How it works

From headline to holding, four times a day.

AURA is an automated newsroom: it reads Nigeria’s business and regulatory press, pulls out who owns and controls what, and publishes it as a register anyone can search. No one types the records in. That is the point, and also the caveat.

The pipeline

  1. 01

    Collect

    Google News RSS and four news APIs, searched for Nigerian companies, agencies and owners, four times a day.

    181 publishers
  2. 02

    Screen

    A topic guard drops sport, celebrity and off-topic stories before any model is paid to read them.

    9,710 screened
  3. 03

    Read

    A language model cascade (Ollama Cloud, then NVIDIA NIM) extracts people, companies, stakes, contracts and a risk score.

    LLM cascade
  4. 04

    Resolve

    Names are normalised and merged, repeat reports grouped into holdings, CAMA 2020 bands and patterns computed.

    7 holdings
  5. 05

    Publish

    The register, the brief and this site are rebuilt from the exports and deployed to the edge.

    180 editions

The record so far

367
pipeline runs since 17 Jul 2026
9,710
articles screened
4,179
stories analysed and published
181
publishers seen
7
ownership holdings mapped
180
brief editions archived

Measured, not assumed

Every stored field is model output, so the archive cannot grade itself. AURA samples the articles it reads into an evaluation corpus, and a person labels what the correct extraction should have been. Only human-verified labels can reach the optimiser (DSPy with GEPA).

277
article inputs captured for evaluation
16
hand-verified gold extractions
42
backend test modules, plus the site’s own unit tests

The validation gate

Extraction is the easy half. Before anything reaches this site, the register passes a set of checks in register_quality.py; every page reads only what passed, and a build test fails if a withheld record renders anywhere.

25
ownership claims extracted
17
passed and published
8
withheld (7 with no source, 1 implausible)

What “worth checking” means

Nigeria requires disclosure of significant control from 5% (CAMA 2020 s.868 and the CAC PSC Regulations 2022), not the 25% used in the UK. The patterns below come from the FATF and Egmont concealment typologies, limited to what the reporting lets AURA compute.

What AURA can’t tell you

It is not the CAC register

Every record is extracted by a language model from news reporting. None carries a CAC filing unless the reporting cited one.

A pattern is not wrongdoing

Offshore vehicles and nominee holders are legal. A pattern is a reason to look closer, nothing more.

Names collide

Different people and companies can share a name. Check the source articles before relying on a record.

Models make mistakes

Stakes can be misread, roles confused, stories mis-scored. The evaluation set exists to measure and reduce that.

Spotted an error?

Corrections are welcome and handled in the open. Say which record is wrong and link the source.

Request a correction

Built with

Source and history on GitHub. Raw exports at /data.

Try a name like “Dangote”, a company like “Zenith”, or a word like “EFCC”.