A six-step pipeline built around one question: does the data we produce actually improve your model? Every step is logged, measured, and reported. Nothing is left to chance.
Most annotation companies take your data, return a file, and leave. You have no visibility into how annotators were selected, whether they were calibrated, how disputes were resolved, or whether the resulting data is going to improve your model or just add noise to it.
Every step of our pipeline generates a log entry. Every batch is tracked through three tiers of quality assurance. The final delivery includes not just the annotated dataset but a QA report with inter-annotator agreement scores, gold standard pass rates, and a data card documenting exactly how the data was produced.
Two weeks after every delivery, we follow up and ask for your benchmark result. If the data produced the expected improvement, we document it as your case study. If it did not, we investigate and re-deliver at no cost.
Every project runs through the same pipeline regardless of size. The steps are non-negotiable — skipping any one of them produces the data quality failures we exist to prevent.
You share your data samples. We ask the questions that actually determine quality: What does your ML training pipeline need as output format? What quality threshold constitutes success? What compliance requirements apply? What is the specific failure mode your current data is producing? The Scope of Work we produce is precise — not a generic template — because every project has different requirements and different quality risks.
The annotation guidelines document is the single most important quality control mechanism in our operation. Vague guidelines produce inconsistent data regardless of how experienced your annotators are. We write 8–15 page project-specific guidelines covering: evaluation criteria with precise definitions, 20–30 worked examples including the hard cases, edge case rules with documented decisions, and explicit escalation procedures for tasks that need review.
From our vetted pool, we select annotators matching three criteria: native fluency in the target language, appropriate education and experience level, and relevant domain expertise for the specific task. Then we run a calibration session — all selected annotators complete 20–30 tasks independently, then discuss every disagreement together. We do not begin live annotation until Cohen's kappa among the team is above 0.70. Anyone consistently below 0.65 on calibration is replaced.
This is where our pipeline outperforms pure-human competitors. The RLAIF pre-scorer (Claude API) evaluates each task and pre-populates a suggested answer with reasoning before any annotator sees it. For image tasks, SAM2 pre-draws segmentation boundaries. Annotators then validate, correct, or override the AI suggestion — they never start from scratch. AI handles the 70–90% of tasks with clear correct answers. Expert humans handle the 10–30% requiring genuine judgment.
Quality assurance runs concurrently with annotation — not as a final check before delivery. Every evening, automated scripts analyse the day's annotation. Every week, a 10–15% double-annotation sample runs through peer review. The senior ML engineer audits all flagged tasks plus a 5% random sample. If kappa drops below 0.65 on any batch, annotation pauses and recalibration runs. The pipeline cannot proceed with degraded quality.
Data is exported and converted to your required format (JSONL, CoNLL, COCO, or custom). Validation scripts check every field for completeness and encoding. Delivery includes three documents: the annotated dataset, a QA report with kappa scores and gold accuracy, and a data card documenting how the data was produced. Two weeks later — the step every annotation company skips — we follow up and ask for your benchmark result.
Traditional annotation companies use humans for 100% of work. AI labeling companies use AI for 100% of work. We use each for what it is genuinely better at.
AI is better at: consistent application of clear rules at speed across large volumes. Humans are better at: nuanced judgment, cultural context, domain expertise, and catching the subtle failures that AI systems produce — sycophancy, hallucination, cultural misrepresentation.
The RLAIF pre-scorer handles the 70–90% of tasks with clear correct answers. Expert human annotators handle the 10–30% requiring genuine judgment. The QA layer runs on everything. The result: AI speed at human quality, at 35–45% lower cost than pure-human competitors.
Most annotation companies run one quality pass. We run three independently throughout the project. Each tier catches failure modes the others miss.
Most annotation companies deliver a data file and an invoice. We deliver three things on every project — because the QA report and data card are what turn annotated data into auditable training infrastructure.
The annotated dataset is delivered in your exact required format — JSONL for RLHF, CoNLL-2003 for NLP, COCO for images. Validation scripts check every field for completeness and encoding. If anything is malformed, it is caught before delivery, not after you try to load it into your training pipeline.
The QA report contains the actual numbers: Cohen's kappa by annotator pair and by task category, gold standard accuracy per annotator, batch error rates, number of tasks flagged and resolved, and a summary of any recalibration events that occurred during the project. These numbers are computed from Label Studio export data by automated scripts — not estimated, not reported by annotators themselves.
The data card is the nutritional label for your training data. It documents the composition of the dataset, the annotator demographics, the known limitations (if one category had lower agreement than others, we tell you which one and why), and the guidelines version used. It is what you show investors during due diligence, auditors during compliance review, and your own ML team when they ask how the training data was produced.
Two weeks after delivery, we schedule a 30-minute model benchmark follow-up call. If the data produced the expected improvement, we document it as your case study — evidence that no number we report on delivery can match.
Most annotation is treated as a one-time event. You pay for data, train your model, and whatever happens next is your problem. We build a continuous improvement loop into every engagement.
After three or four projects with us, our guidelines are tuned to exactly what your model needs, the annotator team knows your product context, and the data we produce is measurably better than what any new vendor could deliver on their first project.
Start the loop →Begin with a free 50-output audit. We return a sycophancy susceptibility score and hallucination rate in 5 working days. No cost, no commitment, no sales call required to start.