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mcsqstudio/africa-startup-directory

MC Studio Africa Startup Directory Dataset card for the curated company directory built by MC Studio (Nairobi-based venture studio, Silver IBM Business Partner) for its startup curation and investment-tracking work. Summary 1,475 technology, agri-business and SME companies headquartered in Africa (plus African-founded companies operating from outside the continent). Each record combines a natural-language company_description with structured fields — founding year… See the full description on the dataset page: https://huggingface.co/datasets/mcsqstudio/africa-startup-directory.

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Dataset Card

MC Studio Africa Startup Directory

Dataset card for the curated company directory built by MC Studio (Nairobi-based venture studio, Silver IBM Business Partner) for its startup curation and investment-tracking work.

Summary

1,475 technology, agri-business and SME companies headquartered in Africa (plus African-founded companies operating from outside the continent). Each record combines a natural-language company_description with structured fields — founding year, company age, HQ country/city/region, employee band, operating countries, products & services — and two curated labels:

  • `aikya_tier` — venture-support tier assigned per the Aikya Legal (Okutta & Wairi Advocates) model: HUSTLEGROWLEAD, or Unknown.
  • `operational_sector` — Sita Sector Program operational-sector code derived by sieving the company description against per-sector trigger-word lists.

This is tabular/text data intended for sector and tier classification experiments, market intelligence, and pipeline triage.

Collection & curation

  • Source: public company directories and websites compiled by MC Studio, 2026.
  • Processing: raw records were combined, de-duplicated (unique company_id, e.g. COMP-000001), cleaned, validated, standardized and enriched with region mapping and sector sieving against per-sector trigger-word lists.
  • The canonical export is data.parquet, derived from the Standardized - Model Data sheet of the source workbook.

Data structure

ColumnTypeDescription
company_idstringUnique identifier (e.g. COMP-000001)
company_namestringCompany legal/common name
company_descriptionstringOne-paragraph natural-language description
year_foundedint32Founding year (null for 1 record)
company_ageint32Age in years as of 2026 (null for 1 record)
hq_countrystringHeadquarters country
hq_citystringHeadquarters city (null for 1 record)
hq_regionstringAfrican region of HQ (6 values incl. Outside Africa)
operating_countriesstringCountries of operation (null for 7 records)
products_and_servicesstringSemicolon-separated product/service list
employee_countstringBanded headcount (e.g. 11–50)
aikya_tierstringAIKYA HUSTLE, AIKYA GROW, AIKYA LEAD, Unknown
operational_sectorstringSector code (see below)

Sector code legend (operational_sector)

CodeMeaning
ATXAgritech
ETXEdtech
HTXHealthtech
FTXFintech
RECRetail & e-commerce
ERGEnergy
MFGManufacturing
XSCCross-sector / multi-sector technology
UnknownNot classified

These are the six Sita Sector Program priority industries (with XSC for cross-sector technology plays). The full trigger-word lists and country→region mapping used during labeling are maintained locally alongside the source workbook.

Distributions (n = 1,475)

Aikya tier: AIKYA LEAD 544 · AIKYA GROW 526 · AIKYA HUSTLE 306 · Unknown 99

Operational sector: XSC 385 · ETX 196 · ATX 195 · FTX 191 · HTX 189 · REC 152 · ERG 101 · MFG 63 · Unknown 3

HQ region: East Africa 516 · Outside Africa 347 · West Africa 270 · North Africa 200 · Central/Middle Africa 87 · Southern Africa 55

Top HQ countries: Kenya 176 · South Africa 155 · Nigeria 123 · Egypt 107 · Uganda 74 · Ghana 68 · Rwanda 65 · Tanzania 65

Employee bands: 11–50 (526) · 1–10 (306) · 51–200 (204) · 201–500 (136) · 1,001–5,000 (79) · 501–1,000 (64) · 10,000+ (44) · 5,001–10,000 (17) · Unknown (99)

Loading

python
from datasets import load_dataset

ds = load_dataset("mcsqstudio/africa-startup-directory", split="train")
print(len(ds))                 # 1475
print(ds.features)

Usage ideas

  • Sector classification: predict operational_sector from company_description.
  • Tier triage: predict aikya_tier from description + structured features.
  • Market intelligence / gap analysis by region, sector and company age.

Missing data

  • 1 record missing year_founded / company_age (COMP-000045, Agoro).
  • 1 record missing hq_city (COMP-001390, Virtual City).
  • 7 records missing operating_countries.
  • aikya_tier/operational_sector fall back to Unknown where no label could be assigned (99 and 3 records respectively).

Files

FilePurpose
data.parquetCanonical 1,475-record dataset (Parquet, nullable int32 years)
dataset_infos.jsonDataset schema & split metadata
push_to_hub.pyScript to upload this folder to the Hugging Face Hub

Ethical considerations

  • Contains public company information only; no personal data or email/phone contacts.
  • Some records are historical or flagged defunct (e.g. "Mobius Motors (Defunct)") — treat as a 2026 snapshot, not live company status.
  • Verify licensing of any downstream redistribution that goes beyond academic/classification use.
  • Company facts are as captured at collection time; verify before investment decisions.

Contact

MC Studio — Nairobi, Kenya. Sector/tier labeling follows the Aikya Legal (Okutta & Wairi Advocates) venture-support framework and the Sita Sector Program.