AI Labs

The Data Infrastructure Behind Frontier Capability.

Where foundation model teams source the human expertise their training pipelines depend on. RLHF for code from credentialed engineers, structured reasoning data, and multilingual datasets at the quality standard frontier training requires.

ISO 27001 Certified · GDPR Aligned · Open Source on Hugging Face

The Opportunity

Why Data Quality Is The New Bottleneck

The performance gap between frontier models is now determined by data. Returns from scaling parameters alone have plateaued, and the labs that move ahead train on higher quality human feedback, more rigorous reasoning traces, and deeper domain expertise.

Human feedback that compounds into capability ceiling

RLHF data quality compounds across training cycles. Annotation that surfaces correct reasoning, identifies architectural issues, and rewards genuine quality lifts the capability ceiling cycle over cycle.

Reasoning data that teaches models to think rather than match

Models trained on structured reasoning traces, multi step decomposition, and explicit explanation of why answers succeed or fail learn to reason through problems, trace dependencies, and surface assumptions.

Multilingual depth in languages the public web cannot supply

Frontier capability now extends to languages where the public web does not provide enough training data. Machine translated English does not deliver meaning, tonal semantics, code switching patterns, or cultural context.

What We Deliver

What We Deliver

Three core capabilities for foundation model teams, supported by credentialed annotation, structured quality architecture, and an open source track record that is independently verifiable.

RLHF for code

Code generation models require feedback from engineers who write and ship production code. Reviewers evaluate AI generated code at the line level for correctness, security, efficiency, and architectural quality.

Every reviewer holds a computer science or software engineering degree and has production experience.

Explore RLHF for code
Post-training data and chain-of-thought annotation

Structured reasoning traces with explicit step decomposition, multi-path analysis, and explanatory depth that documents why an answer succeeds or fails. Annotators are domain matched across mathematics, logic, scientific reasoning, legal analysis, financial modeling, and engineering problem solving.

Explore post-training data
Multilingual NLP and African language datasets

Pre training datasets, RLHF preference data, and speech corpora across Ghanaian and West African languages including Twi, Ewe, Ga, Dagbani, Hausa, and Yoruba.

Open source datasets mGhana-ST and UGSpeechData on Hugging Face serve as a live demonstration of the annotation quality and linguistic depth we apply to every client engagement.

Foundation Model Grade

How We Deliver At Foundation Model Grade

Every annotation program moves through a six stage process designed to meet the quality, security, and consistency standards foundation model teams require.

Stage 1, scoping and rubric design

Every engagement begins with a written scope covering task definition, annotation taxonomy, quality thresholds, calibration approach, and inter rater reliability targets.

Stage 2, annotator qualification and calibration

Reviewers are credentialed against the task specification and run through calibration sets against expert consensus benchmarks before live work begins.

Stage 3, embedded production work

Annotators integrate into your workflow on your guidelines, your platform, and your iteration cadence. Continuity compounds into better data over time.

Stage 4, multi tier review and adjudication

Junior reviewer output passes through senior review. High stakes annotation uses multi reader consensus with senior adjudication by default.

Stage 5, inter rater reliability and quality reporting

Every batch ships with IRR metrics, threshold enforcement, per annotator performance reporting, and CAPA protocols when metrics deviate.

Stage 6, secure delivery and audit trail

Datasets are delivered through encrypted channels with provenance documentation, annotator credentials, calibration scores, rubric versions, IRR metrics, and adjudication records.

Use Cases

How Foundation Model Teams Deploy AdwumaTech

RLHF pipeline scaling for code generation models

AdwumaTech delivers production engineer reviewed RLHF data with line level correctness annotation, security review, multi solution ranking, and unit test generation.

Fit: foundation model labs, applied AI vendors, AI infrastructure companies.

Reasoning data for new model releases and capability expansion

AdwumaTech delivers chain of thought annotation, multi step decomposition, and explanatory depth across mathematics, logic, science, legal, financial, and engineering reasoning.

Fit: frontier model labs, foundation model vendors, applied AI labs, academic AI research programs.

Multilingual capability expansion into underrepresented languages

AdwumaTech delivers pre training corpora, RLHF preference data, and speech datasets across covered languages, with mGhana-ST and UGSpeechData as public reference standards.

Fit: frontier model labs, sovereign and regional model programs, multilingual AI vendors.

Independent capability and safety evaluation

AdwumaTech runs evaluation programs separately from annotation programs, with credentialed evaluators, structured rubrics, and adversarial testing protocols.

Fit: foundation model labs, applied AI vendors, AI safety research programs, model evaluation teams.

Difference

What Makes AdwumaTech Different

Open source proof of annotation quality

Our open source datasets mGhana-ST and UGSpeechData are published on Hugging Face as an independently verifiable demonstration of annotation depth and linguistic precision.

Credentialed reviewers across every modality

RLHF for code by software engineers with CS or SE degrees and production experience. Reasoning data by domain matched annotators. Multilingual NLP by native speakers.

Quality architecture built for foundation model standards

Calibration against expert consensus benchmarks, multi tier review, IRR tracking, threshold enforcement, CAPA protocols, and feedback loops with annotation leads.

Security infrastructure built for proprietary model work

ISO 27001 certified information security management system, encryption, role based access controls, comprehensive audit logging, and NDA ready workflows.

Engagement Models

How AI Labs Work With Us

Four engagement structures designed to fit how foundation model programs operate in production.

Embedded teams

Dedicated annotators trained on your specific guidelines and integrated into your workflows. Best fit for ongoing programs where consistency and institutional knowledge compound.

Project based engagements

Defined scope with deliverables, timelines, and quality SLAs. Best fit for training milestones, capability expansion, or one time dataset creation.

Surge capacity

Rapid scaling of qualified annotators for intensive annotation periods without compromising quality standards.

Independent model evaluation

Capability evaluation, safety testing, red teaming, and adversarial assessment delivered independently from annotation programs.

Security

Security, Governance, and Compliance

Information security

Encryption at rest and in transit using AES 256 and TLS 1.3. Role based access control. Comprehensive audit logging. ISO 27001 certified information security management system.

NDA ready workflows for proprietary model work

Every engagement operates under NDA from initial scoping, with documented access controls and full audit trail for proprietary model outputs and confidential training data.

Regulatory alignment for client workflows

We support clients operating across GDPR for European data flows and the Ghana Data Protection Act 2012 for in country data.

Who we work with

Frontier and foundation model labs, applied AI vendors, sovereign and regional model programs, AI research institutions, and model evaluation, safety, and red team programs.

Free Pilot

Start With a Free Pilot

Test our annotation quality on your data before committing to a program. We offer a complimentary pilot of 1,000 annotated data points across RLHF, reasoning, or multilingual tasks. You receive the annotated dataset, a quality report with IRR metrics, and a debrief with our annotation lead.

Frequently Asked Questions

Common Questions

We deliver credentialed reviewers across RLHF for code, reasoning, and multilingual NLP, structured quality architecture with calibration and IRR built in, and open source datasets on Hugging Face that publicly demonstrate the annotation standard we ship to clients.
RLHF for code generation models, complex reasoning and chain of thought annotation, multilingual NLP datasets across text and speech, and independent model evaluation. Coverage spans pre training corpora, RLHF preference data, reasoning traces, evaluation rubrics, and adversarial testing protocols.
We work with a curated network of credentialed reviewers including software engineers with computer science degrees, domain matched annotators credentialed against task complexity, and native speaker linguists. The network is anchored by active MOUs with the University of Ghana and Valley View University.
Native speaker annotation across Ghanaian and West African languages including Twi, Ewe, Ga, Dagbani, Hausa, and Yoruba. Our open source datasets mGhana-ST and UGSpeechData on Hugging Face demonstrate the annotation standard we apply to client engagements.
AdwumaTech holds ISO 27001 certification covering our information security management system. We align with GDPR for European data flows and the Ghana Data Protection Act 2012 for in country data. Every engagement operates under NDA from initial scoping, with role based access, audit logging, and encryption.
Yes. We support embedded teams for ongoing programs, project based engagements with defined SLAs, and surge capacity for time critical periods. Surge teams draw from our existing credentialed pool, so quality standards hold from day one.
Yes. We offer a complimentary pilot of 1,000 annotated data points across RLHF, reasoning, or multilingual tasks. The pilot includes the annotated dataset, a quality report with inter rater reliability metrics, and a debrief with our annotation lead. Typical turnaround is five to seven business days.

The data infrastructure your model architecture depends on

For foundation model teams, the annotation partner you choose determines your model's capability ceiling. Start with a free pilot to validate the standard against your own data, or discuss the structure of an ongoing program for your training pipeline.