Expert Data Infrastructure for Foundation Models
RLHF for code, complex reasoning data, and African language expertise
Discuss Data RequirementsThe Smart Data Era
Foundation model performance is now determined by data quality, reasoning complexity, and domain expertise.
Reasoning Depth
Chain-of-thought annotation, multi-step problem decomposition, and explanatory rationale. Annotators who document how experts actually reason through complex problems.
Code Expertise
RLHF for code generation requires software engineers who ship production code. Debugging, architectural review, unit testing, efficiency optimization.
Linguistic Intelligence
Multilingual capability in underrepresented languages requires native speakers with cultural fluency. Annotation that captures meaning, context, and nuance.
Domain Knowledge
Expert-level annotation in legal, medical, financial, and scientific domains. Credentialed professionals applying genuine expertise to complex evaluation tasks.
Smart Data for Model Development
RLHF for Code
Expert feedback for code generation models from software engineers with production experience.
- AI-generated code review with line-level correctness
- Debugging and error correction with rationale
- Unit test generation validating edge cases
- Multi-solution ranking on efficiency & security
Reasoning Data
Complex chain-of-thought annotation and multi-step reasoning—explanatory depth that advances model cognition.
- Chain-of-thought with explicit reasoning steps
- Multi-step problem decomposition
- Explanatory depth: why answers succeed/fail
- Cultural and contextual nuance
African Languages
Native-speaker data for multilingual model development
- Pre-training datasets: Akan, Hausa, Yoruba, Ewe, Ga
- RLHF preference data from native speakers
- Cultural alignment annotation
- Text corpora, speech data with transcriptions
Why Foundation Model Teams Work With Us
Purpose-Built for Smart Data
Infrastructure designed for expert annotation at scale. Domain-matched assignment. Quality architecture for complexity that defines frontier model development.
Research Partnership
We engage on novel annotation methodologies, emerging task definitions, and custom evaluation frameworks. Long-term orientation, not transactional delivery.
How Labs Work with Us
Embedded Teams
Dedicated annotators trained on your guidelines, integrated into your workflows.
Ongoing programs, long-term development
Project-Based
Defined scope with deliverables, timelines, and quality SLAs. Full management.
Training milestones, capability expansion
Surge Capacity
Rapid expert scaling for intensive annotation periods with maintained quality.
Variable demand, time-critical data
Evaluation
Independent capability evaluation, safety testing, adversarial assessment.
Pre-deployment, multilingual bias testing
Quality and Security Infrastructure
Annotator Qualification
Software engineers with CS/SE degrees. Domain experts with professional qualifications. Native speakers based in-country.
Quality Architecture
Calibration against expert consensus. Multi-tier review with senior oversight. Inter-annotator agreement tracking. CAPA protocols.
Data Security
ISO 27001-aligned. SOC 2 Type II. Encryption at rest and in transit. Role-based access. Comprehensive audit logging.
Smart Data. Expert Infrastructure. Global Scale.
The next phase of foundation model development requires Smart Data—expert annotation in reasoning, code, and underrepresented languages. We built our infrastructure for this shift.
Discuss Data Requirements