Every company, large or small, has a wealth of valuable data to help make impactful decisions. However, extracting insights typically requires significant manual effort on the raw data, either by semi-technical users (such as founders or product leaders) or by dedicated (and expensive) data specialists.
Either way, to create real value, you need to collect, manage, modify, and extract information from dozens of spreadsheets and various business platforms (including your organization’s CRM, martech stack, e-commerce system, and website data, to name a few common examples). Obviously, this is a time-consuming process, and the result may be old news rather than the latest insights.
Introduction of vibe analysis
The ideal business solution would use smart systems running in the background to query real-time data using natural language (rather than writing code in SQL or Python), correlating and parsing different data sources and formats. This is atmospheric analysis, where users can simply ask questions in plain language and let the AI do the heavy lifting. Instead of spending hours manually wrestling with data and uncovering insights hidden deep within datasets, business users can get results quickly with text, graphics, summaries, and optionally detailed breakdowns.
Fast and accurate data analysis is important for any organization, but for many, real-time insights are key. For example, in the agriculture sector, Lumo uses Fabi.ai’s platform to manage IoT devices at scale, continuously collect telemetry data, and adjust systems based on collated, normalized, and parsed information.
Lumo uses vibe analysis to instantly see not only your device’s performance, but trends that develop over time. Capture weather data and correlate device fleet performance metrics with environmental factors. The data dashboards Lumo builds are the result of atmospheric analysis, not months of work creating data integration routines and front-end coding.
get under the hood
Skeptics of AI’s capabilities often point to vibecoding as an example of where things can go wrong, raising concerns about quality control and the “black box” nature of AI-driven analytics. Many users want visibility into how results are produced, with options to inspect the logic, fine-tune queries, and adjust API calls to ensure accuracy. When done well, vibe analysis can address these concerns by combining transparency and rigor. Natural language input and modular construction methods make it accessible to semi-technical users (such as founders and product leaders), and the underlying system meets standards of accuracy and reliability expected by technical teams. This means users can trust the output whether they are working alone or collaborating with data scientists and developers.
Fabi is a generative BI platform designed specifically for both data professionals and semi-technical data users to enable vibe analysis. The generated code can be completely hidden or viewed and edited on the fly, giving semi-technical users the opportunity to understand how the analysis works under the hood, while allowing technical teams to validate and fine-tune the system’s output. Data either flows from an organization’s systems (the platform brokers the connection) or is uploaded. The resulting actionable insights can be pushed/scheduled to email, Slack, Google Sheets, or displayed as graphics, text, or a combination of both.
Fabi: A generative BI platform
Fabi co-founder and CEO Marc Dupuis explains how many organizations start using analytics platforms by testing workflows and queries on sample data before moving on to actual analysis. As users delve into the treasure trove of data and test their work, thanks to the platform’s Smartbook open and transparent view of what’s going on under the hood, users can often collaborate with someone more technically savvy to check the veracity of the data. It also works the other way around. Users of semi-technical data can ensure that the data they are processing is relevant and accurate.
To address common concerns about quality control and “black box” AI, Fabi uses built-in guardrails to limit vibe analysis to internally controlled and carefully accessed data sources. The ability to view code as is and edit it on the fly allows semi-technical users to visualize how results are generated, while technical teams can audit, validate, and fine-tune output. By collaborating and sharing reports, findings, and actionable code, teams can validate results without working outside their areas of expertise.
Common workflows include real-time KPI dashboards. Natural language Q&A on operational and product data. Correlation analysis (e.g. device performance against weather conditions). Studying cohorts and trends. A/B test results and experiment summary. Scheduled, shareable reports that combine text, graphics, summaries, and detailed breakdowns. These collaborative workflows are designed to be efficient and intuitive, so users can extract insights from even the most complex data arrangements, whether working together or alone.
Fabi received its first backing from Eniac Ventures in 2023 and is a company on the move. The team plans to continue expanding the functionality and making vibe analysis even more seamless for both semi-technical and technical users. Organizations interested in exploring the platform can start by testing their workflows with sample data and scale up to real-world use cases once they are confident in the system’s transparency and accuracy.
(Photo courtesy of Alina Grabniak)
See also: Generative AI Trends in 2025: LLM, Data Scaling, and Enterprise Adoption
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