Six services. Each one addresses a specific failure point in the data pipeline for LLM and VLM development. We do not offer a general annotation platform. We build for your domain.
Real-world data is expensive, unbalanced, and often impossible to collect at the scale modern AI training demands. Our synthetic data programs close structural coverage gaps without sacrificing domain fidelity.
We combine generative modeling, constraint-based sampling, and expert validation. The result is data that behaves like the real thing — because specialists sign off on it before it leaves our pipeline.
Instruction-tuning corpora
Structured tabular data
Vision-language pairs
Time-series sequences
Multi-turn dialogues
Code & reasoning traces
25+ additional specializations. Ask us about your domain.
When your model needs to reason like a specialist, it needs to learn from one. Crowdsourced annotation cannot replicate the judgment of a physician deciding between two diagnoses, or an attorney reading contract risk across jurisdictions.
We run structured elicitation programs with credentialed, active practitioners — not academics, not retired professionals. Sessions are designed around your model's specific gaps, then transcribed, tagged, and delivered in your format.
The pipeline behind the data. We design and operate the annotation infrastructure — not just deliver output from it.
Custom workflow architecture for multi-step, multi-label, and multi-modal tasks. IAA measurement built in from day one.
End-to-end management of domain-expert annotators as a managed service — recruiting, vetting, training, scheduling.
Two-tier review with configurable acceptance thresholds and automated consistency flagging tuned to your sensitivity level.
Closed-loop systems that incorporate training signals to refine data quality across production cycles — not one-shot batches.
JSONL, CSV, Parquet, HDF5, or custom schemas. Transfer via encrypted cloud storage or direct API endpoint.
Full documentation of data origin, annotation decisions, and version history for audit, reproducibility, and regulatory review.
Independent benchmarking against the criteria that matter to your users — not the metrics that look good in a press release. We evaluate across accuracy, calibration, safety, and domain-specific competency.
Our evaluation programs are designed by practitioners who have worked on real AI deployments. They know what breaks in production, which is exactly what we test for.
Domain-specific precision, recall, and confidence calibration.
Adversarial probing, jailbreak resistance, output risk scoring.
Demographic parity, representation audits, equalized odds.
Expert-reviewed performance across your target use cases.
Findings written for non-technical stakeholders.
Full metric breakdowns in machine-readable format.
Specific recommendations to close identified gaps.
Optional follow-up after remediation to confirm progress.
Human preference collection built around expert judgment, not crowd consensus. The difference shows up in model behavior at the edges — which is where it matters.
Side-by-side comparison labeling by domain experts who can explain why one response is better — not just which one they clicked.
Absolute quality scoring on configurable rubrics — helpfulness, accuracy, safety, tone — with calibration across your annotator panel.
Expert rewriting of model outputs to produce ideal reference responses for SFT and DPO training pipelines.
Adversarial prompting by domain experts to surface failure modes and jailbreaks before they reach production users.
Principle-based critique and revision data for CAI frameworks, including multi-step feedback chains and policy compliance annotation.
Ongoing preference collection that evolves with your model across training iterations — not a one-time batch that ages out.
Enterprise AI deployments need data that is defensible, not just accurate. That means documentation your legal, compliance, and risk teams can review — and processes that hold up under regulatory scrutiny.
We treat governance as a core engineering practice. Every dataset we deliver includes provenance records, quality certification, and contributor agreements.
Start with a discovery call. We'll map your data gaps and recommend a specific approach — no package upselling.
Schedule a Discovery Call