Humaxa

The PDF isn't the problem. The workflow is. Imagine you're developing the next generation of electric vehicles. Over the past year, your company has invested in #marketresearch covering everything from autonomous driving and battery range to in-car technology and driver preferences. The research exists. The insights are there. But they're scattered across data sets and reports, making it difficult to find the answer to a simple business question when time matters. Now imagine your product team asks: "What features are women under 40 asking for most, and how has that changed compared to last year's research?" Traditionally, an analyst would need to search through multiple reports or reprogram a tabulation tool, then compare findings manually before presenting an answer. It's a process that takes time even though the information already exists. AI changes that workflow. Instead of searching document and enormous data sets,teams can simply ask the question. Within seconds, the AI compares relevant research studies, whether coordinated over months or instantly populated with modern tools, summarizes the findings, and presents the supporting charts. The research hasn't changed. The way people interact with it has. That's why #ArtificialIntelligence is becoming one of the biggest opportunities in #BusinessIntelligence and #DecisionIntelligence. When every product manager, marketer, engineer, or executive has instant access to trusted Customer Insights, organizations spend less time searching and more time making better decisions. For industries like automotive—where product cycles are measured in years and decisions involve billions of dollars—that shift can become a significant competitive advantage. The future of Market Research isn't creating more reports. It's making every insight instantly accessible. What do you think changes first over the next five years—how we collect research, or how we interact with it? #EnterpriseAI #ProductManagement #AutomotiveIndustry #Innovation

When we read the announcement regarding “Top 50 Thought Leading Companies on AI for 2026,” we didn't expect to see smaller companies. We probably expected to see “the big guys” like Anthropic, OpenAI, Google, etc. But the truth is, smaller, #VerticalAI companies are making huge strides at the #ApplicationLayer. We've heard it said that in earlier years, foundation models attracted all the attention. Then it was AI infrastructure. Perhaps this year is the year of the application layer? Regardless, we at Humaxa continue to share what we’ve learned in our AI adventure: What we’ve learned about #benchmarking, what we’ve learned about vertical vs horizontal AI, what we’ve learned about approaching 100% accuracy, what we’ve learned about measuring impact, and what we’ve learned about #ROI. And we are VERY grateful to have been included on the Top 50 Thought Leading Companies on AI for 2026. Thank you Thinkers360 and Lisa Thee!

Growing #MarketResearch Business with the Help of AI  In the time it takes most teams to clean a single survey export, competitors are already using #AI to pivot their product roadmap. In industries like #automotive and tech, speed isn’t a luxury anymore—it’s the barrier to entry. The real bottleneck isn’t a lack of data. It’s the time analysts spend fixing broken variables and rebuilding crosstabs. AI isn’t just changing how research gets done—it’s changing how research businesses grow and how clients extract value from them. The biggest shift?  Market research is becoming a real-time, decision-driving function. What this means for both sides: If you’re a market research supplier:  AI increases your capacity without linear headcount growth. You can deliver faster insights, take on more projects, and expand the scope of what you offer—without sacrificing quality.  #AIandmarketresearch If you’re a buyer of research (brand, product, insights team):  You’re no longer limited to static reports. You can explore many data sources simultaneously, ask follow-up questions instantly, without wasting time reprogramming traditional tabular tools. This allows you to make decisions while the opportunity is still there.  Where AI is driving shared value: Uncovering hard-to-find insights: Surfacing patterns that were previously too complex or time-consuming to detect—leading to stronger recommendations and better decisions.   Connecting disconnected #data: Combining survey, behavioral, and transactional data into a single view—something that used to be costly, slow, or simply not done.  The result:  Suppliers become strategic partners.  Buyers get faster, more confident decisions.  That’s how growth happens on both sides. How are you seeing AI change the relationship between research providers and clients? #MarketResearch #AI #ConsumerInsights #BusinessGrowth #DataAnalytics #Innovation #Humaxa #automotivemarketing #howtouseAIformarketresearch

It's amazing how much work has changed since AI has entered the scene. #AI as a #forcemultiplier - now that's something Humaxa can get behind. But #Security is paramount. Can the two live side by side? Let's talk.

The $5 Million #Fault That Never Happened The Setup — Everything Looked Normal It was mid-way through #ThermalValidation on a next-generation hybrid SUV. The #engineering team had just completed over 200 hours of endurance #testing on the cooling system — all the performance curves looked within spec. Pressure... Temperature... Flow… Everything was “green to go.” Buried deep in the raw #sensor logs, however, there was a subtle pattern: a tiny 1.5°C drift in coolant temperature that appeared every few hours during part-load operation. It was so small it passed all acceptance limits and wasn’t flagged in the standard summary reports at all. The Challenge — Data Too Deep to See At this point, the project had already logged over 4 terabytes of test data from dynamometers, vehicle sensors, and CAN traces. The validation team’s job was to review performance summaries — not analyze terabytes of waveform noise. The problem? That tiny anomaly pattern was the early fingerprint of a latent pump control instability. Left undetected, it could lead to thermal fatigue in a $400 aluminum housing after several thousand field hours. But nobody knew yet. The Turning Point — Letting the AI Dive Deeper The team decided to run an AI platform – specifically tuned to analyze their data – and ran it on the entire dataset. This went well beyond consolidated reports. The AI’s deep anomaly detection engine compared thousands of historical tests, recognizing micro-correlations between coolant flow ripple, PWM duty cycles, and under-hood temperature harmonics. Within hours, it flagged a previously unseen pattern: “Potential emerging harmonic between pump control signal and pressure wave frequency — matches 2019 failure signature.” It even connected the signal behavior to an old supplier issue from a different powertrain platform. The Discovery — The Hidden Instability More here: https://lnkd.in/gsahiqyi

Join Matchbook AI, Winich Inc, Staple AI, NVISIONx, WEBOOKYOUFLY LTD., Gesture, and us 😊 for an inside look at what's coming next for a select group of Expert Dojo's portfolio companies. Can't wait!

#Data Overload to Design Breakthrough The Challenge: Drowning in Data You know the feeling. I’ve felt it too. It feels like looking for a needle in a haystack… What am I looking for? That one test result that allows me to put my finger on WHY these particular sets of tests are failing. In this case, a #QA #Engineer at a major #automotive manufacturer faced an invisible bottleneck. Every week, hundreds of gigabytes of test data poured in — from wind tunnels, coastdowns, NVH rigs, thermal chambers, and durability tracks. Yet despite all that data, insight lagged behind schedule. Engineers spent hours sorting, labeling, and manually comparing datasets — hoping to catch #correlations or failure precursors before they caused delays. The result? Test cycles stretched. Launch dates slipped. And costly physical retests became the norm. The Turning Point: Asking “What If AI Could See What We Can’t?” Leadership realized their testing data was gold — but only if they could mine it... More here: https://lnkd.in/ge6-Brkm

Is a customized AI + #ChatGPT better than ChatGPT alone? ChatGPT is a powerful general-purpose #LanguageModel, capable of answering questions, generating content, and holding intelligent conversations across a wide range of topics. But while its versatility makes it valuable for many applications, organizations often find that its one-size-fits-all nature limits its usefulness for specialized tasks. That’s where a customized AI solution — trained on your #data, #workflows, and goals — adds serious value. A customized AI built on top of ChatGPT can be tailored to understand your unique language, industry terminology, and data sources. For example, in the automotive industry, a customized AI might know your engineering part codes, regulatory acronyms, or testing procedures — things that generic ChatGPT wouldn’t recognize or interpret properly. With the right integrations, it can also access your documents, databases, and #APIs in real time, allowing it to generate insights grounded in your actual operations. This combination becomes even more powerful when you embed business logic, compliance rules, or decision-making frameworks directly into the AI. While ChatGPT alone might generate a plausible-sounding answer, a customized version can vet that answer against your policies, product constraints, or documentation before presenting it. The result is faster, more reliable output you can trust in production environments. Using ChatGPT alone can also raise concerns around data privacy and control. Customized solutions give you the option to host your AI environment securely, restrict sensitive data access, and log interactions for auditing or compliance. For industries like healthcare, finance, or automotive, this kind of oversight is often essential. In short, ChatGPT is a great foundation — but for organizations that need accuracy, context, and control, combining it with a customized AI layer is the smarter approach. More here: https://lnkd.in/gUQBZDY8