24/7 query classification: Equipped with machine learning algorithms, our models learn from every interaction. This continuous improvement in learning enables more accurate query classification, identifying patterns in frequently asked questions and reported incidents to deliver effective solutions faster. Depending on the complexity, the query can be resolved autonomously or redirected to the corresponding support team to ensure efficient management.

• Personalized interactions: Using generative AI techniques such as embeddings, indexing and Deep Language Models (LLMs), our systems generate personalized responses in natural language, in conversational mode. They support their response with external sources such as web pages and internal company resources, such as incident histories, or technical manuals and procedures, to provide accurate and contextual assistance to users.

As more tickets are resolved, the system becomes richer, expanding its knowledge base and refining its ability to resolve increasingly complex queries.

While traditional software can be useful for simple rule-based forecasts, our models based on machine learning techniques offer more advanced, granular, and automated solutions for cash flow forecasting.

Our systems offer two main advantages:

• Automating the collection and analysis of data from multiple sources, such as invoices, bank statements, accounting software and CRM systems. These sorting and extraction tasks not only save time, but also reduce human error.

• Generating accurate and dynamic cash flow forecasts: we use predictive models to analyze historical trends and payment patterns. These models draw from a wide variety of data sources, including internal transactions, market conditions and even macroeconomic indicators, to generate robust financial projections. Each new transaction feeds the system, which dynamically learns and modifies its forecasts.

Mitigating transactional risk is key for companies with financial operations. Our advanced artificial intelligence models combine predictive analytics and prescriptive algorithms for a detailed and dynamic assessment of customer profiles. This includes the use of techniques such as Machine Learning and neural networks to detect and act on atypical behavior and trends that may indicate credit or fraud risk.

The key to our approach is adaptability and continuous learning. As new transactions are processed, our models are refined to improve predictive accuracy. This iterative improvement allows for more accurate risk classification, adjusting customer credit profiles in real time, and providing a proactive response to suspicious transactions.

We also apply sentiment analysis and natural language processing techniques to assess customer communication, and detect early warning signals that may indicate dissatisfaction or potential fraudulent behavior. By correlating these signals with transactional patterns, our solutions provide a 360° view that helps companies minimize their risk exposure and, when necessary, make informed and personalized decisions for each customer.

We design Machine Learning algorithms to predict absenteeism and the risk of abandonment by employees, or groups of employees. This information is actionable to improve personnel management and minimize productivity losses.

• Absenteeism Forecasting: Our models analyze historical data to identify patterns that may indicate future absences. We include various factors, such as shifts, staffing levels and specific calendar dates, among others, to adjust the accuracy of the forecast.

• Employee Turnover Forecasting: We combine data such as demographics, geography, performance metrics, compensation details and surveys with external macroeconomic data. By predicting when employees or categories of employees are likely to leave the company, our clients can take proactive and targeted retention measures.

We designed generative AI-based applications to improve the effectiveness and efficiency of contract review, a significantly time-consuming routine operation in legal departments.

We use advanced instructions and algorithms based on deep language models (LLMs) to efficiently and accurately review large volumes of contract documents. The system identifies key terms, obligations, rights and exclusion clauses, as well as discrepancies with corporate standards, in a fraction of the time that a manual review would require.

The application not only detects the standard elements of a contract. It also assesses risk based on historical patterns and predefined criteria, and generates alerts on critical issues requiring human attention. This allows legal teams to anticipate potential conflicts or problems, and to make informed decisions about negotiating and signing contracts.