Services
From raw data to a model you can trust
Five service lines built on one discipline: a trained, full-time team and a QA process that scores every delivery — from a single annotation batch to fractional data leadership.
Data Annotation & Labeling
The foundational work, across every modality — image, video, 3D, and text — labeled to the standard your model needs, at the volume your timeline demands.
Image annotation
Bounding boxes, polygons, keypoints, and classification at scale.
Video annotation
Object tracking and temporal attributes across frame sequences.
Image & instance segmentation
Pixel-level and instance segmentation for computer vision models.
3D point-cloud labeling
LiDAR and radar annotation for robotics, AV, and mapping.
Text & NLP annotation
Classification, sentiment, and instruction-following labels for language models.
Data cleaning & structuring
Turning messy, inconsistent data into something a pipeline can trust.
Data Validation & Quality
Human verification that catches errors before they reach your model. This runs under every service line by default — see the full process below — and stands on its own for teams that need a second, independent QA pass on data labeled elsewhere.
Gold-standard & consensus review
Every batch checked against benchmarks and cross-annotator agreement.
Error analysis & correction
Systematic review that catches drift before it reaches your model.
Class distribution & bias checks
Flagging skew and gaps a spec sheet alone won't catch.
Accuracy & overlap scoring
IoU and accuracy metrics on every delivery, not a spot-checked sample.
Generative AI & LLM Data
The same annotation and QA discipline, applied to prompts, responses, and preferences instead of pixels — the data work behind a model that actually behaves in production.
RLHF & preference ranking
Domain-informed ranking of model outputs for reinforcement learning and DPO.
Prompt & response evaluation
Fact-checking, instruction-following review, and rewrites that raise response quality.
Red-teaming & edge cases
Targeted prompts that surface where a model breaks before your users do.
GenAI system design
Architecture for LLM and generative pipelines — retrieval, evaluation, guardrails.
Data Science, ML & Analytics
Advanced analytics and machine learning aimed at one thing: moving your business toward a measurable outcome, not a research demo that stalls at the prototype stage.
ML proofs of concept
Fast, scoped demos that test feasibility before you commit to a build.
Analytics & data science
Turning operational data into decisions, not just dashboards.
Model evaluation
Structured testing of model output quality against your own criteria.
Capability Building
For teams that need senior data leadership or engineering support without a full-time hire — we step in as an extension of your team.
Fractional CDAO
Senior data & analytics leadership on a part-time basis — strategy, governance, roadmap.
DevOps support
Pipeline, infrastructure, and workflow support for data and ML systems.
Quality control
Quality isn't a promise here — it's a scored process
This is the part enterprise buyers usually ask about in diligence. Here's the actual mechanics.
Task-specific, before live data
Annotators are trained on your task type and guidelines before they touch a single real record.
Higher stakes, proven annotators
Workers are ranked against task benchmarks, so the hardest edge cases go to the most qualified people.
Tracked through delivery
Every response is scored against your QA criteria on an ongoing basis, not sampled once at the end.
Pricing
How pricing works
We don't publish a rate card because scope varies too much to make one honest — but here's what actually drives the number, so you can size a project before you talk to us.
| Factor | What it changes |
|---|---|
| Task complexity | Simple classification prices very differently from pixel-level segmentation or point-cloud labeling. |
| Volume | Larger, ongoing volumes bring the per-unit cost down — we price project-based work and standing pipelines differently. |
| Turnaround | Standard timelines cost less than compressed ones; we'll always tell you which lever moves the price. |
| QA depth | Higher review thresholds (multi-pass scoring, senior QA) add cost but reduce rework downstream. |
Not sure which service fits?
Tell us what you're trying to ship — we'll tell you which of these gets you there.