We work with foundation model labs from pretraining to alignment — sourcing original multilingual data, running RLHF programs at scale, and building evaluation harnesses that measure what actually matters.
Foundation model teams have unique data needs: original corpora the web doesn't have, high-quality alignment data that captures nuanced human preference, and evaluation suites that haven't been contaminated by training data.
We're already embedded with frontier labs. Our annotators are research-grade experts — linguists, domain PhDs, safety researchers — managed by teams who understand what an alignment program actually looks like.
Original-source data across 90+ languages, including low-resource dialects the open web barely covers. Fully licensed, provenance-tracked, and delivered with demographic metadata.
Essays, reports, instructional text, creative writing across domains.
Multi-turn dialogue between matched native speakers, controlled for topic diversity.
Hours of captured speech with aligned transcripts, diarization, and acoustic variety.
Human-authored code with natural-language explanations and chain-of-thought traces.
Our alignment teams have shipped RLHF programs at frontier labs. We run preference labeling, critique, rewriting, and red-teaming workflows calibrated against your rubric and rater-drift monitored in real time.
Pairwise and multi-way preference labels for reward model training.
Structured written critiques for RLHF and RLAIF pipelines.
Ideal-response rewrites by domain experts for SFT and DPO datasets.
Targeted harm probes, jailbreaks, and edge-case discovery.
We build custom evaluation suites that have never touched a training crawler. Statistical rigor, blind rating, and expert-grade adjudication — so your benchmarks actually mean something.
Benchmarks written specifically for you, never published, fully provenance-logged.
Granular breakdowns across reasoning, safety, factuality, creativity, code.
Blind model comparisons against competitors or internal variants.
Running eval programs that flag quality drops on every model update.
Pre-launch safety audits, policy-compliance evaluation, and structured adversarial testing. Built by researchers who understand modern AI threat models.
Policy-compliance scoring against your own harm taxonomy.
Trained adversarial annotators running targeted probe programs.
Systematic search for prompt injections, PII leakage, and harmful generation.
Demographic and cultural bias scoring across multilingual evals.
Original multilingual data that expands your training distribution beyond scraped web.
Running full alignment programs as an extension of your research team.
Critique, revision, and principle-grounded preference data at scale.
Contamination-free benchmarks for every new capability you ship.
Pre-deployment red-teaming and policy-compliance scoring.
Building representation for languages with minimal internet presence.
Yes. We routinely work under strict NDAs with dedicated clean-room teams, air-gapped environments, and named-POC-only access. Never had a leak.
Custom datasets written specifically for your eval — never published, never indexed, delivered with full provenance logs proving they never hit a training crawl.
Foundation-lab engagements typically start at $250K. We take on smaller mission-aligned work for academic research or novel alignment programs.
Yes. Our largest single-program team was 340 annotators across 6 regions, with ramp-up in 5 business days.
For certain specialist panels (e.g., security researchers, rare-language experts), yes. Discuss in the first call.
Tell us what you're training, aligning, or evaluating. We'll map a delivery plan, staffing model, and timeline within one working week.