Newbieget Labs is the managed execution partner for AI labs and data platforms. Complete, quality-assured evaluation and training-data projects. Delivered under SLA, with the audit attached.
Produce
1,000
Contributors work the rubric
Named podTier 1
1,000
Every item peer reviewed
100%Tier 2
310
Senior review plus all flags
25–35%Audit
100
Independent of delivery
10%Deliver
970
Review trail attached
+ evidenceRework loop. Items failing Tier 1 return to the contributor with tagged findings. Batches predicted to miss your bar are reworked before delivery, at our cost.
Acceptance rate SLA
Fee-at-risk option available
Inter-rater agreement gate
Cleared before production starts
First-pass peer review
Every item reviewed at least once
Escaped defects / 1,000
Client-found, tracked per batch
Built for the teams defining frontier AI
Client logos withheld under NDA · category placeholders shown
Acceptance rate
Written into every SOW
Agreement gate
Cleared before production
First-pass review
Every item, no sampling
Applicant pass rate
Screen, trial, calibration
Reviewer ratio
Invariant at any scale
Escaped defects / 1k
Client-found, per batch
Azure marks the system: acceptance, agreement, review coverage. Violet marks the people who produce it.
The companies supplying that judgment are constrained by quality, not demand. Newbieget Labs wins by making quality the product. Measured, audited and delivered as evidence with every batch.
Frontier AI development is bottlenecked not by compute or capital, but by trustworthy human judgment at scale.
Individual contributors on marketplaces vary wildly in skill, effort and honesty.
20–40% of budget spent on redundancy and rework
Gig contributors leave mid-project. Calibration is lost and ramp-up repeats endlessly.
Schedule slip and quality regression mid-dataset
Marketplaces deliver raw output, not verified output, so labs staff large internal review teams.
Researcher time diverted to reviewing
Thousands of anonymous individuals touching confidential model data. NDA enforcement is impractical.
Leak risk on unreleased capabilities and benchmarks
Agentic evaluations and repository-grade review need real engineers, not raters.
Roadmap delay on evaluation-gated releases
20–40%
of budget spent on redundancy and rework, because freelancer variance means individual contributors on marketplaces vary wildly in skill, effort and honesty.
Gig contributors leave mid-project. Calibration is lost and ramp-up repeats endlessly.
Schedule slip and quality regression mid-dataset
Marketplaces deliver raw output, not verified output, so labs staff large internal review teams.
Researcher time diverted to reviewing
Thousands of anonymous individuals touching confidential model data. NDA enforcement is impractical.
Leak risk on unreleased capabilities and benchmarks
Agentic evaluations and repository-grade review need real engineers, not raters.
Roadmap delay on evaluation-gated releases
None of this is a sourcing problem. It is a management problem, and it is the one Newbieget Labs is built to absorb.
Rubric-anchored scoring of model behaviour, executed by raters who understand the domain and calibrated against your own acceptance bar before a single production item is scored.
21/25
Item score
Mandatory dimensions all cleared
Any mandatory dimension below 3 caps the item regardless of the rest. That rule is written into the SOW, not left to reviewer judgment.
Agentic evaluation is where crowd models break. Task authoring, environment engineering and trajectory grading are engineering disciplines, and they are the founding team's specialty.
A run that ends green can still be wrong in the middle. Scoring the trajectory is what separates an agent benchmark from a unit test.
Two more service lines sit behind this page: human data and RLHF, and AI data operations for teams who already run a pipeline.
All four service linesMarketplaces deliver raw output and leave verification with the buyer. We deliver verified output with the evidence attached.
Review coverage
Random 10% plus risk-weighted sample
25–35% sample plus all flagged items
100% of contributor output
Self-check against rubric checklist before submit
Acceptance rate
Share of delivered items accepted by the client without rework.
First-pass yield
Share of contributor items passing Tier 1 unchanged.
Inter-rater agreement
Cohen’s κ / Krippendorff’s α on the double-rated sample.
Escaped-defect rate
Client-found defects per 1,000 delivered items.
Gold-task accuracy
Contributor score on blind, seeded known-answer tasks.
Review accuracy
Tier 1 decisions overturned at Tier 2 or Tier 3.
Rule of the house
No batch leaves Newbieget Labs below the client’s acceptance bar. If internal QA predicts a miss, the batch is reworked before delivery. Schedule slips are negotiable. Quality misses are not.
Documented engagement patterns with illustrative volumes. Named client case studies publish once engagements clear for publication.
The standard six-week play: pilot scoped against the client’s own acceptance bar, pod calibrated to an agreement gate before production, batch delivered with full QA evidence, converted to a dedicated pod inside two weeks.
Agentic evaluation is where crowd models break. Task authoring, Dockerised environment engineering, verified gold solutions and step-level trajectory grading, run as one accountable workstream.
A review and audit layer operated over a third party’s delivery: the client’s own crowd, or another vendor. We publish the metrics either way, including the ones that are inconvenient.
Including the answers that are inconvenient for us, because a buyer finds those out anyway and would rather find them out now.
All questionsWith a paid pilot, usually two weeks. You send a rubric and the acceptance bar you already use. We return a two-page feasibility memo, a staffing plan and a fixed quote. If we miss the bar, the rework is at our cost.
It does not ship. Internal QA runs two points above your bar, so a predicted miss is reworked before delivery rather than after. If something escapes and you find it, the rework is at our cost within the window written into the SOW.
Four models: fixed-scope project delivery, a monthly retainer for a dedicated pod, per-item or retainer pricing for QA over someone else’s output, and project pricing for benchmark engineering. Rates depend on domain, expertise tier and volume, and are quoted per engagement.
Work is performed inside your environment by default, so the data never leaves your systems. Where we do host, it is encrypted at rest and in transit under least-privilege role-based access with 90-day access reviews and deletion certificates at project close.
Yes, and it is often the best way to start. We publish the metrics either way, including findings that are commercially inconvenient for us.
Not yet. The security policy pack, incident-response plan and pre-drafted vendor questionnaire responses exist today, and SOC 2 Type I is on the roadmap with Type II following. We would rather state the position plainly than imply a certification we do not hold.