Scenario
AI + human operations for review and exception queues
Published · Last updated
Direct answer
Actigy BPO provides human-in-the-loop operations around an existing AI workflow. Reviewers check defined outputs, record corrections and route unresolved cases to your decision-maker. Your team keeps the model, policy and final decisions. Start with one review queue and a paid pilot before adding volume.
Describe the workflow, queue volume and review rules. Do not send production data or personal records through the enquiry form.
The buyer problem
Your AI workflow creates work that still needs a person
A document extractor leaves fields unresolved. A support assistant drafts a reply that needs a policy check. A classification tool sends uncertain items to a queue. These are different review tasks; they need different instructions and decision owners.
This model fits teams that have approved tools and rules but lack capacity to work their review queues. First, measure new arrivals, review time and unresolved cases. Do not assume that an industry automation rate describes your own process.
- Exceptions wait because no team owns the next step.
- Engineers spend time on repeatable checks that have written rules.
- Reviewers disagree because examples or escalation rules are unclear.
- A volume target hides rework and cases awaiting a client decision.
A queue with no agreed policy needs policy work first. Extra reviewers cannot decide your risk appetite or make an unreliable model safe by default.
A defined handoff
Give every reviewed item a clear next step
Agree which records reviewers may correct and which must stop for client review. Keep the source record, relevant output, rule version and review notes together in your approved system.
| Review state | Operator action | Client responsibility |
|---|---|---|
| Meets the written rule | Record the check and route the item to the approved next step. | Define the rule and whether further approval is required. |
| Can be corrected within scope | Correct the permitted field or record, retain the reason and send it through the agreed QA check. | Approve which changes operators may make. |
| Missing evidence or policy exception | Record the gap, stop the affected step and send it to the named reviewer. | Resolve the exception or request more evidence. |
Review completion is not model validation, customer acceptance, a clinical decision or approval of a payment. Those decisions stay with your authorized team. A low-confidence score alone does not replace the written escalation rules.
Choose the review task, not a generic AI BPO label
Live exception handling
Work a defined production queue. Check evidence, make permitted corrections and route open questions. Agree whether review happens before or after an output reaches a user.
Output evaluation
Score test responses against a client rubric and record supporting evidence. Use human LLM evaluation when the job is to assess outputs rather than run a live queue.
Annotation and preference data
Label source records or compare model responses under written guidelines. See data annotation operations and RLHF human-feedback services. Your ML team decides how to use accepted data.
These tasks can share a delivery plan, but they should not share a vague completion metric. A corrected record, a scored response and an accepted preference pair are different outputs.
Pilot before scale
Test the human review queue before adding capacity
- Map one queue. Describe its inputs, approved tools, daily arrivals and next decision owner.
- Set the rules. Agree access, examples, permitted corrections, stop conditions and QA checks.
- Run a paid pilot. Include unclear records and exceptions, not only easy cases. Calibrate reviewers before expanding the batch.
- Review the evidence. Measure accepted work, handling time, repeat defects and cases waiting for your team. Include retained management time in the cost comparison.
- Approve the next scope. Add volume only after your team accepts the pilot against the agreed thresholds.
Quotes are per full-time equivalent (FTE) by role. Volume, task time, review depth and working hours inform the staffing plan; they are not a published per-item price. See Actigy BPO's process audit and paid-pilot method.
Delivery teams are in Bulgaria, Romania, Poland and Ukraine. Confirm the proposed location, languages, hours and data-processing terms. ISO 9001 and SOC 2 alignment describes the stated approach, not certification or an independent audit.
When is Actigy BPO a fit for human-in-the-loop operations?
Best-fit conditions
- Your team owns an existing AI workflow and its operating rules.
- There is repeatable review work with measurable arrivals and handling time.
- A named client owner can resolve policy and evidence gaps.
- You can approve access and test difficult cases in a paid pilot.
Not a fit for this service
- You need a vendor to build, train or deploy the model.
- You want to transfer final policy, risk or regulated decisions.
- You have no defined review task or cannot permit the proposed data access.
- You need guaranteed error-free output or instant unplanned capacity.
If your own manager will direct the reviewers each day, compare client-directed operations outstaffing. Agree who owns daily QA rather than assuming every staffing contract includes a managed review service.
Read the adjacent annotation and feedback case
Actigy BPO's published AI case describes annotation, calibration and human-feedback work. It is anonymized and self-reported, not independently audited. It supports that process experience, not a guaranteed result for your live AI queue.
FAQ
Questions about AI + human operations
Does this service build or train AI models?
No. Actigy BPO provides human operations around client-owned models. Your team keeps model architecture, training, validation and deployment. Reviewers handle the agreed annotation, output checks or exception queue.
How is work split between AI and human reviewers?
There is no fixed split. Observed queue volume, task time, QA needs and staffed hours determine the review team's scope. A paid pilot tests those assumptions before more work is added.
How is managed review different from staff augmentation?
In managed review, the provider runs the defined queue and agreed delivery checks. With outstaffing, your manager directs the operators and owns daily review and acceptance. In both models, you retain policy and final decisions. The written scope must name each owner.
Can human-in-the-loop review run in production?
Yes, Actigy BPO can scope live review in your approved systems. Agree which outputs are checked, when they may proceed and who receives unresolved cases. Human review does not guarantee that every output is correct or safe.
Where are reviewers based, and how are controls agreed?
Actigy BPO has delivery teams in Bulgaria, Romania, Poland and Ukraine. Confirm the actual team's location, access, language coverage, staffed hours and data-processing terms before work. Document the QA checks, logs and escalation path for that queue.
Related scenarios
Other situations teams bring us
Scope one human-review queue
Describe what arrives, what the reviewer must return and who decides on exceptions. Review the proposed team, controls and paid-pilot scope before work starts.
Share the workflow, volume and required hours, not customer records or confidential datasets.