TexaData

Lagos, Nigeria · Douala, Cameroon

The data that was never licensed.

Texa Data is a supply house. We find material that has never been licensed to anybody — radio and podcast back-catalogues, film and documentary libraries, company document archives, property and institutional records — clear it per asset, clean it, annotate it, and deliver it to the companies that buy data. Where the material does not exist at all, we go and originate it.

A welder at work on a kerb, a burst of sparks off the weld
Corridor environments · Lagos, Ibadan, Douala — licensed editorial frames, not Texa capture
Corridor
11 countries
Consent coverage
100%
Rights model
Per asset
Registered
RC 8467566
01

Two absences here: what nobody cleared, and what nobody filmed.

Public text is running out and the labs have moved to licensing. But that market organised itself around archives that were already represented — broadcast libraries, publisher backfiles, studio catalogues, every one of them with an agent. West and Central Africa has decades of radio, call-in programming, broadcast and film, and not one aggregator working it. And beyond the tape, there are working environments nobody ever pointed a camera at. Neither absence is about access. Both are about work nobody has done.

12,628hours

The largest validated African multimodal speech dataset, covering forty languages between them.

African Languages Lab, 2026

88per cent

Of African languages are classified as severely underrepresented or entirely ignored in computational linguistics.

Voice of a Continent, arXiv 2505.18436

395million

Speakers of the eight languages we work across, against a licensed commercial corpus that is, for practical purposes, zero.

Speaker estimates, all varieties

Speakers by language, West & Central Africa
Approximate, all varieties, in millions
Nigerian Pidgin — approx. 120 million speakersNigerian Pidgin120MHausa — approx. 88 million speakersHausa88MYoruba — approx. 47 million speakersYoruba47MLingala — approx. 40 million speakersLingala40MFulfulde — approx. 37 million speakersFulfulde37MIgbo — approx. 31 million speakersIgbo31MAkan (Twi) — approx. 20 million speakersAkan (Twi)20MWolof — approx. 12 million speakersWolof12M
Estimates vary by source and by how varieties are counted; these are mid-range figures used for scale, not precision. The argument is the ratio between the speaker base and the licensed corpus available to train on.

What is already open — and what it does not cover.

Three corpora landed in 2026 that bear directly on this argument, and a buyer who has read them will raise them whether or not we do. So we raise them first. Two are free, enormous and worth taking. None of them holds the material below.

Egocentric-100K
Build AI · Hugging Face, 2026
100,405 hours · 2,010,759 clipsApache 2.0
EgoStandard
Lightwheel · Hugging Face, 2026
100,000 hours · 15,000+ tasks and scenesOpen
Ego4D
Meta AI, 2022
3,670 hours · 923 participants · 74 locationsRestricts commercial use
WAXAL
Google with Makerere, University of Ghana and Digital Umuganda — February 2026
11,000+ hours · ~2m recordings · 21 languagesOpen
02

Seven supply lines, and we are explicit about which are stocked.

Two states, marked on every line. Live means there is cleared material or a running capture programme behind it and you can take delivery against a spec. Intake means we clear it to your mandate and hold no standing stock — no inventory, and in one case no public sample, ever. A supplier who blurs that line is asking you to discover it later.

Records & documents

Structured and document data, cleared to a named mandate. No standing stock by design.

Published reference collections

One worked example per environment, complete with its instruments, so the standard can be inspected before anything is commissioned.

All datasetsEvery collection carries frames, a manifest and a signed instrument per asset.
03

Four questions decide whether data is an asset or a liability.

Buyers in this market converge on the same test, and most suppliers fail it quietly — in aggregate assurances, in licences written for distribution rather than training, in rights holders who were never actually paid. We answer all four in writing, per asset, before anything ships.

  1. 01

    Documented per asset

    Every manifest row carries a consent reference and a licence reference resolving to a scanned, signed document. Provenance travels attached to the file, not in a separate assurance.

  2. 02

    AI training named in the grant

    Train, pre-train, fine-tune, validate, benchmark and evaluate — including generative models — and sublicensable onward through multiple tiers.

  3. 03

    Producible on request

    Consent is captured twice: a signed paper instrument and a verbal on-camera confirmation in the contributor's own language, against the same asset ID.

  4. 04

    Contributors actually paid

    Settled at the point of capture, with a payment reference tied to the assets it covers. Revenue share, where it applies, is written into the instrument.

A market quarter from above, roofs and umbrellas to the horizon
Nigeria — corridor at scale · editorial
04

Name any asset ID. We return the signed agreement behind it.

A live resolution from the register, not an illustration. Each link is a record an auditor can open.

  1. 01AssetTXA-LAG-EGO-000238-column manifest row
  2. 02ConsentTXA-AVR-0004Signed instrument plus on-camera verbal
  3. 03ContributorTXA-CTR-0002Identified rights holder
  4. 04LicenceAI training grantedSublicensable, multi-tier
  5. 05PaymentTXA-PAY-0002₦25,000 · Settled at the point of capture
05

The chain holds because of how the people in it were treated.

Provenance is a paperwork problem downstream and a labour problem upstream. A contributor recruited through a broker, paid out of someone else's margin, or told what the recording is for only after signing, produces an instrument that will not survive being read closely by anyone who wants it not to. Ours are recruited in person by an operator who comes back, paid in full on the day at a rate benchmarked to the trade being filmed, and keep title to what they made.

01
RecruitmentContributors are recruited in person, at their own place of work, by an operator who comes back. There is no crowd panel, no labour broker and no intermediary taking a margin out of a fee we set.
02
Training and calibrationEvery operator runs identical locked settings and the same slate procedure, and is calibrated against a reference lot before any of their material enters a delivery.
03
What is actually paidA fee agreed before recording, benchmarked against a working day's earnings in the trade being filmed, paid in full on the day — whether or not the material is ever used or ever sells.
04
Who we do not filmNobody under eighteen contributes. We do not recruit through an employer who stands to benefit from a worker's participation: a fee routed through someone with authority over the contributor is not freely accepted.
05
What the contributor keepsTitle. We license, we do not buy out. A contributor may refuse a category of use at signature and the refusal is recorded against the asset rather than argued with.
06

Sourced, cleared, cleaned, annotated, delivered.

This is the whole operation, and it is stated twice at every stage — once for media, once for records — because how you clean a reel of tape and how you clean a table of transactions are not the same question, and a supplier who gives one answer for both has not done either. Stage four is the one buyers pay for without ever raising it in a meeting. It is written out here at the same length as the rest.

  1. 01

    Source

    Find who holds it and get into the room. Every line in this catalogue begins as a relationship rather than a download.

    Media
    Station managers, podcast networks, production houses and the event and commercial videographers who hold thirty years of ordinary life on tape — approached in person in Lagos, Ibadan, Douala and Yaoundé.
    Records & documents
    Institutions, professional firms and archive holders, approached with a written mandate naming exactly which records are wanted and what they are for. Nobody is asked to hand over data on the basis of an idea.
  2. 02

    Clear

    Reconstruct chain of title per asset and put a signed instrument behind it. This is the work, this is the cost, and this is why the material has never been licensed before.

    Media
    Production agreements read line by line, performers and contributors traced, third-party music and licensed inserts identified and removed, and a licence signed that names AI training in the grant and permits sublicensing onward through multiple tiers.
    Records & documents
    A data-sharing agreement with the holding institution, a de-identification schema agreed in writing before anything moves, and an NDPA 2023 assessment per tranche. Where a record class cannot clear, the tranche is declined rather than reduced.
  3. 03

    Capture & digitise

    Get it off the shelf and into one technical standard. Uniformity beats peak quality: a buyer's pipeline cares far more about every file being the same than about the best file in the set.

    Media
    Tape and film transferred at a fixed standard; originated capture runs locked identical settings across every operator — one resolution, one frame rate, one codec, no HDR — with a clap-synced second angle and lavalier audio on the subject.
    Records & documents
    400 dpi colour scanning, deskewed, one page per image, TIFF master with a PDF/A compile. Structured records exported to a single schema with the data dictionary written alongside rather than reconstructed later.
  4. 04

    Clean

    The unglamorous half, and the half a buyer pays for without ever mentioning it in a meeting. Material arrives inconsistent. It leaves uniform, and every change made to it is written down.

    Media
    Deduplication, colour-bar and dead-air trimming, channel separation where the source allows, loudness normalisation to a stated target, corrupt-segment detection, and technical metadata extracted from the container rather than typed in by hand.
    Records & documents
    Deduplication, field normalisation, date and currency coercion, entity resolution across sources, and a null and outlier report shipped with the tranche. Every transformation applied is logged, so nothing about the delivered set is unexplained or unreproducible.
  5. 05

    Annotate

    The layer that turns cleared material into training data. To your schema where you have one, ours where you do not — and ours is published, not held back as leverage.

    Media
    Transcription with speakers attributed and code-switching preserved rather than tidied out; diarisation and overlap ratio; task, intent, objects handled, step timings and failure events — including the reasoning behind a change of method, which is the scarcest layer we produce.
    Records & documents
    Page regions and reading order, table structure, stamps and handwriting flagged, entity and field extraction, and schema validation on every row before it is allowed into a delivery.
  6. 06

    Deliver

    One format, one folder structure, rights already attached. Your ingest cost is our design constraint, not an afterthought we leave you to absorb.

    Media
    Manifest, annotations, rights pack, data card, proxies sized for a single download and masters on acceptance — by signed URL, bucket delivery or physical media.
    Records & documents
    Manifest, data dictionary, transformation log, rights pack and data card, in Parquet, CSV or JSONL against your stated schema.
07

One corridor, two language systems, one operator.

Most suppliers who reach Africa reach anglophone Africa. Texa is founded across both — Nigerian-registered and Lagos-based, with the founder's own network running through francophone Cameroon. That is why the corridor below is a single operating plan rather than two unrelated markets.

LagosIbadanDoualaYaoundéAbujaKanoPort HarcourtCotonouLoméAccraKumasiAbidjanDakarLibrevilleKinshasa
Operating base — Lagos, Nigeria Founder network — Cameroon Corridor plan
A dense city quarter from the air, high-rises beyond
Lagos — operating base
Bowls of grain and pulses set out at a market stall
Corridor — capture environment
The driver of a yellow bus at the wheel
A barber cutting hair at a stand on the roadside
Hands beating the weft on a narrow kente loom
An angle grinder throwing a wide arc of sparks across a yard
A market seen from above, dense with trading umbrellas
A motorcycle rider from behind, following traffic
08

Hear it, read it, download it — before you email anybody.

Every data type we supply has a page carrying its delivery specification, its metadata schema and, where the material is cleared for it, a playable excerpt and a downloadable annotation. No NDA, no form, no call. What sits behind an NDA is the signed instruments and the masters, because the people in them agreed to be recorded for training rather than for a website.

If you are holding material, you are the other half of this business.

A supply house with a catalogue and no counterparties is a brochure. If you run a station, a podcast network, a production house, a professional firm, an estate agency or an institutional archive — and you have never licensed any of it — we fund the clearance and the preparation, you keep title, and you are paid out of what it earns. Nothing up front, no exclusivity by default.

Maize roasting over charcoal at a roadside stand
09

Tell us the spec. We will tell you what it costs to collect.

We work best against a written brief: an action schema, an environment list, a language set, a volume. If you have one nobody has been able to fill from an archive, that is the conversation we want.

Pilot
Fifty hours at production standard, two weeks from spec sign-off, priced at cost
Price
Quoted per delivered hour, below equivalent US or EU managed programmes
Capacity
Stated against operators trained and instruments signed, restated monthly
Indemnity
Texa warrants the rights it grants and indemnifies against rights-chain claims
Samples
Manifest schema and a complete annotation file are public — no NDA. Media and signed instruments follow one.