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 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.
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.
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.
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.
Three core capabilities for foundation model teams, supported by credentialed annotation, structured quality architecture, and an open source track record that is independently verifiable.
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.
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.
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.
Every annotation program moves through a six stage process designed to meet the quality, security, and consistency standards foundation model teams require.
Every engagement begins with a written scope covering task definition, annotation taxonomy, quality thresholds, calibration approach, and inter rater reliability targets.
Reviewers are credentialed against the task specification and run through calibration sets against expert consensus benchmarks before live work begins.
Annotators integrate into your workflow on your guidelines, your platform, and your iteration cadence. Continuity compounds into better data over time.
Junior reviewer output passes through senior review. High stakes annotation uses multi reader consensus with senior adjudication by default.
Every batch ships with IRR metrics, threshold enforcement, per annotator performance reporting, and CAPA protocols when metrics deviate.
Datasets are delivered through encrypted channels with provenance documentation, annotator credentials, calibration scores, rubric versions, IRR metrics, and adjudication records.
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.
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.
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.
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.
Our open source datasets mGhana-ST and UGSpeechData are published on Hugging Face as an independently verifiable demonstration of annotation depth and linguistic precision.
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.
Calibration against expert consensus benchmarks, multi tier review, IRR tracking, threshold enforcement, CAPA protocols, and feedback loops with annotation leads.
ISO 27001 certified information security management system, encryption, role based access controls, comprehensive audit logging, and NDA ready workflows.
Four engagement structures designed to fit how foundation model programs operate in production.
Dedicated annotators trained on your specific guidelines and integrated into your workflows. Best fit for ongoing programs where consistency and institutional knowledge compound.
Defined scope with deliverables, timelines, and quality SLAs. Best fit for training milestones, capability expansion, or one time dataset creation.
Rapid scaling of qualified annotators for intensive annotation periods without compromising quality standards.
Capability evaluation, safety testing, red teaming, and adversarial assessment delivered independently from annotation programs.
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.
Every engagement operates under NDA from initial scoping, with documented access controls and full audit trail for proprietary model outputs and confidential training data.
We support clients operating across GDPR for European data flows and the Ghana Data Protection Act 2012 for in country data.
Frontier and foundation model labs, applied AI vendors, sovereign and regional model programs, AI research institutions, and model evaluation, safety, and red team programs.
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.
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.
