Step 01
Scope the signal
We scope the work with your team, defining the domains, the formats, and what good looks like for your model, calibrated on sample outputs before production begins.
Enterprise · Data Operations
The human signal at the frontier of post-training.
Reasoning traces, verifiable-reward data, and expert preference judgments, produced where a model cannot yet train itself.
ISO 27001 Certified · GDPR Aligned
Why Now
Post-training is where capability is now won.
Most of a model's usable capability is now built after pretraining, in the post-training stack: supervised fine-tuning, preference optimization, and reinforcement learning with verifiable rewards. Synthetic and AI feedback have absorbed the high-volume, low-difficulty work, which has moved the value of human data up the curve, to the reasoning, verification, and judgment a model cannot yet generate for itself.
That frontier is the work we do, expert human data produced where it is the signal that moves a model, and nowhere it is not.
The practical consequence is that the gap between a capable base model and a useful production system is now almost entirely a post-training problem. Reasoning quality, instruction following, preference alignment, and safety behavior are all set in this stage. The human data that shapes them is the highest-leverage input in the stack, and it is the most difficult to produce at quality because the work is genuinely hard: multi-step problem decomposition that holds under scrutiny, preference judgments that generalize rather than overfit, and adversarial inputs that find real failure modes rather than surface ones.
The Data
What we produce.
Reasoning and chain-of-thought data
Expert, multi-step reasoning traces that document how a correct answer is reached, why alternatives fail, and how a problem decomposes into verifiable steps. Produced across mathematics, science, law, and other domains where a single-pass answer is not enough, in machine-readable formats that train and evaluate reasoning models.
Chain-of-thought traces produced by annotators who cannot verify their reasoning fail in two ways: the steps are surface-level and do not generalize to adjacent problems, or the verification claims contain errors the model learns to replicate. Domain specialists with subject-matter credentials are what separate traces that teach a model from traces that teach it to sound like it is reasoning.
Includes: Step-by-step reasoning traces, error and failure analysis, problem decomposition, and domain-matched expert annotation.
Verifiable-reward data for RLVR
The verified problem, solution, and test data that reinforcement learning with verifiable rewards depends on. We build checkable problem sets with ground-truth answers, executable test suites for code, and the verification harnesses that turn a domain into a training signal, the verified problems and rewards that RLVR methods such as GRPO and DAPO train on, in math, code, and logic.
Includes: Ground-truth problem and answer sets, executable test cases, verification rubrics, and difficulty-graded curricula.
Preference and alignment data
Human preference judgments for the domains a verifier cannot score. Where correctness is subjective, helpfulness, safety, tone, nuanced quality, we produce the pairwise and ranked preference data that trains reward models and drives preference optimization methods like DPO, with the rationale behind each judgment documented as signal.
Preference data that records what annotators chose but not why trains reward models on surface patterns. We require and document the reasoning behind every call, giving reward model training the signal it needs to generalize rather than to overfit to annotator style.
Includes: Pairwise and ranked preferences, reward-model training data, and documented judgment rationale.
Evaluation and red-teaming
Expert measurement of what a model does in practice. We build evaluation sets that go beyond public benchmarks: task-specific test suites calibrated to your deployment distribution, hard negatives elicited through structured adversarial prompting, and held-out cases your benchmark competitors have not seen.
For safety and evaluation teams, we produce structured red-team data that surfaces failure modes under controlled conditions before they appear in production, with full documentation of the elicitation methodology so the data is reproducible and auditable.
Includes: Custom evaluation sets, expert and rubric scoring, adversarial and red-team data, and held-out benchmarks.
Instruction and demonstration data
Expert demonstrations for supervised fine-tuning where human quality still exceeds distillation, high-difficulty instructions, specialized domains, and the cases a stronger model cannot yet generate cleanly.
How We Work
Calibrated to your pipeline, from scope to delivery.
Step 02
Match specialists
We match domain specialists to your work through our academic partnerships and hold them on the project, so context accumulates and quality compounds across batches.
Step 03
Produce against living standards
We produce against your guidelines as living documents, refining the standard with you as edge cases surface and your model's needs sharpen.
Step 04
Deliver into the pipeline
We deliver in your pipeline's formats with quality metrics, agreement scores, and full documentation, integrated to your training stack rather than handed over to adapt.
Pilot engagements scope quickly, with a quality report and proposal before any commitment.
Why AdwumaTech
Built for the work a model cannot do for itself.
The value of human data now lives at the frontier, and that is where we built our team.
Expertise drawn from academic partnership
We produce specialized data through formal partnerships with universities, including the University of Ghana and Valley View University, with domain specialists fitted to the work across mathematics, the sciences, and other technical fields.
Continuity that compounds
Specialists stay on your project, accumulating the context that sharpens quality over a long engagement rather than resetting each batch.
Built into your stack
Delivered in your formats and schemas, with the metrics and documentation a training pipeline consumes directly.
Who We Work With
Who we work with.
We work with the teams training and aligning models, at the point where human data is the constraint.
Frontier and foundation model labs
Teams training models where reasoning, alignment, and evaluation decide the result.
Where we fit: Reasoning traces, verifiable-reward data, and held-out evaluation.
Applied and domain model teams
Teams adapting models to a domain that demands expert judgment.
Where we fit: Domain reasoning data, preference data, and expert demonstrations.
Post-training and alignment teams
Teams running SFT, preference optimization, and RL who need human signal where verifiers stop.
Where we fit: Preference and alignment data for non-verifiable domains.
Evaluation and safety teams
Teams measuring capability and risk before release.
Where we fit: Custom evaluations, adversarial data, and red-teaming.
Frequently Asked Questions
Questions model teams ask.
Where does human data still beat synthetic in 2026?
At the frontier of difficulty and in domains a verifier cannot score. Synthetic and AI feedback handle high-volume, low-difficulty generation well; expert humans remain the signal for hard reasoning traces, subjective preference judgment, adversarial evaluation, and verification that has to be correct. We produce where human data moves the model, and we will tell you where it does not.
Do you support RLVR pipelines?
Yes. We build the verified problem and answer sets, executable test suites, and verification rubrics that reinforcement learning with verifiable rewards depends on, difficulty-graded for curricula and ready for the RLVR methods such as GRPO and DAPO that train on them.
What domains do you cover?
Mathematics, code, science, law, and other technical and professional domains, produced through academic partnerships and matched to specialists qualified in each. We scope domain coverage with you before production.
How do you handle quality on subjective tasks?
Calibration before production and measurement throughout. We align with your team on sample outputs, refine guidelines as edge cases surface, and report agreement scores and audit sampling rather than asserting a static rubric.
What formats do you deliver in?
JSONL, Parquet, or your own schema, with the metadata, metrics, and documentation your training pipeline expects, defined during scoping so the data integrates directly.
Who owns the data you produce?
You do. The data, and the rights to it, are yours, produced under NDA and handled under our ISO 27001 certified information security management system. Your data and your model are never shared, and we have no model of our own for them to serve.
Put expert human data where it counts.
Scope a data engagement with our team, or request sample data to see the quality before you commit.