Building the Data Infrastructurefor the Global South.
LeanGoogs provides the data, human intelligence and evaluation infrastructure AI companies need to build systems that work across the Global South.
AI doesn't fail everywhere
in the same way.
The same system can be reliable in one market and quietly wrong in another. Performance shifts with language, culture, geography, profession and the conditions people actually use it in.
Generic datasets and evaluation methods cannot always capture those differences.
Prompt · identical across all three
My transfer failed but my account was debited. What should I do?
Omits the dispute path and the reference number the customer needs.
Materially incomplete. Misses reversal window, reference and dispute process.
Illustrative of a failure pattern we measure, not a published benchmark result.
Performance shifts along every one of these
That's where we come in.
We connect AI builders with the people, knowledge, data and evaluation capabilities required to understand how AI performs in real-world markets.
Three capabilities. One partner.
AI that works within your boundaries.
Build, evaluate and manage AI data programs around the jurisdiction, governance, security and ownership requirements of your organisation.
We do not sell a sovereignty guarantee. Requirements differ by jurisdiction, regulator, contract and data type — so we design the program around the ones that apply to you, and say plainly what we cannot support.
Explore Sovereign AIBuilt for the contexts global AI cannot afford to overlook.
The Global South is not one market. It is thousands of languages, communities, industries, environments and cultural contexts — and every one of them enters the same controlled pipeline.
Markets
- 01Collect
- 02Structure
- 03Evaluatea share rejected at review
- 04Deliverproduction-ready
Hover a market to trace its path through the system.
The world's AI systems increasingly operate in markets whose languages, cultures and real-world contexts have historically received less representation in AI infrastructure. LeanGoogs builds the local intelligence needed to evaluate and improve AI within those contexts.
Explore the Global South →People are part of the infrastructure.
A verified network of local contributors and subject-matter experts, structured by language, professional background and demonstrated capability — not a generic freelancer pool.
Incoming task
Evaluate a clinical assistant in Yoruba
Language
Expertise
Capability
Matched to a qualified contributor: Yoruba · Healthcare · Evaluation
From definition to delivery.
Every engagement runs the same controlled path, so you know what happens to your work at each stage.
Define
Tell us what your AI system needs — the languages, markets, expertise and the standard it has to meet.
Match
We identify the right languages, locations, skills and subject-matter expertise for the work.
Execute
Qualified contributors and experts complete the work through controlled workflows.
Validate
Our quality systems and reviewers evaluate the outputs before anything reaches you.
Deliver
You receive production-ready data, evaluation results or intelligence.
Built for industries where context matters.
Where being wrong in a particular language, market or professional domain has a real cost.
Quality isn't a feature. It's the system.
The difference between usable data and expensive noise is the process around the people producing it.
Local depth
We build networks within markets rather than treating entire regions as anonymous data sources.
Verified expertise
Contributors are matched according to language, professional background, skills and demonstrated performance.
Quality by design
Our workflows are built around qualification, review, gold standards and continuous measurement.
Built for AI
We don’t simply provide labour. We build data and evaluation systems designed around AI development.
The quality system
Four layers, running continuously.
Qualification
Contributors are assessed for language, professional background and demonstrated skill before they are eligible for work.
Gold standards
Known-answer items are seeded through live work so quality is measured continuously, not sampled at the end.
Review
Independent reviewers check output against the brief, with escalation paths for disagreement.
Measurement
Agreement, accuracy and consistency are tracked over time and fed back into who is matched to what.
One partner for the human side of AI development.
Instead of assembling four vendors and reconciling four standards of quality, the data, the experts and the evaluation come from one system.
- Data collection✓
- Data annotation✓
- Expert data✓
- Multilingual data✓
- Human preference data✓
- Model evaluation✓
- Red teaming✓
- Domain experts✓
- Local-market evaluation✓
- Continuous evaluation✓
What we're learning about AI in the real world.
Reports, benchmarks and research on how AI actually performs across languages and markets — including the results that are inconvenient.
Ready to build AI that works in more places?
Tell us the markets, languages and systems involved, and we will scope the data, expertise or evaluation the work needs.