Quality
Written guidelines, calibrated reviewers and a QA gate on every batch.
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AI data & talent
The data and talent behind modern AI.
Annotate. Rank. Review. Deliver. Repeat.
Data annotation, RLHF and embedded specialists for teams training and shipping models, all run by one accountable team.
10×
Faster labelling throughput on a crop-imagery pipeline (internal benchmark)
Every batch
Passes a QA gate before it reaches your pipeline
One team
For annotation, RLHF and engineering talent
Why Vyntex
Built for quality, not just volume.
Labels are only useful if you can trust them. Every engagement is set up so quality is designed in, not inspected at the end.
Speed
Batch workflows and fast feedback loops cut rework, so throughput climbs without cutting corners.
Flexibility
Start with a pilot batch, then scale the team up or down as your roadmap moves.
Clean delivery
Outputs in the format your pipeline expects, versioned and ready to train on.
Case study · Confidential client
A crop-labelling pipeline that runs 10× faster.
We rebuilt an agricultural imagery labelling workflow around clear guidelines, batch review and QA checkpoints, cutting rework and turnaround for the ML team downstream.
- Throughput vs. the previous process
- ~10×
- Throughput vs. the previous process
- Checkpoints on every batch
- QA
- Checkpoints on every batch
RLHF & human feedback
Human judgement, built into your model.
Reviewers compare, rank and rate model outputs against your rubric, and write the demonstrations your model learns from.
Preference ranking · task 2,184QA passed
P
Explain why crop rotation improves soil health, in two sentences.
Response APreferred
Rotating crops varies which nutrients each season draws from the soil, so none is exhausted. It also breaks the pest and disease cycles that build up under a single crop.
Response BRank 2
Crop rotation is good for soil because it makes the soil healthier over time and helps farmers grow more.
Helpfulness
Accuracy
Safety
- 01
Preference ranking
Side-by-side comparisons of model responses
- 02
Rubric rating
Scores for helpfulness, accuracy and safety
- 03
Demonstrations (SFT)
Expert-written prompt and response pairs
- 04
Evaluation sets
Hard, varied prompts that expose weak spots
How it works
Scope. Calibrate. Produce. Deliver.
Every engagement runs the same loop, and each round sharpens the next.
- 1
Scope
We agree the task, the guidelines and what a correct label looks like.
- 2
Calibrate
A pilot batch aligns reviewers with your expectations before we scale.
- 3
Produce
Batches move through annotation and a QA gate, with feedback loops that cut rework.
- 4
Deliver
Data lands in your format and schema, ready for the next training run.
Data inputs
Supported data types
Image annotation
- Bounding boxes
- Polygons and segmentation
- Keypoints
- Image classification
Talent
Specialists, embedded in your team.
Need people, not just labels? We place vetted engineers and data specialists inside your team, working in your tools and your rituals.
- Start with one specialist or a full pod
- Managed by us, directed by you
- Scale up or down as the roadmap moves
Your teamAnnotation leadsQA reviewersData engineersML engineersFull-stack engineers
Tell us aboutyour data.
Share the task, the volume and the timeline. We will come back with a pilot plan.
Talk to our team