‍Data Reply, the Reply company specialized in artificial intelligence, big data, and advanced analytics, and Nebuly, the user analytics platform purpose-built for Generative AI, have announced a partnership to improve the performance, reliability, and measurable value of enterprise Generative AI solutions.

Individual user tracking misses the bigger picture. Discover how tag-based user cohorts reveal organizational AI adoption patterns and drive business impact through behavioral segmentation.

AI projects fail due to cultural gaps, not technical issues. Learn how to build user analytics culture that drives AI adoption through human-centered measurement and cross-functional collaboration.

Traditional AI monitoring misses critical business risks hiding in conversations. Discover how proactive risk detection in AI interactions protects compliance, reputation, and legal liability.

Many AI conversations end prematurely, but traditional metrics miss this failure. Learn how conversation drop-off analysis reveals hidden user frustration and drives better AI experiences.

Discover why web analytics fails for conversational AI and how User Analytics for GenAI creates a new discipline. Learn why the AI era demands measuring human insights, not just clicks and conversions.

AI copilots capture real feedback every day. Learn how Nebuly helps companies turn conversational data into insights that reveal productivity gaps, user friction, and risk.

HR can spot workplace friction and risky behavior through AI conversation analytics. Anonymized trends show what employees need and where culture needs support.

Learn how legal teams use AI user analytics to prevent compliance violations through proactive risk detection. Track risky behaviors without storing PII or individual conversations.

FT Strategies is the specialist media consultancy owned by the Financial Times, helping news and media organisations worldwide tackle strategic challenges, drive sustainable growth and innovate with AI, technology and data.

Discover the five core metrics for Generative AI user analytics – retention, topic analysis, risky behavior, hours saved, and error rate – and how they help enterprise teams gauge AI adoption, user satisfaction, and safe usage.

Discover how advanced user analytics transform AI infrastructure investments into measurable business value. Learn to track real-time agent performance, boost adoption, and prove ROI across enterprise AI deployments.

Seeing real business value from internal AI should be simple. Nebuly now lets you measure hours saved with every use of your AI, across teams, geographies, and workflows.

Discover how enterprises are closing the sector gap in AI adoption through strategic implementation and user analytics. Learn data-driven approaches to overcome industry-specific barriers and accelerate AI transformation across finance, retail, manufacturing, and healthcare sectors.

Learn how enterprise executives build AI agents value dashboards using user analytics to measure ROI, track adoption, and demonstrate business impact across finance, retail, and manufacturing sectors.

Learn how a top-tier bank with 80,000+ employees used GenAI user analytics to prevent compliance violations, detect hallucinations, and scale AI adoption across departments while maintaining strict data privacy.

Discover the valuable marketing insights hiding in GenAI conversations that most marketing teams don't know exist. Learn why comprehensive user analytics beyond thumbs up/down ratings could transform campaign effectiveness.

See how user analytics from GenAI conversations deliver actionable insights across every department, from sales and operations to HR, revealing business opportunities that technical monitoring alone can’t uncover.

Proving ROI for AI assistants in healthcare requires more than system logs. Learn how to measure financial outcomes, workflow impact, and user analytics signals like topic analysis, retention rate, risky behavior, and error rate.

Anthropic and OpenAI recently shared how people use AI through public reports. This post explains how user analytics gives companies their own "AI economic index" by analyzing internal GenAI tool usage – revealing user intent, friction points, drop-offs, and satisfaction to drive better AI experiences.

Generative AI is transforming healthcare with applications from clinical documentation to drug discovery. Explore key use cases, adoption challenges, and why measuring user behavior is essential to scale AI copilots safely.

Learn how banks can transform employee-copilot interactions into systematic feedback loops that improve AI model performance, reduce risk, and drive measurable business outcomes across trading, compliance, and operations.

Most banks track AI uptime, not AI adoption. See why financial services need user behavior analytics to make GenAI transformation actually work.

Learn how to define clear enterprise AI adoption benchmarks for 30, 60, and 90 days. Discover key metrics, user engagement strategies, and ROI measurement frameworks for successful AI transformation in finance, retail, manufacturing, and healthcare sectors.

Learn how to build reliable agentic AI systems with effective SLOs and human escalation protocols. Essential guide for enterprise leaders deploying autonomous AI agents.

Learn how enterprise product managers use AI copilot usage data to build ROI-driven roadmaps. Discover measurement frameworks for conversational AI analytics that drive business results in finance, healthcare, manufacturing, and retail sectors.

Learn how leading enterprises accelerate AI assistant time-to-value using early usage signals for rapid iteration. Discover proven strategies for moving from deployment to measurable ROI in weeks, not quarters, through intelligent analytics and continuous optimization.

How enterprise leaders can build an AI-first operating model that unites customer experience and fraud prevention with measurable adoption.

Discover how enterprises can manage GenAI risks beyond system metrics by tracking user behavior, compliance, adoption, and business impact.

Futureproof your GenAI investments with the right scaling strategy. Learn how enterprises can balance infrastructure, governance, and adoption metrics to turn pilots into business value.

Discover how CIOs prioritize buying versus building GenAI solutions. Explore model comparisons, key decision factors, and why user analytics matter for enterprise GenAI adoption, compliance, and ROI.

Worried about ChatGPT privacy? Discover how leading enterprises actually adopt LLMs, which provider features matter most, and why deep user analytics—not just system monitoring—are the key to secure, impactful GenAI scale.

Enterprise leaders face a choice with generative AI: start with internal copilots, customer-facing tools, or product innovation. This guide compares the options, explains why most begin internally, and shows how user analytics helps scale AI adoption

Why so many AI projects fail isn’t about models. It’s about missing user analytics. Learn how enterprises close the gap and build AI that works.

Generative AI adoption is rising fast, but most copilots stall without user feedback. Discover why every prompt should be treated as feedback and how user analytics turns GenAI adoption into real business value.

Internal AI copilots dominate because they’re lower-risk. Broad GenAI adoption stalls not due to the AI’s limits, but missing user analytics, feedback, and trust.

An MIT Media Lab report reveals the 'GenAI Divide': why 95% of AI projects stall. Learn how to bridge the gap with user analytics and feedback loops to drive real ROI.

Agentic AI adoption depends on trust. Discover why AI agents need continuous user feedback to evolve, earn confidence, and scale in the enterprise.

The hardest part of GenAI isn’t the AI. Learn how to overcome adoption challenges with change management, training, and user feedback.

How GenAI reshapes customer service KPIs with AI UX metrics, drop-off analysis, and user behavior data beyond traditional CSAT.

Regulated industries such as telecom and fintech demand strict control over data. Learn why self‑hosting generative‑AI models and choosing vendors with strong security certifications offers superior privacy, compliance, and autonomy.

A European media company built a financial AI assistant with analytics from day one. Real-time user data revealed key queries, reduced latency by 50%, and guided feature development for higher adoption.

An educational look at silent churn in enterprise generative AI: why focusing on behavioral data matters, how user analytics differs from observability, and what steps leaders can take to catch churn early.

Learn why keyword search and conversational AI serve different needs and how Nebuly’s user analytics and prompt insights help teams create better onboarding and AI experiences.

Most AI teams track latency and error rates but miss the metrics that truly drive adoption. Discover why user analytics — from intent achievement to retention — is the missing layer of your GenAI stack.

Who owns GenAI chatbots today, why ownership will decentralize, and why GenAI adoption data, alongside system metrics, is key to measuring and improving business value across every department.

Our user analytics platform is designed for teams running GenAI in production: product, operations, and beyond. Here’s how to know when it’s time to invest in user analytics for your GenAI products.

A global agricultural machinery enterprise used Nebuly’s AI user analytics to understand how its chatbot was being used across dealers. The data revealed where adoption was strong, where it dropped off, and why some regions had higher error rates.

Understand why context matters in generative AI and how LLM user analytics track intent, sentiment and conversation flow to turn correct answers into useful ones.

Many organizations are rushing to build internal copilots and other Large Language Model powered tools. Usage may grow quickly, but managers often still have an uncomfortable question. Does the tool deliver real value?

What is it, and why does it matter?

LLM Monitoring: Ensuring Optimal Performance and User Experience

The LLM Feedback Loop: A Complete Guide for Improving AI Models in Production

The Ultimate Guide to Analytics Agents: Enhancing Bot Interactions

Comparing OpenAI's Strawberry o1-preview and GPT-4o, highlighting their differences, optimal use cases, and helping you to decide which model to use.

Fine-Tuning vs. Prompt Engineering vs. RAG: Tailoring LLMs for Optimal Performance

MLOps and Model Monitoring: Ensuring Quality in AI and Machine Learning Deployments

Product Usage Analytics: Understanding User Behavior for Better Products in the LLM era.

ChatGPT User Research Tools: Unlocking Insights from Conversations

AI Product Analytics: The New Era of Understanding User Behavior with LLMs

Evaluating LLM Outputs: A Quick Guide for AI Professionals

Understanding LLM Accuracy: Navigating the Complex Landscape of Generative AI

Human-AI Interaction (HAX): The Impact of LLMs on how we Communicate and Collaborate with Technology

Nebuly’s SOC2 Type I: setting new standards in data security and what this means for you.

Guidelines for Effective Human-AI Interaction (HAX)

Key LLM statistics to understand and follow.

How AI is changing UX design and how we interact with technology?

Enhancing Customer Experience with Modern AI-Powered Chatbots

Topic Modeling: Techniques, Applications, and Advances with LLMs

Explore the top 60+ LLM and GenAI terms every enterprise leader should know. Includes user analytics metrics, observability concepts, and the most common enterprise models like GPT-4o, Claude, Gemini, and Mistral. Updated September 2025.

The Evolution of OpenAI GPT-4 API Pricing: The Drops from 2023 to 2024

Understanding AI Text Analysis: Transforming Text Into Insights

LLM Parameters, their role in taking LLMs into production

GPT-4o Mini vs Claude 3 Haiku vs Gemini 1.5 Flash: Comparing the Top Small Language Models

GPT-4o vs GPT-4o Mini: Comparison of OpenAI's Latest Models

The EU AI Act in a nutshell, what does it ban and what are the key dates.

Mastering AI Prompts: How to Maximize Efficiency and Creativity in AI Models

Open-source large language models (LLMs), list of top LLMs, differences in licenses and privacy considerations.

How are LLMs supported by AWS using Bedrock?

Discover the best LLM leaderboards of 2025, from Vellum and LLM-Stats to MCP-Universe and Prompt-to-Leaderboard. See how GPT-5, Grok-4, and new safety benchmarks are shaping evaluation trends this year. Last updated: September 2025.

Coactive learning, leveraging implicit user feedback to efficiently personalize and improve large language models (LLMs).

Understanding AI Accuracy: A Balanced Approach to Performance and Transparency

GPT-4 vs GPT-4o: Technical and practical comparison. The evolution of OpenAI's flagship Large Language Models (LLMs)

Comparing OpenAI's GPT-4o and Anthropic's Claude 3.5 Sonnet, focusing on their key features and ideal use cases.

LLM System Prompt vs. User Prompt. Key Differences and Best Practices.

How Large Language Models (LLMs) user experience (UX) can be enhanced by understanding user intent and interaction patterns.

Digital Experience Monitoring (DEM). Evolution of DEM tools from availability monitoring to LLM insights.

The Transformative Power of Conversational AI: Enhancing Customer Interaction and Operational Efficiency

LLAMA 3.1 vs Mistral Large 2: Comparing the Latest Models from Meta and Mistral, technical overview, best use cases and differences.

Understanding LLM Analytics: The Future of Data Analysis and AI Performance

The top real-world LLM use cases in 2024.

AI Customer Experience with examples and best practices

Introducing a new, user-centric framework for evaluating and developing LLM-based products.

Measure and Improve Your Return on Investment (ROI) with LLM-powered Internal Productivity Chatbots

How advanced Chatbot Analytics can transform businesses by providing deep insights, real-time data, and personalized customer interactions.

AI Sentiment Analysis: Unveiling User Emotions and Business Potential

Conversational Analytics what it is and How AI and LLMs are changing the game.

Exploring the methodologies of LLM evaluation, splitting everything between two primary categories: LLM-as-a-Judge and Human-as-a-Judge.

Learn about Explicit and Implicit LLM user feedback and understand the pros and cons of both methods.

Written by Nebuly and Marina Ashurkina, author of the book “Transforming Conversational AI”