An AI answer you cannot verify is an opinion. Business decisions need more than opinions, so Butterflai is built around one principle: every answer must be traceable back to the data that produced it.
Answers with their sources attached
When Butterflai answers from your documents, it shows which files the answer came from. When it answers with numbers, those numbers are computed by real queries over your synced business records, not recalled from a language model’s memory. You can always ask the follow-up that matters in a real business: “where does that figure come from?”
Computed, not guessed
Butterflai separates two jobs that most AI tools blur together. Retrieving and computing facts is done by deterministic machinery: SQL queries over your analytics warehouse and semantic retrieval over your documents. The language model’s job is to plan the work and express the result. This separation is why a revenue figure from Butterflai matches your ERP, and why the same question asked twice does not produce two different totals from thin air.
Deterministic reports
Generated analyses such as the Cashflow Gap Analysis and the Business Health Check are produced by fixed analytical pipelines. The calculations are code, not improvisation: the same inputs produce the same outputs, every run. That makes these reports something you can put in front of an accountant or a board.
You always know it is AI
Butterflai implements the transparency obligations of the EU AI Act (Article 50) across web, iOS, and Android. Chat surfaces carry a persistent, non-suppressible indication that you are talking to an AI assistant. AI-generated reports and dashboard widgets carry a clear “AI” marking. The markings ship in every supported language and meet accessibility contrast standards. This is not a banner we added for launch week; it is verified by automated tests on every release.
Bounded by permissions
Explainability includes knowing what the AI could see. Every retrieval is scoped to the asking user’s permissions, per file, per integration, per report. An answer never draws on data its reader is not entitled to. Details on the Security page.
Honest about limits
No AI system is infallible, and vendors who claim otherwise are the ones to worry about. Butterflai reduces error by grounding answers in retrieved evidence, monitoring model interactions with dedicated observability tooling, and continuously evaluating answer quality against curated question sets. Where data is missing or a question cannot be answered from your sources, the honest response is to say so, and that is the behavior we engineer for.
See how the pipeline works end to end under Platform Architecture, or get a demo and ask it something hard.