# QuadSci - Complete Content for LLM Crawlers # https://quadsci.ai # Last updated: 2025-12-11 ================================================================================ COMPANY OVERVIEW ================================================================================ QuadSci is an AI-powered revenue acceleration platform that transforms raw product telemetry into predictive intelligence for Go-To-Market (GTM) teams. Our mission is to help B2B SaaS companies understand their customers through actual product usage data, not relationship proxies. ## The Problem We Solve Traditional customer success and revenue operations rely on: - Health scores based on survey responses and relationship strength - Lagging indicators that surface problems too late - Subjective assessments from account managers - CRM data that captures conversations, not behavior This approach misses 80% of the signal. Product telemetry—how customers actually use your software—is the leading indicator of retention and growth. ## Our Solution QuadSci ingests raw product telemetry to predict with precision, 12 months in advance, which customers will churn and where revenue is actually growing. Our AI platform: 1. Ingests billions of telemetry signals from your product 2. Applies quantitative machine learning models trained on your data 3. Delivers predictions directly into your GTM applications 4. Enables your teams to act before problems materialize ================================================================================ PRODUCTS ================================================================================ ## Growth AI Growth AI is our predictive intelligence engine for churn prevention and expansion identification. ### Capabilities - **Churn Prediction**: Identify at-risk accounts 12 months before renewal with 94% accuracy - **Growth Detection**: Uncover expansion opportunities with 90% accuracy - **Behavioral Analysis**: Understand which product behaviors indicate health or risk - **Early Warning System**: Get alerts on engagement drops, feature abandonment, and usage pattern changes ### How It Works 1. Connect your product telemetry (event streams, usage data, feature adoption metrics) 2. Our ML models train on your historical data and outcomes 3. Receive predictions ranked by confidence and impact 4. Integrate insights into Salesforce, Slack, or your existing workflows ### Business Impact - Reduce surprise churn by identifying risk quarters in advance - Focus CS resources on accounts that need intervention - Prioritize expansion conversations with growth-signaling accounts - Provide product teams with behavioral feedback loops ## Cohorts AI Cohorts AI automates customer segmentation using machine learning. ### Capabilities - **Automatic Segmentation**: Discover meaningful customer groups without manual rules - **Journey Mapping**: Track cohort progression through product adoption stages - **Behavioral Clustering**: Group customers by usage patterns, not demographics - **Trend Detection**: Identify emerging cohort behaviors before they become problems ### How It Works 1. Our models analyze product usage across your entire customer base 2. Cohorts emerge based on behavioral similarities 3. Track cohort performance over time 4. Identify which cohorts are at risk or primed for growth ### Business Impact - Move beyond simple ARR tiers to behavioral segments - Tailor playbooks to cohort-specific needs - Identify best-fit customer profiles for acquisition - Understand feature adoption patterns by segment ================================================================================ KEY DIFFERENTIATORS ================================================================================ ## 1. Product Telemetry First Our intelligence is based on your customer behavior. Not surveys. Not relationship strength scores. Actual product usage. We ingest: - Feature usage events - Session data - API calls - Error logs - Adoption metrics - Any event stream from your product This approach captures 80% more signal than traditional health scores. ## 2. Data Ownership Your data never leaves your environment. QuadSci products are deployed directly in your infrastructure and data warehouse. - No data egress to third-party clouds - Full compliance with your security requirements - SOC 2 aligned security practices - GDPR compliant architecture ## 3. Action from Prediction Predictions are only valuable if your teams can act on them. We integrate directly into: - Salesforce - Slack - Your existing CS platforms - Custom webhook integrations Intelligence flows to where decisions happen. ================================================================================ INTEGRATIONS ================================================================================ QuadSci integrates with 50+ enterprise tools: ### CRM Systems - Salesforce - HubSpot ### Product Analytics - Segment - Mixpanel - Amplitude - Heap - Pendo ### Observability & Infrastructure - Datadog - New Relic - Splunk - Dynatrace - Elastic - Grafana ### Customer Success - Gainsight - Zendesk ### Data & Analytics - Tableau - Snowflake (data warehouse integration) ### Communication - Slack - Marketo ================================================================================ SECURITY & COMPLIANCE ================================================================================ ## Self-Hosted Deployment QuadSci products are deployed directly in your environment. Your data never leaves your infrastructure. ## Security Practices - SOC 2 aligned security controls - Encryption at rest and in transit - Role-based access control - Regular vulnerability assessments - Penetration testing ## Privacy - GDPR compliant - No collection of end-user personal data - Customer-controlled data retention ================================================================================ FREQUENTLY ASKED QUESTIONS ================================================================================ Q: What kind of data does QuadSci need? A: We primarily use product telemetry—event streams, usage data, feature adoption metrics. We can also integrate CRM data to enrich predictions. Q: How long does implementation take? A: Typical deployments take 4-8 weeks depending on data complexity and integration requirements. Q: What accuracy can we expect? A: Customer results vary, but our flagship deployment with Clari achieved 94% churn prediction accuracy 12 months in advance. Q: Does our data leave our environment? A: No. QuadSci is self-hosted in your infrastructure. We don't collect or store your customer data. Q: What integrations do you support? A: We integrate with 50+ tools including Salesforce, Segment, Mixpanel, Datadog, and more. ================================================================================ COMPLETE BLOG ARTICLES ================================================================================ -------------------------------------------------------------------------------- ARTICLE: How Clari Used AI to Predict Churn and Uncover Growth Opportunities URL: https://quadsci.ai/blog/clari-customer-story Date: November 18, 2025 Category: Customer Story -------------------------------------------------------------------------------- A conversation with Kevin Knieriem, President of Clari, on the Sound Bites Podcast with Bill Binch When Clari set out to tackle customer churn, they didn't expect to also uncover new avenues for growth. In a recent episode of Sound Bites with Battery Ventures' Bill Binch, Clari President Kevin Knieriem shared how his team, working with QuadSci, used AI and telemetry data to predict churn with unprecedented accuracy—and in the process, revealed hidden growth signals. THE CHALLENGE: SEEING BEYOND TRADITIONAL HEALTH SCORES Every SaaS business wrestles with customer retention. Traditional tools like health scores and relationship trackers can only show a snapshot in time and often surface false positives. Clari wanted to go deeper, identifying leading indicators that could forecast customer contraction or churn up to a year in advance. "We wanted to get ahead of it. Not just a quarter out—but four or eight quarters ahead." — Kevin Knieriem THE SOLUTION: PARTNERING WITH QUADSCI FOR PREDICTIVE INSIGHTS To make that vision real, Clari partnered with QuadSci, which ingested more than 6 billion telemetry signals from Clari's post-sale systems, customer success data, contracts, and usage analytics. QuadSci built a custom data science model that could detect subtle behavioral shifts in how customers used Clari's platform. From there, Clari integrated those predictive signals directly into its own systems and blended them seamlessly into its renewal and account management workflows. "It gave our teams relevant, forward-looking data. Signals that showed us where to focus." — Kevin Knieriem THE OUTCOMES: ACCURACY, GROWTH, AND TIME TO ACT The results were transformative. The model was: - 94% accurate in predicting churn 12 months in advance - 90% accurate in predicting growth opportunities - Able to pinpoint 80% of contractions six months ahead More, some of the most valuable insights were counterintuitive. For example, a drop in support cases sometimes indicated customer disengagement, a pattern that traditional metrics would have missed. "While we were looking for potential churn, we actually found growth signals. And that was just as important." — Kevin Knieriem These insights gave Clari's customer success and account management teams time to act, folding the intelligence into their 13-week business cadence. With constantly refreshed data, they could stay ahead of risk and capitalize on emerging opportunities. THE BROADER IMPACT: PRODUCT AND PROCESS INTELLIGENCE Beyond retention, Clari's product team gained valuable feedback loops. The model highlighted which workflows performed well and which needed refinement, informing product improvements and future roadmap priorities. "It's been a great partnership. One that we're going to continue." — Kevin Knieriem THE POWER OF THE PARTNERSHIP This collaboration between Clari and QuadSci demonstrates what's possible when AI-driven predictive modeling meets operational execution. By connecting the dots across billions of data points, Clari not only reduced churn risk but also unlocked a new lens for growth, innovation, and customer understanding. -------------------------------------------------------------------------------- ARTICLE: The CFO and the Renewal Decision URL: https://quadsci.ai/blog/cfo-renewal-decision Date: December 11, 2025 Category: Insight -------------------------------------------------------------------------------- How Telemetry and AI help you tell the value story to the CFO A renewal is about more than your champion. Increasingly, the CFO is playing a central role in renewal decisions as their role expands in many organizations. This new scope for finance creates challenges for both internal champions and external service providers come renewal time. To best position themselves at renewals, it's critical account teams and their revenue leaders have a clear view of product adoption, usage and value to the client throughout the course of a contract. It's no longer enough to rely on the strength of the relationship with a team, accounts teams need to come armed with numbers and a clear value story. TL;DR: • CFOs are stewards that need to see and understand organizational value at renewal time • It's critical CS teams provide the information their internal champions need to make the case • AI unlocks the value story for internal champions and their external support • Data creates transparency and durable investments the CFO trusts HOW DOES AI CLARIFY THE VALUE STORY? Telemetry data tells a story about what's happening on a piece of software. But on its own, raw telemetry is a fragment of the value story. It doesn't explain why the customer is seeing results, where value is being created, or how the outcomes connect to business goals. AI changes that. It turns scattered signals into a coherent story. AI analyzes telemetry data alongside broader behavioral and organizational patterns and marries them to customer signals like CRM data. It identifies meaningful trends, contextualizes them, and highlights the connections that matter most during a renewal conversation. Instead of a list of metrics, it produces a narrative that explains: → which activities are driving measurable outcomes → where adoption is accelerating or flattening → which use cases have the strongest operational impact → how the customer compares to relevant benchmarks → where the product can deliver additional value going forward AI transforms raw data into a value story that internal champions, CFOs, and CROs can all align around. This shift from "here are the numbers" to "here's what the numbers mean" is what makes renewal conversations clearer, more factual, and more collaborative. HOW TO BUILD A COMPELLING RENEWAL NARRATIVE? AI gives CSMs the tools to translate usage into business impact for their champions. That story is a critical component in CFO's understanding of the investment value. Armed with objective data, champions can highlight where value is being created, where adoption is expanding, and where deeper engagement would drive stronger outcomes. For instance, Cohorts AI maps the historical usage of a product across a variety of features for all users. First, the view of usage indicates the value the product is delivering to the entire team. Then, through analysis of telemetry data, you can unpack how the team is using the product and what they are using it to do. For example, if support tickets dipped over two quarters, understanding why is valuable information that speaks to product engagement in an account. Conversely, if support tickets increased across multiple users, it demonstrates to the CFO that the team is engaged and using the product. With this sort of objective information, the discussion moves away from price defense and toward shared goals. When both sides look at the same data, it creates transparency and trust, leading to more productive and balanced conversations with CFOs who want to make smart, durable investments. -------------------------------------------------------------------------------- ARTICLE: Why is Growth Slowing Down? The Revenue Reality for B2B Software URL: https://quadsci.ai/blog/revenue-reality-b2b Date: December 10, 2025 Category: Insight -------------------------------------------------------------------------------- Understanding why traditional approaches fail and how telemetry-driven intelligence changes the equation For years, revenue teams have tried to grow by optimizing workflows, tightening processes, and creating health scores. Informing those actions is machine-learning intelligence based on human data to try and stave off churn and stabilize their revenue. The results speak for themselves. According to SBI, 58.8% of B2B organizations saw NRR decline over the last two years. Business leaders continue to explore the data their teams generate in the hopes it will lead to better decisions, better bets and better revenue. But, the truth isn't in what people say, it's in what they do. To spur growth, you need to understand behavior and that lives in telemetry. WHAT'S THE PROBLEM WITH HUMAN-GENERATED DATA? Most companies and market solutions rely on subjective inputs: CRM notes, NPS scores, reactive health indicators, or the "feel" of the relationship. The challenge with human-generated data is that it paints only a partial picture of account health, is influenced by emotion and often arrives too late for teams to act. The consequence is surprise churn, which is preventable but nearly impossible to see with traditional systems. In one recent example from a customer, their account lead told them a customer appeared happy, the relationship was strong, and they forecasted a safe renewal. Telemetry told a different story: no product expansion, declining usage, stagnating adoption. Thirty days before renewal, they churned. The financial and operational shockwaves were immediate. The reality is, no matter how strong the relationship, the CS leader or the signals from CRM, you're placing bets on unreliable data. WHAT'S THE ROLE OF TELEMETRY IN PREDICTIVE INTELLIGENCE? Telemetry changes the visibility equation by grounding predictions in objective customer behavior on a product. It can provide 80% of the signal that human-generated data can't because it reveals how people actually interact with software. Not on an account basis but across an entire customer base during their customer journey. WHY DOESN'T EVERYONE USE TELEMETRY DATA? The reality is that telemetry is not text and it's a lot of data. So a LLM can't understand what it is looking at and CRMs can't ingest it because it doesn't map to fields. More importantly, marrying telemetry to CRM data takes experience and expertise and you still have to translate what you find into comprehensible intelligence for GTM teams. WHAT IS THE ROLE OF FORWARD DEPLOYED ENGINEERS? Forward deployed engineers translate telemetry into explainable models tailored to each customer's business context. They help GTM leaders understand: Why an account is failing to adopt, What behaviors separate growing customers from stagnant ones and Which actions will change forecast outcomes. This helps CROs set strategy, CPOs sharpen product and CMOs enrich customer journeys. WHAT MAKES QUADSCI DIFFERENT? QuadSci ingests billions of product telemetry signals, far deeper and broader than the CRM- or workflow-based data sources of other market solutions. We activate forward deployed engineers to make sense of your telemetry and translate it into predictive intelligence that reduces churn and unlocks growth — 12-month predictive accuracy up to 94%. This agentic guidance built directly on usage data gives teams the time and intelligence they need to actually affect change with their customers. -------------------------------------------------------------------------------- ARTICLE: Surprise Churn Hits Hard. The Impact Starts Fast URL: https://quadsci.ai/blog/surprise-churn Date: November 21, 2025 Category: Insight -------------------------------------------------------------------------------- How predictive AI powered by telemetry data transforms forecast accuracy and prevents unexpected churn. Forecast accuracy sits at the center of every revenue organization. It shapes investment decisions and board conversations. It influences hiring plans, operating budgets, and overall company health. When a forecast holds steady, the entire business feels more confident. When it collapses, everything becomes harder. This is why surprise churn has such a devastating effect on revenue plans. It does not just remove dollars from the renewal column. It removes stability from the entire operating rhythm of the company. Surprise churn happens when a customer that appears healthy informs the team that they will not renew. This often comes late in the cycle and with very little warning. Even strong customer relationships can shift quickly when usage declines or internal business priorities change. When value is unclear, the risk increases sharply, even if a relationship feels strong. WHAT DOES SURPRISE CHURN DO TO AN ORGANIZATION? The revenue impact is immediate. A single unexpected renewal loss forces revenue teams to scramble. Forecasts must be rebuilt. Pipeline must be rebalanced. New business targets suddenly feel unreachable. The board begins asking harder questions about predictability. The CRO has to shift from steering strategy to explaining what went wrong. The operational cost of one surprise renewal can cascade for months. "When you miss the number, it's not just about explaining it to the board. It's about the sales reps you can't hire, the territories you can't expand, and the strategic bets you can't make because you didn't see it coming." — Kevin Knieriem, President of Clari A recent internal example illustrates this clearly. For more than a year, QuadSci's AIs revealed signals that an enterprise account was drifting away. Usage had not grown. Executive engagement had faded. Buying committees were quiet. Product adoption patterns never crossed the thresholds that indicate a healthy renewal. Yet the internal relationship appeared positive. The customer was friendly. Calls were pleasant. The team believed the renewal was safe. Thirty days before the renewal, the customer chose not to renew. The impact is on both sides of the deal. A renewal is a financial and operational decision. When value is not clear inside the customer organization, or when the product is not deeply embedded, a renewal becomes difficult to justify, even with strong personal relationships. No CFO wants to cut useful tools. They simply need to support decisions that reflect usage, adoption, and business outcomes. When the champion can't tell that story or demonstrate the value, they suffer too. AI AGENTS POWERED BY TELEMETRY DATA The important learning here is not that anything was done incorrectly. The lesson is that people are limited by what they can see. Humans interpret signals through the lens of relationships, emotions, and the most recent interactions. AI sees something different. It sees patterns of behavior that unfold over years, marries that to usage trends and CRM data to paint an objective picture of each account. The result is more nuanced and more accurate than a health score. AI reveals when usage is shallow or when a champion has lost influence. It reveals when a buying committee has disengaged long before the team becomes aware of the shift. This is where predictive AI changes everything. It provides clear visibility months before risk signals appear in dashboards, on calls or in email. Telemetry data highlights declining adoption trends, shows when an account is not expanding into new teams and surfaces early shifts in executive engagement. Leveraging telemetry early and often gives revenue leaders time to understand what is happening and to respond in a thoughtful way. Forecast accuracy improves when the organization moves from reaction to foresight. A CRO can only steer the business when they have a clear view of the road ahead. Predictive telemetry makes that possible by turning uncertainty into insight. It replaces surprise with understanding. It creates time and space for thoughtful conversations with customers. It brings stability back to the revenue engine. -------------------------------------------------------------------------------- ARTICLE: Telemetry Over Talk: How AI Predicts Churn and Unlocks Growth URL: https://quadsci.ai/blog/telemetry-over-talk Date: October 29, 2025 Category: Research & Product -------------------------------------------------------------------------------- Analysis of 9,100 SaaS accounts reveals 80% of commercial outcomes are predicted by product usage, not CRM sentiment It's a familiar scenario: the quarter is closing, leadership wants a clear picture of revenue, and it's time to scrutinize the pipeline. What happens next is a mix of spreadsheets, long meetings, anecdotes, and messy CRM data — all with the goal of providing clarity for leadership, investors, and advisory boards. For years, this was the only way to piece together a picture of revenue health. But there's a canyon between a "great meeting" and the CFO's renewal forecast, between optimistic health scores and actual product usage, and between a champion's enthusiasm and their ability to articulate value to leadership. Today, buying decisions are centralized and often removed from daily product experience. If companies want to retain and grow accounts, they need to arm their champions with usage data that clearly connects product behavior to business value. THE DATA BEHIND THE STRATEGY That's the foundation of the new study from SBI, The Growth Advisory, in partnership with QuadSci. The analysis of 9,100 SaaS accounts and 160 billion telemetry data points found that 80% of commercial outcomes are predicted by product usage. Not customer stories. Not gut feel. Product usage. The strongest indicator of retention and growth isn't what your CRM says — it's how your customers behave. Across the industry, few teams are equipped to capitalize on that data to align CSMs and account executives around the same customer reality. The result: missed forecasts and preventable churn. GUT CHECK Most forecasts are stitched together from anecdotes and lagging indicators. That makes them reactive, not predictive. By the time pipeline health looks off or renewals soften, it's already too late to course-correct. Retention has quietly become the defining metric for SaaS valuation, yet many teams still can't explain why some customers expand while others churn. "Growth doesn't hinge on luck or loyalty. It hinges on understanding signals." — Michael Hoffman, SBI Growth Advisory FROM INTUITION TO AI-DRIVEN INTELLIGENCE Declining NRR isn't a sales problem. It's a data problem. Traditional forecasting frameworks look at revenue through a static lens — past performance, renewal history, or self-reported health scores — while customer reality shifts daily. That's where QuadSci's AI-driven revenue intelligence changes the equation. By turning product usage, engagement, and sentiment data into actionable predictions, QuadSci enables GTM teams to detect early warning signs and growth opportunities months in advance. 90% ACCURACY, 12 MONTHS IN ADVANCE In our work with enterprise SaaS companies, QuadSci predicts churn or expansion up to 12 months in advance with 90% accuracy. We run blind tests on customer data, training our Growth AI agent on 80% of telemetry while holding back 20% for validation, ensuring consistent predictive performance over time. The result is a clear, early view at the account level — identifying churn risk and expansion potential based on actual usage patterns across every user. GTM teams can re-engage at-risk accounts before it's too late and guide healthy customers toward the features that will create new value and expansion in the future. -------------------------------------------------------------------------------- ARTICLE: Reflections from BayLearn 2025: A Day of Machine Learning Insights and Inspiration URL: https://quadsci.ai/blog/baylearn-2025 Date: November 4, 2025 Category: Culture & Product Author: Madhuri Pujari, DataML Engineer -------------------------------------------------------------------------------- Exploring the Future of AI with Leading Minds in the Field I had the chance to attend BayLearn - Machine Learning Symposium, hosted at Santa Clara University on Oct 16th. My work at QuadSci as a DataML Engineer often focuses on core ML techniques and LLMs, so seeing how those foundations connect to new frontiers in AI was both grounding and inspiring. A DAY PACKED WITH IDEAS From the very start, the energy inside the Locatelli Center at Santa Clara University was contagious - researchers, practitioners, and students all buzzing with ideas. It was inspiring to see how many perspectives and applications of machine learning came together in one place. A few sessions stood out to me in particular: KEYNOTE #1 – Christopher Manning (Stanford): "The Surprising Victory of NLP: From History and Philosophy to Universal Tools" Manning's talk was both humbling and insightful. He traced the journey from symbolic AI to modern transformers, showing how decades of linguistic and philosophical work laid the foundation for today's breakthroughs. It reminded me how critical strong fundamentals are, the kind we rely on every day at QuadSci when building interpretable, data-driven ML systems. Even as AI grows more complex, success still depends on clarity of data, models, and reasoning. On a personal note, Christopher Manning's lectures were a big part of my learning journey during my master's back in the pre-ChatGPT era. His videos were my go-to whenever I needed clarity, and I must've replayed them countless times. It's incredible to see how much has changed since then. Today, AI itself can act as a teacher, answering questions endlessly and helping people learn at their own pace. KEYNOTE #2 – Bryan Catanzaro (NVIDIA): "Nemotron: Building an Open and Accelerated Future" This was an inspiring look at the open ecosystem NVIDIA is enabling for large model development, blending open-source collaboration with scalable compute. It highlighted how openness and accessibility are becoming central to innovation in AI, something that resonates with me as we think about scalable, transparent ML systems at QuadSci. It also raised an interesting question: how can smaller teams like ours adopt that same spirit of openness to experiment faster and collaborate more effectively within our projects? PANEL DISCUSSION – Agentic AI A lively session featuring voices from Google DeepMind, NVIDIA, and Stanford, exploring how "agentic systems" are changing the way we think about autonomy and human-AI interaction. There was also discussion around the path to AGI framed less as speculation and more as a gradual shift toward systems that can reason, plan, and act with increasing independence. Several speakers highlighted the practical challenges of deploying such systems in enterprise environments, especially around security, oversight, and trust. Fully autonomous workflows introduce new risks, yet restricting them too tightly can limit innovation, the kind we thrive on. The consensus was clear: progress will depend on finding the right balance between openness and control and recognizing that the right level of restriction depends on the use case. The kind of AI solution being built should determine how much autonomy, monitoring, or safeguard it truly needs. The takeaway for me was that the next frontier isn't just smarter AI, but responsible autonomy designing systems that can act, reason, and adapt while staying aligned with human and organizational goals. EVOLVING FOUNDATIONS: HOW LLMS ARE STRENGTHENING TRADITIONAL ML While much of the buzz centered around LLMs and agentic systems, core ML techniques continue to underpin most real-world AI applications. What's changing now is how LLMs are beginning to enhance these existing workflows rather than replace them. I saw several discussions and demos showing how LLMs can act as signal amplifiers improving data quality in preprocessing (through text understanding, labeling, and feature extraction) and even helping evaluate or interpret outputs after model inference. The idea of "LLM as a judge" where a large model assesses or refines another model's output came up in multiple sessions and is already showing early promise. As for agentic systems, the vision of fully autonomous workflows is exciting but still early. The technical, ethical, and security challenges discussed at BayLearn made it clear that we're not quite ready for widespread production deployment. But the trajectory is clear: LLMs are already making traditional ML smarter, and agentic ideas are shaping what comes next. FINAL THOUGHTS BayLearn 2025 reinforced that AI's future isn't about replacing old methods with new ones, it's about layering innovation on top of solid foundations. From Manning's timeless emphasis on fundamentals to NVIDIA's call for openness and the debate around responsible autonomy, every session pointed to the same theme: progress in AI depends on finding balance between innovation and stability, scale and accountability. As I continue my journey at QuadSci, I'm excited to explore how these ideas can come together building systems that stay interpretable, adaptable, and aligned as the field continues to evolve. -------------------------------------------------------------------------------- ARTICLE: Cohorts AI Swarm - Q1 Insights URL: https://quadsci.ai/blog/cohorts-ai-swarm Date: 2025 Category: Product Insights -------------------------------------------------------------------------------- Key insights and learnings from our Q1 AI Swarm cohorts. Our Q1 AI Swarm cohorts have provided invaluable insights into how businesses are adopting and implementing AI solutions. Through collaborative learning sessions and hands-on experimentation, we've gathered critical data about AI adoption patterns, challenges, and success factors across different industries. UNDERSTANDING AI ADOPTION PATTERNS The Q1 cohorts revealed distinct patterns in how different organizations approach AI implementation. Companies that achieved the greatest success shared common characteristics: clear use case identification, strong leadership support, and a willingness to iterate based on real-world feedback. These insights are shaping how we design our solutions. COMMON CHALLENGES AND SOLUTIONS Through our cohort sessions, we identified recurring challenges that organizations face when implementing AI solutions. Data quality issues, change management resistance, and unclear ROI metrics were among the most common obstacles. Our AI Swarm methodology addresses these challenges through structured approaches and peer learning. "The AI Swarm cohorts demonstrated that success in AI implementation isn't just about technology—it's about creating an environment where teams can experiment, learn, and adapt together. The collaborative approach accelerates learning and reduces risk." INDUSTRY-SPECIFIC INSIGHTS Different industries showed varying approaches to AI adoption. SaaS companies focused heavily on customer behavior prediction, while manufacturing companies prioritized operational efficiency improvements. These sector-specific insights help us tailor our solutions to address unique industry requirements and challenges. COLLABORATIVE LEARNING BENEFITS One of the most significant discoveries from our Q1 cohorts was the power of peer learning in AI implementation. Organizations benefited enormously from sharing experiences, challenges, and solutions with others facing similar problems. This collaborative approach accelerated learning curves and improved outcomes for all participants. LOOKING AHEAD: Q2 AND BEYOND The insights from our Q1 AI Swarm cohorts are directly influencing our roadmap for Q2 and beyond. We're incorporating the lessons learned into our platform development, our customer success methodologies, and our approach to helping organizations achieve sustainable AI transformation. -------------------------------------------------------------------------------- ARTICLE: Back from CDMX: QChat, Connection, and a Growing Team URL: https://quadsci.ai/blog/back-from-cdmx Date: May 22, 2025 Category: Company Updates -------------------------------------------------------------------------------- Highlights from our Mexico City experience and team growth. Our return from Mexico City marks more than just the end of a successful summit—it represents a pivotal moment in QuadSci's evolution. The connections made, innovations shared, and team bonds strengthened during our CDMX experience continue to drive our momentum forward. QCHAT: INNOVATION IN ACTION One of the standout highlights from our CDMX summit was the demonstration and discussion around QChat, our latest innovation in AI-powered communication. The in-person collaboration allowed our team to refine and enhance this tool through real-time feedback and collective brainstorming sessions. THE POWER OF FACE-TO-FACE CONNECTION While we excel at remote collaboration, there's something irreplaceable about gathering in person. Our CDMX summit created opportunities for spontaneous conversations, deeper understanding of each team member's perspectives, and the kind of creative collaboration that can only happen when brilliant minds share the same physical space. "Mexico City gave us more than just a beautiful backdrop for our summit—it provided the perfect environment for our team to connect on a deeper level and push the boundaries of what we can achieve together. The energy and inspiration we gained will fuel our innovations for months to come." A GROWING TEAM, A STRONGER VISION Our time in CDMX also highlighted how much our team has grown—not just in numbers, but in capability, diversity of thought, and shared commitment to our mission. New team members integrated seamlessly with veterans, creating a dynamic environment where fresh perspectives enhanced our collective wisdom. MEXICO CITY'S LASTING IMPACT The choice of Mexico City as our summit location proved to be transformative. The city's vibrant tech ecosystem, rich culture, and central location for our international team created an atmosphere of innovation and possibility. Many team members have expressed how the experience has influenced their approach to their work. MOVING FORWARD WITH MOMENTUM As we continue to build on the momentum from our CDMX summit, the innovations, connections, and strategic insights gained during our time together continue to drive our progress. The experience has reinforced our belief in the power of bringing diverse minds together to solve complex challenges. -------------------------------------------------------------------------------- ARTICLE: 10 Years and Counting: What Drives Aaron Hinojosa in Tech and at QuadSci URL: https://quadsci.ai/blog/aaron-hinojosa-10-years Date: May 7, 2025 Category: Team Spotlight -------------------------------------------------------------------------------- Aaron reflects on his decade in tech and what motivates him at QuadSci. With over a decade of experience in technology, Aaron Hinojosa brings a wealth of knowledge and perspective to QuadSci. His journey through the tech industry has been marked by continuous learning, adaptation, and a passion for solving complex problems through innovative solutions. A DECADE OF TECH EVOLUTION Aaron's ten years in technology have spanned an era of incredible transformation. From the early days of cloud computing to the current AI revolution, he has been both a witness to and participant in the industry's most significant shifts. This experience gives him a unique perspective on how technology trends evolve and mature. WHAT DRIVES CONTINUOUS LEARNING One of Aaron's defining characteristics is his commitment to continuous learning. In an industry where technologies emerge and evolve rapidly, staying current requires dedication and curiosity. Aaron thrives on the challenge of mastering new tools and methodologies, always seeking to expand his technical expertise. "What keeps me passionate about tech after all these years is the constant opportunity to learn and grow. Every project presents new challenges, and at QuadSci, we're working on problems that push the boundaries of what's possible with AI and machine learning." FINDING PURPOSE AT QUADSCI Aaron's decision to join QuadSci was driven by the opportunity to work on meaningful problems that have real-world impact. After years in the industry, he was drawn to our mission of democratizing AI and making advanced machine learning accessible to businesses of all sizes. TECHNICAL EXCELLENCE AND TEAM COLLABORATION At QuadSci, Aaron appreciates the balance between technical excellence and collaborative teamwork. His experience has taught him that the best solutions emerge when talented individuals work together toward a common goal. He values the opportunity to both contribute his expertise and learn from his colleagues. LOOKING TOWARD THE FUTURE As Aaron looks toward the next decade in technology, he's excited about the potential of AI to transform industries and solve complex global challenges. At QuadSci, he sees the opportunity to be at the forefront of this transformation, building solutions that will define the future of business intelligence and automation. ================================================================================ BLOG: Humans See Relationships. AI Sees Patterns. Forecasts Need Both Published: December 16, 2025 URL: https://quadsci.ai/blog/humans-ai-forecasts ================================================================================ Sales is a combination of art and science. It rewards intuition, relationships, and the ability to read a room. These skills matter in complex B2B sales. They help people build trust and guide customers through long decisions. But forecasting is different. Executives rely on forecasts to invest in their companies growth and boards use them to assess the effectiveness of leadership. Despite all the pressure on a forecast, they are notoriously unreliable. Why? Because humans can't predict the future and when you ask them to, you're inviting wishful thinking to the table. According to Harvard Business Review, sales teams often become more susceptible to wishful thinking in pursuit of targets. ## Why Revenue Forecasting Accuracy Breaks Down Most revenue forecasts are built on human-generated data. CRM fields, deal notes, pipeline stages, and subjective health scores dominate the signal set. These inputs reflect what people believe is happening, not always what customers are actually doing. By the time problems appear in CRM, they often start months earlier. This is the core limitation of traditional forecasting. It relies too heavily on perception and too little on behavior. ## How Predictive AI Improves Forecast Accuracy Improving forecast accuracy requires expanding the signal set, not replacing human judgment. Predictive AI can analyze billions of data points across multiple systems and time horizons. When deployed correctly, it evaluates accounts objectively and consistently, without being influenced by quotas or optimism. No single data source is sufficient. Accurate revenue forecasting depends on combining multiple perspectives into a unified view. ## Why Telemetry Data Matters for Revenue Forecasts Telemetry data shows how customers actually use a product in their daily work. It captures which teams are active, which features are being adopted, how usage is trending, and whether key stakeholders continue to engage over time. These behavioral signals add depth that most CRMs cannot capture. CRM data still matters. It provides commercial structure, deal context, and relationship history. Support systems contribute another layer by revealing friction, risk, and operational strain. Each source is incomplete on its own. The breakthrough comes when predictive AI connects them. ## The Role of Predictive AI in Modern Revenue Forecasting Predictive AI analyzes telemetry data, CRM data, product events, support patterns, and other go-to-market signals together. It learns from long-term behavior across many customers and identifies patterns that correlate with expansion, contraction, or churn. Most importantly, it detects meaningful shifts months before renewal or forecast reviews. The role of AI is not to replace human judgment. It strengthens judgment by grounding decisions in objective, behavioral evidence. Without telemetry data, AI systems inherit the same human bias embedded in CRM-driven inputs. Telemetry balances that bias by showing what is actually happening at the account level. When predictive AI brings these signals together, it creates a stable foundation for revenue decisions. Forecasts become less reactive and more predictive. Teams align around shared evidence instead of competing narratives. Organizations perform best when they combine human empathy with data-driven clarity. Predictive AI that incorporates telemetry, CRM, and other GTM signals makes that balance possible. It transforms fragmented information into shared understanding and gives revenue leaders the confidence to steer the business with precision, foresight, and trust. ================================================================================ CONTACT INFORMATION ================================================================================ Website: https://quadsci.ai Email: contact@quadsci.com Book a Demo: https://quadsci.ai/contact LinkedIn: https://www.linkedin.com/company/quadsci ================================================================================ END OF FILE ================================================================================