Every organization, large or small, sits on a pile of data that could influence key choices. Turning raw records into reliable insights usually takes a lot of manual work — done either by semitechnical staff such as founders and product leaders, or by dedicated, and often costly, data specialists.
To deliver useful answers, information must be gathered, cleaned, merged, and transformed across spreadsheets and a dozen platforms: the organization’s CRM, martech tools, e-commerce system, website analytics, and more. Those steps cost time, and outputs can arrive as stale analysis rather than up-to-the-minute findings.
An attractive alternative is the ability to query live data using plain English instead of writing SQL or Python, with intelligent systems running in the background to reconcile different sources and formats. That approach is known as vibe analysis: users pose questions in conversational language and AI performs the heavy lifting.
Rather than business teams spending hours hunting through datasets, insights appear quickly — as text summaries, charts, and granular breakdowns. Faster answers can change how operations, product decisions, and customer-facing teams act on emerging signals.
The agricultural sector gives a practical example. Lumo uses Fabi.ai’s platform to manage large fleets of IoT devices, ingesting telemetry continuously and adjusting systems based on collated, normalized, and parsed information. The platform brings multiple feeds together so engineers and field teams can respond rapidly.
With vibe analysis, Lumo observes device performance in near real time and monitors trends that emerge over days or weeks. Weather data is pulled in and device metrics are correlated with environmental factors. The dashboards Lumo relies on did not require months of bespoke integration and front-end coding; they were delivered by automated analysis workflows.
Skeptics of AI often point to vibe-coding as a possible failure point, raising questions about quality control and the “black box” nature of AI-driven conclusions. Many users want a visible trail that shows how results were produced and the option to inspect logic, modify queries, or adjust API calls so outputs match operational expectations.
When built with transparency and technical rigor, vibe analytics meets those needs. Natural-language inputs and modular build methods make the tools approachable for semitechnical staff such as founders and product leaders; the underlying engines satisfy the accuracy and reliability standards technical teams require. That combination helps users trust outputs whether they work independently or together with data scientists and developers.
Fabi markets itself as a generative BI platform designed for both data experts and semitechnical users. The system can keep generated code hidden, or present it verbatim and allow in-place edits. That capability provides semitechnical users a chance to follow the logic and gives technical teams a route to audit and refine what the platform produces.
Data can stream from an organization’s systems — the platform mediates connectors — or be uploaded as files. Actionable insights then flow out in a variety of formats and destinations: email, slack, google sheets, visual dashboards, and written summaries that combine narrative with charts for easier consumption.
Co-founder and CEO of Fabi, Marc Dupuis, says many organizations begin by testing workflows and queries against sample datasets before moving to live analysis. As teams probe large data stores and validate outputs, they often work with a more technical colleague, using the platform’s open Smartbooks to reveal the transformations, SQL or scripts, and intermediate tables that led to a result.
That interaction goes both ways: semitechnical users can confirm that the data being processed is relevant and correctly scoped, while technical teams can verify and tune models and queries. To keep quality under control, Fabi restricts vibe analysis to internally managed, carefully accessed sources and adds guardrails around what the system can query and modify.
The platform’s Smartbooks act as interactive records that map the steps of a workflow. Users can trace how raw inputs were joined, what filters were applied, and what calculations produced a KPI. Technical staff can extract or edit the generated code, rerun steps in a staging environment, and feed adjustments back into the shared notebook.
Typical workflows handled by vibe analysis include real-time KPI dashboards; natural-language Q&A across operational and product datasets; correlation studies such as matching device performance with weather; cohort and trend exploration for retention or conversion analysis; A/B test readouts and experiment summaries; and scheduled, shareable reports that mix text, graphics, and detailed breakdowns. Those formats are designed for team collaboration and quicker decision cycles, allowing users to access insights hidden in complex data setups.
Fabi closed its first funding round with Eniac Ventures in 2023 and has been scaling the product and team since. The company is adding capabilities aimed at making vibe analysis smoother for both semitechnical and technical audiences. Its suggested onboarding pattern typically starts with sample workflows and ramps into production use as teams gain confidence in the platform’s transparency and accuracy.
As organizations juggle more data sources and faster timelines, platforms that combine plain-language access with clear, auditable logic are becoming a practical option for turning noisy inputs into timely, trustworthy answers.