AlphaSense vs Kensho AI in Financial Services

AlphaSense vs Kensho AI: Introduction

Artificial intelligence is revolutionizing financial services, providing firms with data-driven insights, faster analysis, and improved decision-making. Two of the leading AI solutions in the financial sector are AlphaSense and Kensho AI. The financial industry has always been at the forefront of technological adoption, from the first electronic trading systems to today's sophisticated algorithmic trading platforms. However, the recent explosion in artificial intelligence capabilities has triggered perhaps the most significant transformation yet, fundamentally changing how financial professionals research, analyze, and make decisions. In this rapidly evolving landscape, AlphaSense and Kensho AI have emerged as pioneering solutions, each offering unique approaches to harnessing AI for financial intelligence. For a look at how AI is transforming other industries, see Butterfly iQ: Revolutionizing Ultrasound and DeepMind vs Hugging Face.

The volume of financial information generated daily has grown exponentially over the past decade, creating both opportunities and challenges for industry professionals. Market data, company filings, earnings calls, news articles, research reports, and social media all contribute to an overwhelming flood of information that no human analyst could possibly process comprehensively. This information overload has created a critical need for AI-powered tools that can not only aggregate data from disparate sources but also extract meaningful insights, identify patterns, and highlight relevant information that might otherwise be missed. AlphaSense and Kensho AI represent two distinct but equally innovative approaches to solving this fundamental challenge. For more on AI-powered search and analysis, see DeepSeek vs OpenAI.

The Evolution of AI in Financial Services

The integration of artificial intelligence into financial services represents the culmination of decades of technological advancement, beginning with basic automation and evolving into today's sophisticated cognitive systems. The 1980s saw the first wave of computerization in finance, with electronic trading systems and basic analytical tools replacing manual processes. By the 1990s, quantitative analysis had gained prominence, with mathematical models driving investment strategies and risk assessment. The early 2000s brought increased computational power and the rise of algorithmic trading, where predefined instructions executed trades based on market conditions, timing, and price. However, these systems, while powerful, were fundamentally rule-based and lacked the ability to learn from experience or adapt to changing conditions without explicit reprogramming. For a comprehensive look at AI frameworks, see TensorFlow vs PyTorch.

The true AI revolution in finance began in the 2010s, driven by breakthroughs in machine learning, natural language processing, and big data analytics. These technologies enabled systems to move beyond rigid rules to identify patterns, generate insights, and even make predictions based on vast amounts of structured and unstructured data. Financial institutions quickly recognized the potential of these capabilities to transform everything from investment research and portfolio management to risk assessment and regulatory compliance. Early applications focused primarily on automating routine tasks and enhancing existing analytical processes, but as the technology matured, more sophisticated use cases emerged. Today's financial AI systems can analyze sentiment in earnings calls, identify emerging market trends from news and social media, detect anomalies that might indicate fraud, and generate investment theses based on comprehensive market analysis. For more on NLP advances, see GPT vs BERT vs T5 and Gemini 1.5 vs Phi-3.

What is AlphaSense?

AlphaSense is an AI-powered search engine designed for financial professionals, analysts, and investment firms. Founded in 2011 by Jack Kokko and Raj Neervannan, AlphaSense emerged from the founders' firsthand experience with the challenges of financial research during their careers at Morgan Stanley. They recognized that despite the wealth of information available to financial professionals, finding relevant insights remained extraordinarily time-consuming and inefficient. Traditional search tools were poorly suited to financial research, lacking the specialized vocabulary and contextual understanding needed to identify truly relevant information across disparate document types and sources. AlphaSense was created to address this fundamental problem, applying artificial intelligence to transform how financial professionals discover, analyze, and leverage market intelligence. For another example of AI transforming research, see Shopify Magic Canada.

Key Features of AlphaSense

• Advanced Natural Language Processing (NLP): AlphaSense employs sophisticated NLP algorithms specifically trained on financial terminology and concepts. This specialized training enables the platform to understand nuanced financial language, including industry-specific jargon, technical terms, and contextual meanings that general-purpose search engines typically miss. The system can distinguish between different uses of the same term across various contexts and industries, ensuring that searches return truly relevant results rather than merely keyword matches. This capability is particularly valuable when researching complex financial concepts or specialized industry segments where terminology may be highly specific or evolving.
• Smart Synonyms Technology: One of AlphaSense's most distinctive features is its proprietary Smart Synonyms technology, which automatically expands search queries to include related terms, industry-specific language, and common variations. When a user searches for a concept like "revenue growth," the system automatically includes related phrases such as "sales increase," "top-line expansion," and industry-specific metrics. This capability ensures comprehensive research coverage without requiring users to manually construct complex Boolean searches or know every possible way a concept might be expressed across different companies or documents. The synonym database is continuously updated and refined based on actual usage patterns in financial documents, ensuring it remains current with evolving terminology.
• Comprehensive Document Coverage: AlphaSense aggregates content from an extensive range of premium financial sources, including SEC filings, earnings call transcripts, broker research reports, press releases, news publications, trade journals, and company presentations. The platform maintains historical archives going back many years, enabling longitudinal analysis and trend identification. This comprehensive coverage ensures that users can conduct thorough research across multiple information sources through a single interface, rather than needing to search separate databases or platforms. The system also includes content from thousands of private companies and industry sources that might not be available through public databases, providing insights into competitive landscapes and market dynamics.
• Sentiment Analysis: The platform's AI can analyze the tone and context of discussions in earnings calls, analyst reports, and news coverage to determine positive, negative, or neutral sentiment around specific topics or companies. This capability helps users quickly identify changing market perceptions, potential risks, or emerging opportunities that might not be immediately apparent from factual statements alone. Sentiment analysis can be particularly valuable for tracking how market narratives evolve over time or comparing management commentary across different companies in the same sector. The system can highlight significant sentiment shifts that might warrant further investigation or signal changing business conditions.
• Real-time Alerts and Monitoring: Users can create customized alerts to receive notifications when new information becomes available on specific companies, topics, or trends of interest. These alerts can be configured based on document type, source, keyword relevance, and other parameters to ensure users receive timely updates without information overload. The monitoring system continuously scans incoming documents across all sources, enabling financial professionals to stay informed about relevant developments without needing to manually check for new information. This capability is particularly valuable for time-sensitive research or when tracking rapidly evolving situations that might impact investment decisions or business strategies.
• Collaboration Tools: AlphaSense includes features designed specifically for research teams, allowing analysts to share insights, annotate documents, and collaborate on research projects within the platform. Users can highlight important passages, add notes and comments, and share specific documents or search results with colleagues. These collaborative capabilities help organizations leverage collective expertise and institutional knowledge, while also creating searchable records of analysis and insights that remain accessible even as team members change roles or leave the organization. The platform's permission settings allow administrators to control access to sensitive information or premium content based on user roles and requirements.
• Mobile Accessibility: Recognizing that financial professionals often need information access while away from their desks, AlphaSense offers mobile applications that provide core functionality on smartphones and tablets. The mobile interface is optimized for on-the-go research, allowing users to quickly search for information, review saved documents, and receive alerts while traveling or attending meetings. This accessibility ensures that critical information remains available whenever and wherever decisions need to be made, supporting the increasingly mobile and distributed nature of financial work, particularly in client-facing roles or during market-moving events that require immediate attention.

For more information, visit AlphaSense Official Website. For a comparison of AI-powered research tools, see DeepMind vs Hugging Face.

What is Kensho AI?

Kensho AI represents a fundamentally different approach to financial intelligence, focusing on quantitative analysis and predictive modeling rather than document search and research. Founded in 2013 by Daniel Nadler, a Harvard PhD, Kensho was conceived as a next-generation analytics platform that could answer complex financial questions by analyzing relationships between world events and market outcomes. The company's name, derived from Zen Buddhism and referring to achieving enlightenment through direct experience, reflects its mission to provide immediate, data-driven insights that cut through market noise and conventional wisdom. Kensho quickly attracted attention from major financial institutions and investors, culminating in its acquisition by S&P Global in 2018 for $550 million—at the time, one of the largest acquisitions of an AI company. For more on event-driven AI, see DeepSeek vs Kimiai.

Unlike traditional financial analysis tools that primarily process structured numerical data, Kensho pioneered the integration of unstructured data—news events, geopolitical developments, natural disasters, policy changes, and other real-world occurrences—into quantitative market analysis. The platform's core innovation lies in its ability to automatically identify correlations and causal relationships between these events and subsequent market movements across assets, sectors, and geographies. This capability enables users to quickly understand how similar historical situations affected markets and use those insights to inform current decision-making. For more on scenario modeling, see AI Revolutionizes Cybersecurity.

Key Features of Kensho AI

• Event-Based Market Analysis: Kensho's signature capability is its ability to analyze how markets and individual securities have historically responded to specific types of events or conditions. The platform maintains a comprehensive database of categorized events—from central bank policy changes and economic data releases to natural disasters and geopolitical developments—and their corresponding market impacts across different timeframes. Users can query this database to understand how similar historical situations affected markets, helping them anticipate potential outcomes and position portfolios accordingly. This event-based framework provides context that goes beyond traditional technical analysis or fundamental metrics, incorporating the real-world catalysts that often drive market movements.
• Machine Learning Forecasting Models: Kensho employs sophisticated machine learning algorithms to identify patterns and relationships in financial data that might not be apparent through conventional analysis. These models can process hundreds of variables simultaneously, detecting subtle correlations and non-linear relationships that traditional statistical methods might miss. The platform's predictive capabilities extend beyond simple trend extrapolation to incorporate complex interactions between multiple factors, enabling more nuanced forecasting of market behaviors under various scenarios. These models are continuously refined as new data becomes available, improving accuracy over time while adapting to changing market dynamics and relationships.
• Natural Language Generation: One of Kensho's most innovative features is its ability to automatically generate natural language narratives and reports based on data analysis. The system can transform complex analytical outputs into readable, contextually appropriate text that explains key findings, highlights significant patterns, and provides relevant context. This capability enables the automation of routine financial reporting, such as market summaries, earnings analyses, and economic updates, freeing human analysts to focus on higher-value interpretative work. The generated content maintains consistent quality and objectivity while incorporating the specific metrics and comparisons most relevant to the analysis at hand.
• Advanced Data Visualization: Kensho transforms complex quantitative analyses into intuitive visual representations that make patterns and relationships immediately apparent. The platform's visualization capabilities go beyond standard charts and graphs to include interactive elements that allow users to explore data from multiple perspectives, adjust parameters in real-time, and drill down into specific areas of interest. These visualizations are designed to communicate complex analytical findings to diverse audiences, from quantitative specialists to executive decision-makers who may lack technical backgrounds. The system automatically selects the most appropriate visualization formats based on the data being presented and the specific insights being highlighted.
• Alternative Data Integration: Kensho excels at incorporating non-traditional or "alternative" data sources into financial analysis, including satellite imagery, social media sentiment, mobile device location data, and other emerging information streams. The platform's flexible architecture allows it to ingest, normalize, and analyze these diverse data types alongside traditional financial information, creating a more comprehensive analytical framework. This capability is particularly valuable for identifying leading indicators or market signals that might not be captured in conventional financial data. The system includes robust data quality controls and normalization processes to ensure that insights derived from alternative sources are reliable and contextually appropriate.
• Scenario Analysis and Stress Testing: The platform enables users to model how portfolios or individual securities might perform under various hypothetical scenarios, from market corrections and interest rate changes to specific geopolitical developments or economic conditions. These scenario analyses incorporate both historical precedents and forward-looking models to provide comprehensive risk assessments. Users can customize scenarios based on their specific concerns or outlooks, adjusting parameters to test sensitivity to different factors or assumptions. This capability supports both risk management and opportunity identification, helping financial professionals prepare for potential market developments rather than simply reacting to them after they occur.
• API and Integration Capabilities: Kensho's architecture includes robust APIs and integration frameworks that allow its analytical capabilities to be embedded within other applications and workflows. This flexibility enables financial institutions to incorporate Kensho's intelligence into proprietary trading platforms, risk management systems, client-facing applications, and other enterprise software. The platform supports various data exchange formats and authentication methods, facilitating secure integration with existing technology ecosystems. This interoperability extends Kensho's impact beyond standalone analysis to become an embedded intelligence layer within broader financial technology infrastructures.

For more details, visit Kensho AI Official Website. For a comparison of AI-powered analytics, see AlphaSense vs Kensho AI.

Technological Foundations and Approaches

AlphaSense and Kensho AI represent distinct technological approaches to financial intelligence, each built on different AI foundations optimized for their specific use cases. AlphaSense's technology centers on advanced natural language processing (NLP) and semantic search capabilities designed specifically for financial documents. The platform employs deep learning models trained on millions of financial texts to understand the specialized vocabulary, contextual meanings, and implicit relationships that characterize financial communication. These models incorporate domain-specific knowledge about corporate disclosure practices, regulatory requirements, and industry terminology that general-purpose NLP systems typically lack. AlphaSense's proprietary Smart Synonyms technology represents years of development in financial linguistics, mapping the various ways similar concepts are expressed across different companies, industries, and document types. This specialized language understanding enables the platform to extract meaningful insights from unstructured text data that would be inaccessible to conventional search technologies. For more on transformer models, see Transformer vs RNN.

Kensho AI, by contrast, builds on quantitative analysis traditions, employing statistical modeling and machine learning to identify patterns and relationships in financial data. The platform's core technology includes sophisticated time-series analysis capabilities that can detect how markets respond to specific events or conditions across different timeframes and contexts. Kensho's machine learning models incorporate both supervised learning approaches, where algorithms are trained on labeled historical examples, and unsupervised techniques that can identify patterns without predefined categories. The platform employs natural language understanding to classify and categorize news events and other textual information, converting unstructured data into structured formats that can be incorporated into quantitative models. Kensho's visualization engine translates complex analytical outputs into intuitive graphical representations, employing cognitive science principles to ensure that insights are immediately comprehensible to human users regardless of their technical background. For more on AI for business, see OpenAI Sora.

AlphaSense vs Kensho AI: Feature Comparison

Feature	AlphaSense	Kensho AI
Natural Language Processing	Yes - Specialized for financial document search and analysis	Yes - Focused on event classification and market impact analysis
Market Insights	Yes - Derived from document analysis and sentiment tracking	Yes - Based on quantitative analysis of market responses to events
Predictive Analytics	Yes - Limited to sentiment trends and topic momentum	Yes - Comprehensive statistical modeling and scenario analysis
Economic Forecasting	No - Primarily focused on company and industry research	Yes - Includes macroeconomic modeling and impact analysis
Risk Assessment	Limited - Identifies risk factors mentioned in documents	Yes - Quantitative risk modeling and stress testing capabilities
Integration with Financial Data	Yes - Focuses on textual data with some financial metrics	Yes - Comprehensive integration of market data, alternative data, and textual information
Document Search	Yes - Industry-leading capabilities with Smart Synonyms	Limited - Basic search functionality for supporting documents
Sentiment Analysis	Yes - Sophisticated analysis of tone and context in documents	Yes - Primarily focused on market sentiment indicators
Collaboration Tools	Yes - Comprehensive sharing, annotation, and team features	Limited - Basic sharing capabilities for analysis results
Automated Reporting	Limited - Document highlighting and summary features	Yes - Advanced natural language generation for automated insights
Alternative Data Analysis	Limited - Some integration of news and social media	Yes - Comprehensive alternative data incorporation
Historical Analysis	Yes - Document archives and historical tracking	Yes - Extensive historical market data and event analysis
Mobile Access	Yes - Full-featured mobile applications	Limited - Basic mobile viewing capabilities
API Access	Yes - Available for enterprise customers	Yes - Comprehensive API ecosystem
Data Visualization	Limited - Basic charts for document analytics	Yes - Sophisticated interactive visualizations

Use Cases and Applications

AlphaSense excels in supporting fundamental research and analysis across investment management, corporate strategy, and advisory services. Investment analysts leverage the platform to conduct comprehensive company research, quickly identifying relevant information across earnings calls, SEC filings, broker research, and news sources. This capability is particularly valuable during earnings seasons, when analysts must rapidly process information from hundreds of companies to identify opportunities and risks. For example, an analyst covering the semiconductor industry might use AlphaSense to track mentions of specific technologies across all industry participants, identifying which companies are gaining or losing momentum in key growth areas. The platform's sentiment analysis helps users gauge management confidence and market perception, providing qualitative insights that complement quantitative metrics. Corporate strategy teams use AlphaSense to monitor competitive landscapes, track industry trends, and inform strategic planning processes. The platform's ability to surface relevant information from industry publications, expert interviews, and conference presentations helps these teams identify emerging opportunities and potential disruptions before they become widely recognized. For more on AI in business strategy, see Leonardo AI.

Kensho AI's strengths lie in quantitative analysis, scenario modeling, and automated intelligence generation for trading, portfolio management, and risk assessment. Traders and portfolio managers use Kensho to understand how specific market events historically impacted asset prices and correlations, informing positioning decisions during similar current situations. For instance, a fixed income trader might query how different types of Treasury securities typically perform following Federal Reserve policy announcements with specific characteristics, receiving instant analysis based on decades of historical data. Risk managers leverage Kensho's scenario analysis capabilities to stress-test portfolios against various market conditions, identifying potential vulnerabilities and optimizing hedging strategies. The platform's ability to incorporate alternative data sources—from satellite imagery to social media sentiment—provides early signals and insights not captured by traditional financial metrics. Financial media and research departments utilize Kensho's natural language generation to automate routine market reports and data summaries, freeing human analysts to focus on higher-value interpretative work while maintaining consistent coverage of market developments. For more on generative AI, see OpenAI Sora.

These distinct use cases highlight how the two platforms complement rather than directly compete with each other in many financial workflows. AlphaSense typically serves as the primary research platform for fundamental analysts and strategic decision-makers who need to deeply understand specific companies, industries, or thematic trends through comprehensive document analysis. Kensho, meanwhile, functions as a quantitative intelligence layer for market participants focused on understanding statistical relationships, modeling scenarios, and making tactical decisions based on historical patterns and predictive analytics. Many sophisticated financial organizations employ both platforms for different aspects of their investment processes—using AlphaSense for fundamental research and thematic discovery, then applying Kensho's quantitative capabilities to model potential market impacts and optimize execution strategies. This complementary relationship reflects the increasingly multi-faceted nature of financial decision-making, which requires both deep qualitative understanding and sophisticated quantitative analysis to navigate complex global markets effectively. For a look at AI in enterprise integration, see Google's Vertex AI.

User Experience and Accessibility

AlphaSense has designed its user interface to balance sophisticated functionality with intuitive operation, recognizing that its users range from research analysts who spend hours daily on the platform to executives who may access it only occasionally for specific insights. The platform employs a search-centric design that will feel familiar to users of consumer search engines, but with additional specialized filters and tools optimized for financial research. The main search interface allows users to construct queries using natural language or Boolean operators, with type-ahead suggestions and recently used searches to accelerate common workflows. Search results are presented in a clean, scannable format that highlights the most relevant sections of documents, with options to view full context or navigate to related content. The platform's document viewer includes annotation tools, highlighting options, and collaboration features that support detailed analysis and knowledge sharing. AlphaSense has invested significantly in performance optimization, ensuring that even complex searches across millions of documents return results within seconds, maintaining user engagement and productivity. For more on user-centric AI, see Brutalist AI Design.

Kensho AI approaches user experience from a different perspective, focusing on making complex quantitative analysis accessible to users without statistical or programming backgrounds. The platform employs a question-based interface where users can pose natural language queries about market relationships or historical patterns and receive instant analytical responses. These responses combine data visualizations, key statistics, and automatically generated narrative explanations that contextualize the findings. Interactive elements allow users to adjust parameters, explore different timeframes, or drill down into specific aspects of the analysis without requiring technical expertise. Kensho's visualization engine automatically selects the most appropriate graphical representations based on the data being analyzed, employing cognitive science principles to ensure that complex relationships are immediately comprehensible. For more technical users, the platform provides additional customization options and the ability to export results for further analysis in other tools, balancing accessibility with analytical depth. For a look at how AI is being used in creative domains, see Leonardo AI.

Integration with Existing Financial Workflows

AlphaSense has developed a comprehensive integration ecosystem designed to embed its research capabilities within existing financial workflows and systems. The platform offers browser extensions that allow users to access AlphaSense insights while viewing company websites, news articles, or other online content, providing contextual information without requiring users to switch applications. For enterprise customers, AlphaSense provides API access that enables programmatic searches, document retrieval, and alert management, supporting integration with proprietary research management systems, customer relationship management platforms, and other enterprise applications. The company has also established partnerships with major financial data providers and research distributors, enabling seamless access to premium content within the AlphaSense environment. These integrations support common workflows such as company screening, comparative analysis, and thematic research, allowing users to move fluidly between quantitative data analysis and qualitative document research. AlphaSense's export capabilities enable users to incorporate platform insights into investment memos, client presentations, and internal research documents, maintaining attribution and source tracking for compliance purposes. For more on AI integration, see Amazon Q vs GitHub Copilot.

Kensho AI, particularly since its acquisition by S&P Global, has pursued deep integration with established financial information ecosystems. The company's technology now powers intelligent features across S&P Global's product portfolio, including automated insights in Capital IQ, event-driven analytics in Market Intelligence, and scenario analysis tools in risk assessment platforms. Beyond these native integrations, Kensho offers API access that allows financial institutions to incorporate its analytical capabilities into proprietary trading platforms, risk management systems, and client-facing applications. The platform's modular architecture enables organizations to leverage specific capabilities—such as event classification, market impact analysis, or natural language generation—within existing workflows without adopting the entire Kensho ecosystem. This flexibility is particularly valuable for large financial institutions with established technology infrastructures that seek to enhance specific capabilities rather than replace entire systems. Kensho's integration approach emphasizes embedding intelligence within existing tools rather than requiring users to adopt new interfaces, reducing friction in adoption while maximizing impact on decision-making processes. For more on AI integration in the enterprise, see Google Meet Gets Smarter.

Pricing Models and Return on Investment

AlphaSense employs a subscription-based pricing model that varies based on user numbers, content access, and feature requirements. Individual professional licenses typically range from 10,000 to 25,000 annually, while enterprise deployments with multiple users and premium content can reach six or seven figures depending on scope. The platform offers tiered content packages that allow organizations to select the information sources most relevant to their needs, from basic company filings and news to premium broker research and expert interview transcripts. Feature access also follows a tiered structure, with advanced capabilities like API access, custom alerting, and administrative tools available at higher subscription levels. This flexible approach allows organizations to scale their investment based on user needs and expected value, starting with focused implementations before expanding across teams or departments. AlphaSense typically requires annual commitments, though the company sometimes offers quarterly options for initial deployments to reduce adoption risk. For large enterprise customers, AlphaSense provides customized pricing that reflects specific content requirements, user numbers, and strategic partnership opportunities. For more on AI ROI and business value, see AlphaSense vs Kensho AI and Shopify Magic Canada.

Market Adoption and Industry Trends

The adoption of AI-powered financial intelligence platforms like AlphaSense and Kensho reflects broader industry trends reshaping how financial institutions leverage technology and information. Several key factors are driving accelerated implementation of these solutions: information overload management, margin pressure and operational efficiency, talent attraction and retention, and regulatory compliance requirements. Market adoption patterns reveal interesting segmentation across the financial services landscape. AlphaSense has gained particular traction in asset management firms, investment banking and advisory, corporate strategy teams, and expert networks and research providers. Kensho, meanwhile, has found its strongest adoption among quantitative investment firms, risk management departments, financial media and research distribution, and market-making and trading operations. For more on AI adoption in industry, see GitLab 16: A New Era of DevOps Efficiency.

Data Security and Compliance Considerations

Financial organizations implementing AI platforms must navigate complex security and compliance requirements that reflect both regulatory obligations and the sensitive nature of financial information. Both AlphaSense and Kensho have developed comprehensive security frameworks to address these concerns, though with different emphases reflecting their distinct use cases and deployment models. AlphaSense's security architecture addresses several critical dimensions: data encryption, access controls, compliance tracking, private cloud deployments, and regular security assessments. Kensho's security framework emphasizes secure API architecture, data segregation, on-premises options, model governance, and integration with enterprise security. Both platforms maintain compliance with key financial industry standards and regulations, including SOC 2 Type II Certification, GDPR compliance, CCPA compliance, and information barrier support. For more on AI and compliance, see Reliance AGM 2024.

Training and Change Management

Successful implementation of sophisticated AI platforms requires thoughtful approaches to training and organizational change management. The technical capabilities of AlphaSense and Kensho deliver value only when effectively integrated into existing workflows and adopted consistently by target users. Organizations that achieve the highest returns from these platforms typically develop comprehensive training and change management programs tailored to different user types and use cases. Effective training strategies for AlphaSense typically include role-based learning paths, hands-on workshops, feature progression, workflow integration focus, and internal champions programs. Kensho implementations benefit from training approaches that address both technical operation and analytical interpretation: conceptual foundations, use case libraries, interpretation guidelines, progressive complexity, and peer learning. For more on AI training, see PolyAI: Advanced AI Chatbots.