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.
Reward-model quality is bounded by rater agreement. We run pods against a versioned rubric with weekly calibration, blind gold seeding and a documented escalation path for every ambiguity.
1.3×
Calibrated bench
Held against deployed demand
100%
KYC verified
Before task access is granted
Recruiting, vetting, training, scheduling, reviewing, auditing and reporting, run as one accountable system, inside your tooling wherever your policy requires it.
Marketplaces are scalable but unaccountable. In-house teams are accountable but unscalable. Between them sits the managed services firm. Vetted teams, owned QA, contractual accountability, elastic capacity. In AI data, that layer is still thin.
Expert marketplaces
Vetted individuals, client-managed
Newbieget Labs
Managed pods, owned QA, SLA-backed
Crowd platforms
Volume, high variance
Generalist managed ops
Process depth, not AI-native
We target the expert-tier, outcomes-delivered quadrant, where willingness to pay is highest and crowd models cannot follow.
Boundaries
No self-serve gig pool. Every contributor is sourced, tested, trained, calibrated and managed under a named lead.
We sell accountable deliverables under SLAs, not resumes at an hourly markup. Scope, acceptance criteria and rework terms are contractual.
We build no competing IP. There is no scenario where your unreleased capabilities become our product.
Marketplaces 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.
Every engagement runs the same pipeline. Standardisation is what lets a small leadership team run many programs at once without quality drifting between them.
We check the work against our service lines and against your data-handling constraints before anyone talks price. Work we cannot staff to the acceptance bar is declined at this stage rather than discovered at delivery.
A two-page feasibility memo with a staffing plan and a paid pilot quote. Acceptance criteria and rejection definitions are written down before signature. Ambiguity here is the single largest cause of failed engagements.
A named pod. Contributors, reviewers and a lead. Is drawn from the calibrated bench and matched on domain. Every member signs an engagement-specific NDA before task access is granted.
The pod trains on gold tasks and must clear the agreement gate on two consecutive calibration rounds before production begins. Clarification questions are logged and folded into the rubric, versioned.
Production runs against daily targets with a live quality dashboard shared with you. Contributors below the gold-task floor are moved to recalibration and replaced from the bench the same day.
Every item is peer-reviewed. A senior sample and all flagged items go to Tier 2. Ten percent is audited independently of the delivery line. Internal bar sits two points above yours.
The delivery package contains the work and the proof: per-item review trail, agreement data, gold-task results and defect taxonomy. You never have to ask what you received.
A retro with your ops lead, rubric proposals for the next version, and an unsolicited memo on the systematic model-failure patterns we observed. The insight memo is the part clients remember.
Roughly one in ten applicants reaches our bench. The ones who do are paid for their training, calibrated weekly against a versioned rubric, scored on blind gold tasks, and given a career ladder no gig platform offers. That is not an HR programme. It is the reason the numbers hold.
Contributor funnel
Campus networks, referrals, engineering and Kaggle communities, IIT/NIT/IIIT alumni groups.
Structured application plus an async, domain-matched skills test.
A paid trial batch on gold tasks and a live rubric interview.
KYC, NDA and a paid calibration week before any client work.
Skills-tagged, score-tracked and deployable. Roughly one in ten applicants.
End-to-end applicant pass rate
Screen, paid trial, live rubric interview, calibration week
Reviewer to contributor ratio
Invariant at every scale we operate
Blind gold seeding per queue
Indistinguishable from live work
Pod calibration sessions
Disagreement argued to a versioned rubric change
Every engagement is treated as if it covers unreleased model data, because it usually does. The compliance posture is built before it is asked for, so a security review is a document exchange rather than a project.
Nothing here is gated behind a sales call. Certifications in progress are labelled as in progress, because a procurement reviewer will find out either way.
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.
The delivery arrived with a per-item review trail and an audit sample. It was the first vendor batch we did not have to re-verify ourselves.
They logged three rubric ambiguities instead of guessing. That single behaviour told us more about the team than the acceptance rate did.
We asked them to audit another vendor’s output. They reported findings that were commercially inconvenient for them. We expanded the contract.