“Our AI is confident and wrong.”
Customers see the mistake before we do.
Human-in-the-Loop Operations
AI is fast, but it still makes mistakes, and some mistakes are expensive. We add trained people at the points where your AI is unsure or the decision matters. They check, correct and approve, and every correction helps your AI get better.
AI output“Refund approved: item arrived damaged”
Confidence0.62
Below the 0.80 threshold Sent to a trained reviewerCorrected: partial refundFeedback logged
AI that runs unchecked will eventually get something wrong
“We do not know when to step in.”
Nobody has defined which cases need a person.
“Corrections are made and then lost.”
The same error appears again next month.
We put trained people at the moments that matter and turn every correction into an improvement.
Six moments your AI needs a person
Low confidence
The AI isn’t sure it is right.
Sensitive cases
The decision affects someone’s money, safety or account.
Policy grey areas
The rules don’t give a clear answer.
Tricky language
Slang, sarcasm or local context the AI misses.
Customer-facing answers
What the AI says reflects on your brand.
Exceptions
Cases that don’t fit any normal pattern.
The human-in-the-loop cycle
You set the rules for when the AI hands over. Our team reviews those cases within an agreed time. Corrections are saved in a format your engineers can use to retrain the model.
Fewer mistakeseach cycle
- 01AI decides
- 02Rule check
- 03Human review
- 04Correct or approve
- 05Save feedback
- 06Retrain
Where clients use human review
We do not review healthcare or financial risk decisions without domain experts and a compliance framework.
- Chatbot escalations
- AI answer review
- AI-flagged content
- Fraud alert review support
- Search result checks
- Product classification
- AI exceptions
Know exactly how your AI is doing
Volume reviewedHow many AI decisions people checked.
AI accuracyHow often the AI was right.
Error typesThe kinds of mistakes it makes most.
TurnaroundHow fast reviews were completed.
Escalation rateCases passed on to your team.
ReworkItems that needed a second look.
Set up in four steps
- 1
Map decision points
Where does your AI hand over, and why?
- 2
Write the guide
Clear rules and examples for reviewers.
- 3
Train and test
Reviewers pass a test on real cases.
- 4
Pilot and tune
Run on live cases and adjust the thresholds.
How we set up human review
- Step 1
Choose the moments
Together we decide which cases need a person, such as low confidence, high risk or complaints.
Review triggers
- Step 2
Write the review guide
What to check, what to change and when to escalate.
Reviewer guide
- Step 3
Run with a small team
Reviewers handle a slice of live cases. We measure speed and accuracy.
Pilot results
- Step 4
Close the loop
Corrections are returned to your team in a format you can use for the next release.
Correction dataset and monthly summary
Timings depend on volume and complexity. We agree them with you in writing before the pilot starts.
How we keep the loop reliable
Clear triggersEveryone knows which cases go to a person and why.
Response timesReview turnaround agreed and reported.
Reviewer QAA sample of reviews is checked independently.
Learning loopEach correction is logged and can feed retraining.
Who benefits on your side
AI product owner
Safer launches and evidence of real-world performance.
Risk owner
A record that a person checked the high-risk cases.
Customer experience lead
Fewer customers stuck with a wrong or unhelpful answer.
Straight answers
Still have a question? Ask us directly.
How fast do reviews happen?
We agree turnaround times per case type, based on how urgent each one is.
What format is the feedback in?
Whatever your team can use: labeled corrections in your tool, a data export or a structured report.
Which decisions can your reviewers make?
Those you define in the guide. We do not review healthcare or financial risk decisions without domain experts and a compliance framework.
Will this slow our AI down?
Only on the cases you choose. Most answers go straight through, and people handle the exceptions.
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