“Our labels are inconsistent, so the model is too.”
Two people label the same thing two different ways.
Human Operations for Better AI
AI is only as good as the data it learns from and the people who check it. We give you trained reviewers who label data, catch AI mistakes and feed corrections back, so your AI gets better every week.
Items labeled48k
AI outputs reviewed6.2k
Gold accuracy98%
Labeling queue72%
Human review queue45%
Evaluation run88%
1,140 corrections sent back for retraining
Where AI projects get stuck
“The AI is right most of the time, but the mistakes are costly.”
Nobody checks the risky answers before they go out.
“We don’t have time to review thousands of outputs.”
Engineers end up doing manual checks.
The answer is a trained human team with clear guidelines, test sets and a feedback loop. That is AI Operations.
Where people fit into your AI
Your AI handles the easy cases. When it is unsure, or the decision is sensitive, a trained person steps in. Every correction is saved, so your team can use it to improve the model.
Better AIevery cycle
- 01AI makes a decision
- 02Unsure or risky? Flag it
- 03A person reviews
- 04Decision or correction
- 05Feedback saved
- 06AI improves
Five AI Operations services
Data Annotation & Labeling
Accurate labels for text, images, audio and video.
Learn moreHuman-in-the-Loop
People who review, correct and approve AI decisions.
Learn moreAI Quality Assurance
Score AI answers for accuracy, safety and tone.
Learn moreAI Data Operations
Collect, clean and maintain your datasets.
Learn moreAI Agent Operations
Human backup and review for AI chatbots and agents.
Learn moreThe data types we handle
We only take on work we have the people, tools and process for.
We do not offer medical imaging, LiDAR or map data. These need specialist experts.
Text & chat
- Classification
- Sentiment
- Intent
- Entities
Images & video
- Tagging
- Bounding boxes
- Content review
Audio
- Transcription checks
- Tagging
Product data
- Categories
- Attributes
- Enrichment
How we make labels you can trust
- 1
Clear guidelines
We turn your rules into examples everyone can follow.
- 2
Trained and tested
Reviewers pass a test before touching real data.
- 3
Gold test sets
Hidden known answers measure accuracy every day.
- 4
Agreement checks
We compare reviewers to catch unclear rules.
- 5
Feedback loop
Errors lead to better guidelines, not just fixes.
How we work with your AI team
- Step 1
Agree what “good” means
We write labeling and review guidelines with your team, with examples of right and wrong.
Guidelines and gold examples
- Step 2
Pilot on a small dataset
A small team labels or reviews real data. We score every item.
Pilot dataset and accuracy report
- Step 3
Scale with quality checks
People are added only after accuracy meets the target you set.
Weekly quality report
- Step 4
Feed results back
Errors and unclear cases become guideline updates, so quality improves each cycle.
Change log and error analysis
Timings depend on volume and complexity. We agree them with you in writing before the pilot starts.
How we protect your data and your model
Restricted data accessWho sees which data is agreed in writing before work starts.
Layered reviewLabels are checked by a second person and sampled by a third.
Versioned guidelinesEvery rule change is dated, so results stay comparable.
Agreement scoresYou see how often reviewers agree, not just a pass mark.
Who you will work with
ML lead or data scientist
Cleaner training data and a clear error report to guide the next model.
Product owner
Evidence of how the AI performs on real cases before release.
Risk and privacy owner
Written rules on data handling and access.
Start with a data annotation pilot
Send us a sample of real data and your guidelines. We will label it, measure the quality and show you the results before you commit to more.
What you get
- Guideline review and questions list
- Labeled pilot dataset
- Accuracy and agreement scores
- List of unclear cases for your team
- Plan and quote to scale
Questions buyers ask before they sign
Still have a question? Ask us directly.
Can we keep our data inside our own tools?
Yes. We can work inside your annotation platform or environment, with access limited to what the task needs. Tell us your requirements early and we will say honestly whether we can meet them.
How do you measure label quality?
We score a sample of every batch against gold examples and track agreement between reviewers. You get the numbers each cycle.
Ready to build better operations
Tell us what you need to run, fix, or growWe will map out the right team and a pilot plan