End-to-end
data operations.
Five stages, one operating contract. Collection, annotation, evaluation, research and deployment are engageable independently — the gain comes from running them as a closed loop.
A single closed loop, operated end-to-end.
- 01
Collection
Custom sourcing · synthetic generation · human-authored data
Deep dive - 02
Annotation
Text · image · video · audio — multi-layer review
Deep dive - 03
Evaluation
Benchmarks · rubrics · adversarial probes
Deep dive - 04
Research
Published methodology · agent rubrics · synthetic data
Deep dive - 05
Deployment
Custom solutions · platform builds · enterprise scale
Deep dive
Evaluation and research feed back upstream — rubric edits land in collection before they ever reach a release.
Each stage, in operating detail.
- Stage 01 / 05
Collection
Custom sourcing · synthetic generation · human-authored data
Datasets are commissioned and built, not bought. Every brief opens with a methodology review and closes with a measurement plan, so the corpora we ship are scoped to the model behaviour they exist to change.
A vetted global contributor network pairs with methodology-reviewed synthetic generation. Sourcing, calibration, and quality assurance share a single rubric — the same one the downstream evaluation stage will grade against.
Deliverables
- Source dataset with versioned provenance
- Methodology note + measurement plan
- Calibration set ready for evaluation
What we ship
Custom dataset sourcing
Commissioned, not catalogued — scoped to your model behaviour.
Human data collection
Vetted contributor network across 40+ countries; multi-stage screening.
Synthetic generation
Methodology-reviewed pipelines; every dataset ships with a provenance file.
Global contributor network
200+ domain experts — coding, STEM, multimodal, domain.
Quality assurance
Multi-layer audit, sampled IAA, adversarial probes baked in.
Operating numbers
- DomainsCoding · STEM · Multimodal · Domain
- Network2,400+ vetted contributors
- ThroughputUp to 184k units / hour
- Stage 02 / 05
Annotation
Text · image · video · audio — multi-layer review
Annotation is treated as a measurement problem. Multi-stage review, inter-annotator agreement floors, and a closed feedback loop into source-stage rubrics keep label quality from drifting between batches.
Every modality runs through a domain-expert validation pass. Calibration cycles publish back into Collection so rubric edits land at source, not downstream of a noisy label set.
Deliverables
- Labelled corpus + per-class agreement report
- Rubric revisions, versioned and traceable
- Re-calibration deltas published to Collection
What we ship
Text annotation
Classification, NER, instruction-tuning, preference data.
Image annotation
Bounding boxes, segmentation, detection, dense captioning.
Video annotation
Tracking, events, activity recognition, temporal grounding.
Audio annotation
Transcription, speaker diarization, intent + emotion.
Multi-layer review
Domain-expert validation against published rubrics.
Operating numbers
- ModalitiesText · Image · Video · Audio
- Review depthMulti-layer + domain expert
- Quality floor96.4% IAA
- Stage 03 / 05
Evaluation
Benchmarks · rubrics · adversarial probes
Evaluation is a first-class artifact, not a release gate. Benchmarks, rubrics, and adversarial probes run per-commit, nightly, and at release — with traces stored against every model version we touch.
Coverage spans capability, safety, and alignment. Reporting drops out as per-task trajectories so regressions are diagnosable, not just visible — and so the same rubrics can return upstream into annotation.
Deliverables
- Evaluation harness wired to CI
- Versioned rubric + adversarial test bank
- Trajectory reports per model release
What we ship
Capability benchmarks
Static + dynamic suites across reasoning, code, multimodal.
Multi-rubric agent grading
Long-horizon trajectories scored at every step, not just the outcome.
Adversarial probes
Targeted red-team test sets built per deployment.
Safety + alignment audits
Capability, safety, and alignment in one report.
Per-commit eval harnesses
Hooked into your CI; failures surface against named rubrics.
Operating numbers
- CadencePer-commit · nightly · release
- CoverageCapability · safety · alignment
- ReportingPer-task trajectories
- Stage 04 / 05
Research
Published methodology · agent rubrics · synthetic data
Applied research informs every stage upstream. Papers, technical reports, and methodology notes are written by the same teams operating the pipelines they describe — so what gets published is what production already runs on.
Quarterly publication cadence, with frontier labs and sovereign research as co-authors where the work warrants it. Internal methodology notes feed Collection and Annotation rubrics before they reach a paper.
Deliverables
- Published paper / report / methodology note
- Reproduction artifacts (data, code, rubrics)
- Briefing pack for partner research teams
What we ship
Papers
Peer-reviewed contributions, written by operators.
Technical reports
Internal methodology, declassified for partners.
Whitepapers
Practitioner guides — what to measure, and why.
Methodology notes
Quarterly drops; the working notes behind the papers.
Research partnerships
Frontier labs · sovereign research — co-published.
Operating numbers
- OutputPapers · reports · whitepapers
- CadenceQuarterly publication
- PartnersFrontier labs · sovereign research
- Stage 05 / 05
Deployment
Custom solutions · platform builds · enterprise scale
Bespoke AI, software, and platform builds from prototype to enterprise scale, with operations bundled. Outcomes are contracted — not deliverables — so pilots move into production on a single contract.
Each engagement runs as a joint team of operations, research, and engineering. Pricing is outcome-based where it fits; pilot to scale is scoped to land in 90 days.
Deliverables
- Production-grade build + runbook
- Joint operations contract, ops desk attached
- Outcome-based pricing schedule (where applicable)
What we ship
AI solutions
Agents and retrieval, scoped to the workflow they replace.
Software development
Internal tooling at scale — built where buying doesn't fit.
Platform development
Vertical platforms with our intelligence layer plugged in.
Research partnerships
Co-published methodology, productized for your stack.
Operations contracted
Run-side bundled — we keep what we ship operating.
Operating numbers
- EngagementPilot → scale in 90 days
- TeamOps + research + engineering
- PricingOutcome-based available
The numbers behind the loop.
Layers shipped YTD
Annotation layers delivered across all engagements.
Domain experts
Vetted across coding, STEM, multimodal and domain.
Agreement floor
Inter-annotator agreement minimum, per modality.
Pilot to scale
From scoping note to production-grade build.
Operator response
Operations lead assigned after first contact.
Throughput ceiling
Units processed at peak network capacity.
From scoping note to production, in four moves.
Engagements are scoped to land. Operations replies within one business day, a measurement plan goes back inside a week, and outcomes — not deliverables — are what we contract.
- 01Scoping noteOperations replies within one business day with a scoped pilot, a measurement plan, and a price band.
- 02PilotA focused 4–6 week pilot run by a joint team of operations, research and engineering. Methodology published at the end.
- 03ScalePilot moves into production on the same contract. Ops desk stays attached; rubrics stay published.
- 04RunWe operate what we ship. Outcome-based pricing available; quarterly methodology review baked in.
