tanitdata is an independent studio of open-data intelligence, operating from Tunisia for North Africa and beyond. Its mission is to make public and government data genuinely accessible through AI technologies — bridging the gap between open-data portals and the LLM ecosystem that increasingly structures how analysts, journalists, and policymakers access information.
DataDoIt is one of tanitdata’s partners. Together with Must University, DataDoIt co-operates the talent incubator that trains and supports tanitdata’s research fellows. tanitdata is not a product of any single organization — it is autonomous, mission-driven, and designed to build an open-data infrastructure native to the AI era rather than retrofitted onto pre-LLM tools.
A single practice in three stages: access, analyze, inform
tanitdata organizes its work as one practice in three stages.
Access means AI-native infrastructure — servers and tools that make open datasets directly queryable by AI assistants. The first operational project is agridata MCP, a Model Context Protocol server connecting Tunisia’s agricultural open-data portal to any LLM client. A researcher using Claude, ChatGPT, or Gemini can query agricultural production data as naturally as asking a question in plain language.
Analyze is the empirical layer where the studio and its research fellows turn accessible data into conclusions that are specific, contestable, and empirically grounded. The fellows come through the DataDoIt × Must University incubator, which gives tanitdata both analytical capacity and a pipeline of regional talent.
Inform is the publishing layer. tanitdata publishes in formats designed for how public actors, funders, journalists, and civil society actually read — short, visual, shareable, AI-discoverable. The studio’s first policy brief stress-tests Tunisia’s exposure to the 2026 oil shock and identifies a six-to-nine-month policy window for response.
Why this matters for the EMEA data landscape
Two operational contributions stand out.
First, tanitdata treats open-data quality as a first-class output. The studio’s data audits assess each public data source along five dimensions — accessibility, machine-readability, metadata quality, update frequency, and AI-readiness — before any downstream analysis depends on it. This resolves a gap that sustainability, climate, and economic modeling work repeatedly encounters: the quality of the underlying open data is rarely documented and almost never audited.
Second, tanitdata is positioned from the region, for the region. The institutions that publish open data across North Africa, the Middle East, and sub-Saharan Africa operate under constraints — limited regional capacity to build AI-native infrastructure, governance frameworks designed for a pre-LLM world, funding landscapes that import solutions from elsewhere — that need to be addressed locally. tanitdata’s positioning, with particular attention to the Global South, makes its outputs materially different from solutions designed in OECD capitals and adapted downstream.
Engage with tanitdata
The studio’s Research index collects all its publications — briefings, data audits, methodologies, and notes from empirical work. Researchers, students, institutions, and funders interested in collaboration can find pathways at Get Involved.