Computer Vision
2D/3D boxes, polygons, segmentation masks, and temporal tracking.
Trainingdata, calibratedfor accuracy
Guideline-calibrated annotation, RLHF, and QA—delivered by dedicated specialist pods.
Open capacity
Launch aqueue
Your ontology, your platform, our team.
Engagement model
Start with a call or message
The way training data should'vebeen built in the first place
Bounding boxesVideo trackingPolygonsSemantic masksKeypoints
NER & taxonomySentimentClassificationText curationAudio
LLM evaluationRLHF rankingRed-teamingMulti-tier QAHuman-in-the-loop
Language & NLP
Entity extraction, intent classification, and domain-calibrated text curation.
LLM & GenAI
SFT datasets, RLHF preference ranking, red-teaming, and rubric evaluation.
Every engagement begins with a calibrated ontology—and holds to it. Ambiguities are surfaced and resolved, never guessed. A lead specialist or a dedicated 1–5 person pod runs the queue without daily management, and multi-tier QA delivers production-ready ground truth to your pipeline.
Operational standard
It's “pipeline-ready” better
Replace fragmented crowdsourcing and high-overhead vendors with a dedicated specialist practice.
Shared queue board
Prioritize batches, track velocity, and monitor QA status in one workspace.
Ontology-calibrated
Strict adherence to your schema, with documented edge-case escalation logs.
Autonomous execution
Define your rubric once. Batches move ahead without daily oversight.
Multi-tier verification
Every batch gets a specialist audit and a consensus check before export.
Elastic 1–5 pod capacity
Scale from one lead specialist to a calibrated five-person pod as volume grows.
Native tool integration
We work inside your stack: Labelbox, SuperAnnotate, CVAT, V7, or custom tools.
"Ground truth engineered to your ontology, never approximated."
"Dedicated pods that plug into agency and enterprise pipelines."
Computer Vision & SpatialDense polygons, semantic masks, and video tracking
99.4%+ QA Consensus
LLM & NLP AlignmentRLHF ranking, SFT curation, and domain NER
Rubric-Verified OutputReference datasets & QA
Production-grade annotations calibrated for high-stakes models.
Request sample schemas
Engagement model
One practice,
flexible engagement
Dedicated Pod & Studio
Direct or agency partner
1–5 calibrated specialists
Dedicated
pod
Crowdsourced
Included
- Dedicated 1–5 specialist pod
- Ontology-calibrated labeling
- Vision, NLP, audio & multimodal
- LLM evaluation, SFT & RLHF
- Multi-tier QA & consensus audit
- Native in your platform & stack
- Direct, retainer, or agency partner
Elastic queue control
Scale capacity up or pause between model training cycles—your calibrated ontology stays intact.
Start with a calibration pilot
Align on your schema, sample batch, or partnership via a 15-minute call or async brief.
Frequently asked questions
How fast are annotated batches delivered?
Calibration batches and active queues typically return within 24 to 48 business hours. Larger datasets and multi-stage RLHF work move on agreed daily throughput, so verified batches keep reaching your pipeline.
How does onboarding and calibration work?
We review your schema, edge-case rules, and model objectives, then configure the shared queue. A short calibration batch locks alignment, and production starts without daily oversight.
Who handles annotation and quality review?
Work stays with a senior specialist, expanded to a calibrated pod of up to five when volume or domain complexity requires it. Nothing is sent to anonymous crowds. Every batch passes an independent QA review before export.
Can agencies and enterprises partner?
Yes. Alongside direct work with machine learning teams, the practice collaborates with AI consultancies, agencies, corporate R&D groups, and independent specialists—as an embedded unit, an overflow pod, or a longer delivery partner.
How does pausing or scaling a queue work?
Pause when a dataset is complete and your team is evaluating checkpoints. Resume when the next cycle is ready. Guidelines, tooling, and the calibrated team stay in place.
How are large datasets and modalities handled?
Large sets are split into milestone batches. The pod scales from one specialist to five annotators and reviewers, with verified partitions delivered every 24 to 48 hours until the corpus is complete.
Which platforms and formats do you support?
We work in your stack, including Labelbox, SuperAnnotate, Scale, V7, CVAT, Label Studio, Encord, or an internal tool. Exports include JSON, COCO, YOLO, Pascal VOC, CoNLL, Parquet, or a custom schema, under NDA.
How are accuracy and edge cases handled?
Edge cases are logged and resolved against your rubric, not guessed. Each batch is checked before delivery. If a label misses the specification, we recalibrate and correct it at no additional cost.