Every business, big or small, has a wealth of valuable data that can inform impactful decisions. But to extract insights, there’s usually a good deal of manual work that needs to be done on raw data, either by semitechnical users (such as founders and product leaders), or dedicated – and expensive – data specialists.
Either way, to produce real value, information has to be collected, shepherded, altered, and drawn from dozens of spreadsheets and different business platforms: the organisation’s CRM, its martech stack, e-commerce system, and website data, to name a few common examples. Clearly, that’s a time consuming process, and the outcomes can be old news, rather than up-to-the-minute insights.
Introducing vibe analytics
The ideal business solution would be querying real-time data using natural language (vs writing code in SQL or Python), with smart systems working in the background to correlate and parse different data sources and formats. This is vibe analysis, where users can simply ask questions in plain language and let AI do the heavy lifting. Instead of manual data-wrestling and business users spending hours uncovering insights hidden deep in datasets, they get results fast — in text, graphics, summaries, and, where needed, detailed breakdowns.
Fast and accurate data analysis is important to every organisation, but for many, real-time insights are crucial. In the agricultural sector, for example, Lumo uses Fabi.ai’s platform to manage large fleets of IoT devices, collecting telemetry data continuously and adjusting its systems based on collated, normalised, and parsed information.
Using vibe analysis, Lumo sees device performance immediately, as well as trends that develop over time. It pulls in weather data, and correlates the device fleet’s performance metrics with environmental factors. The data dashboards Lumo has built are not the result of many months of work writing data integration routines and front-end coding, but are a result of vibe analysis.
Getting under the hood
Sceptics of AI’s abilities often point to vibe-coding as an example of where things can go wrong, raising concerns about quality control and the “black box” nature of AI-driven analysis. Many users want visibility into how results are generated, with the option to inspect logic, tweak queries, or adjust API calls to ensure accuracy. When done well, vibe analytics addresses these concerns by combining transparency with rigour. Natural language inputs and modular build methods make it accessible to semitechnical users (such as founders and product leaders), while the underlying systems meet the accuracy and reliability standards expected by technical teams. This means users can trust the output whether they’re working independently or in collaboration with data scientists and developers.
Designed specifically for both data experts and semitechnical data users, Fabi is a generative BI platform that brings vibe analysis done right to life. The code it produces can be hidden away entirely, or shown verbatim and edited in place, giving semitechnical users a chance to understand how the analysis works under the hood, while allowing technical teams to verify and fine-tune the system’s output. Data flows from an organisation’s systems (the platform mediates connections) or is uploaded. The resultant actionable insights can be pushed/scheduled to email, slack, google sheets, displayed in graphics, text, or a mixture of both.
Fabi: A generative BI platform
Co-founder and CEO of Fabi, Marc Dupuis, describes how many organisations start using the analysis platform by testing workflows and queries on sample data before progressing to real-world analysis. As users delve into data troves and test their work, they can check its veracity, often in collaboration with someone more technically astute, thanks to the platform’s open, transparent view of Smartbooks to show what’s happening under the hood. It works the other way, too: semitechnical data users can confirm that the data being processed is relevant and accurate.
To address common concerns about quality control and “black-box” AI, Fabi limits vibe analysis to internally controlled, carefully accessed data sources, with built-in guardrails. Code can be shown verbatim and edited in place, giving semitechnical users visibility into how results are produced, while allowing technical teams to audit, verify, and fine-tune outputs. Collaborative sharing of reports, findings, and working code helps teams validate results without working outside their areas of expertise.
Typical workflows include real-time KPI dashboards; natural-language Q&A over operational and product data; correlation analyses (for example, device performance against weather conditions); cohort and trend exploration; A/B test readouts and experiment summaries; and scheduled, shareable reports that mix text, graphics, summaries, and detailed breakdowns. These collaborative workflows are designed to be efficient and intuitive, so, whether working collectively or solo, users can unlock insights from even the most complex data arrangements.
Fabi landed its first round of backing from Eniac Ventures in 2023, so it’s a company on the move. The team continues to expand its capabilities, with plans to make vibe analysis even more seamless for both semitechnical and technical users. Organisations interested in exploring the platform can start by testing workflows on sample data, then scale up to real-world use cases as they grow more confident in the system’s transparency and accuracy.
(Photo by Alina Grubnyak)
See also: Generative AI trends 2025: LLMs, data scaling & enterprise adoption
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