Human judgment at scale
The service provides trained operators for the labeling, evaluation and review that models depend on.
BPO Service
AI Outsourcing: Data Labeling, Annotation and RLHF
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Direct answer
Actigy BPO provides AI outsourcing for data labeling, annotation, model-output evaluation and human preference ranking. Reviewers work in your tools and follow your guidelines and quality assurance (QA) rules. Your machine learning (ML) team keeps model architecture, training, validation and deployment decisions. Start with a defined batch and a paid pilot.
Describe the data, review task and quality rubric. Review the proposed sample and scope before choosing a pilot.
Your guidelines · Your model decisions · Calibrated pilot before scale
Start with the review task: describe the data type, batch size, tool and rules for accepting or rejecting work. Do not send datasets or personal records through the enquiry form. Agree access and handling terms first.
AI data results: 99.92% annotation precision (Actigy BPO-reported)
- The team delivered more than 1.5 million LiDAR and video annotations.
- The team held a 99.92% QA pass rate.
- The team scaled annotation throughput 10x.
Results come from anonymized client engagements, are reported by Actigy BPO and are not independently audited. Results depend on scope. Read the case study
Actigy BPO details LLM evaluation services and RLHF services as human-review workflows using client tools and rubrics.
At a glance
AI outsourcing at a glance: labeling, evaluation and RLHF
| Provider | Actigy BPO (Prague; delivery in Bulgaria, Romania, Poland and Ukraine) |
|---|---|
| Service | AI Outsourcing |
| Work in scope | Data labeling & annotation, Dataset QA, Model output evaluation, RLHF & preference ranking, Prompt testing & QA, Human-in-the-loop review |
| Pricing model | Per full-time equivalent (FTE) by role and domain; data volume, task time and review depth inform staffing, not a per-label fee |
| Engagement | Managed team or staff augmentation |
| Team includes | Trained operators, a Tech Lead, and QA |
| Coverage | Review hours and turnaround agreed for each queue; up to 24/7 requires a staffed and approved coverage plan |
| Delivery region | Bulgaria, Romania, Poland and Ukraine (Central and Eastern Europe) |
| Controls | Approved data access and QA checks. The stated approach describes ISO 9001 and SOC 2 alignment, not certification or a SOC 2 audit; agree dataset handling before access. |
| Pilot readiness | Agree timing after data access, guidelines, reference examples and operator calibration are ready |
| You keep | Data-use policy, quality thresholds, model training, validation and deployment decisions |
What AI operations work can be outsourced?
The team provides the human operations used in data preparation and model evaluation. A scope can include labeling, dataset checks, response ranking, prompt tests or a production-review queue. The work is performed against client-approved instructions; it is not a substitute for model engineering, validation or accountable product decisions.
For industry context, review AI ROI statistics; published returns do not forecast this engagement's results.
Data labeling adds tags, boxes, masks or categories to text, images, audio, video or documents. Reinforcement learning from human feedback (RLHF) can use human preference rankings as training input. The team's role is the agreed ranking and review work, while your ML team runs training. Model-output evaluation scores responses against your rubric for properties such as accuracy, safety and helpfulness.
Real projects mix several task types. In the published case study, the work ran in three streams. The first was multi-sensor object tracking with polygons, cuboids and semantic masks. The second was edge-case classification for rare conditions such as occlusion and severe weather. The third was RLHF review of model outputs for LLM reward models.
Quality comes from clear guidelines, calibration and QA. The team runs labeling and evaluation against your annotation guidelines, measures inter-annotator agreement (IAA) and feeds disagreements back into calibration. In the published case study, two annotators labeled every frame independently. A senior auditor reviewed any frame where their results differed beyond the agreed threshold.
Some work stays with your team. Your ML team keeps model architecture, training runs, model weights, validation code and research. You also keep the raw data and decide where it is hosted. Operators work in your labeling and evaluation tools and follow your data-handling rules. In the published case study, the team worked through read-only access and did not store training data outside the client's cloud environment.
What should an accepted annotation or review batch contain?
Agree a small output specification before the pilot. It separates completed work, corrections and records that still need your decision.
| You provide | Review output | Your acceptance check |
|---|---|---|
| Record IDs, source data and a versioned labeling guide | Labels linked to their source records, with uncertain items flagged. | Required fields, source traceability and agreed label checks pass. |
| Model outputs, test inputs and a scoring rubric | Scores, supporting evidence and questions the rubric cannot resolve. | Review coverage and evidence meet the agreed standard; model release remains your decision. |
| QA sample and correction rules | Defect log, corrected records and items still awaiting review. | Rework is checked before the batch is accepted; unresolved records are not counted as complete. |
For task-specific records, see human LLM evaluation and review evidence or RLHF preference-data review. For live exceptions, compare human-in-the-loop operations around an existing AI workflow.
Capabilities included
Which AI data tasks can the team handle?
Choose a defined annotation or review queue from the capabilities below. The team applies your guidelines, records task decisions and performs the agreed QA checks. Your team resolves policy questions and decides whether the resulting data or evaluation evidence is suitable for use.
Data labeling & annotation
Text, image, audio, and document annotation to your guidelines and schema.
Dataset QA
Dataset samples checked against your quality rules, with labeling errors and potential coverage gaps flagged for review.
Model output evaluation
Model responses rated for accuracy, safety, and helpfulness against rubrics.
RLHF & preference ranking
Response comparison and ranking to support preference tuning and alignment.
Prompt testing & QA
Prompts and outputs checked against agreed examples and rubrics for the model versions in scope.
Human-in-the-loop review
Production AI outputs reviewed, corrected, and escalated to your thresholds.
Content moderation
AI-generated and user content reviewed against your safety policy, with each decision documented.
Which teams need outsourced AI data operations?
This AI data service fits:
- AI and ML teams that need labeled data and evaluation at scale
- Companies deploying models that require human-in-the-loop review
- Teams running RLHF, fine-tuning, or alignment programs
- Product teams testing and QA-ing LLM features and prompts
- Trust & safety teams needing consistent content review
- Computer vision and autonomous mobility teams that need image, video and LiDAR annotation
Scenarios
When should you consider an AI data operations team?
Teams outsource AI data work in four common situations.
You need labeled data faster than you can hire.
Start with a representative sample, check operator calibration and measure accepted output before agreeing the staffing needed for a larger batch.
Your AI feature needs human review in production.
Define which outputs enter the review queue, the time available to act and what happens when review is unavailable or an issue exceeds the operator's authority.
You're running an RLHF or fine-tuning program.
The team can supply preference rankings against a versioned rubric. Your ML team decides how to use that data and whether the resulting model passes validation.
Trust & safety review is inconsistent.
Calibrate reviewers on policy examples, compare disagreements and route uncertain cases to your policy owner before changing the guidance.
Is this AI data service the right fit?
When the service is a strong fit
- You need labeling or evaluation capacity beyond your in-house team
- You have annotation guidelines or are ready to define them
- You need measured quality, not just volume
- Your AI feature needs human review in production
- You want capacity that scales with model and data cycles
- You can give tool access and a gold set of labeled examples
When another model may fit better
- You need ML research scientists to design and train models - this service provides data and evaluation operations, not research headcount
- You require zero human review and fully automated labeling
- You cannot share guidelines, examples, or a quality rubric
- Your data volumes are too small to justify a managed team
AI data delivery
What supports consistent AI data review?
Measured quality
Inter-annotator agreement and QA sampling expose inconsistent judgments. Review the sample and task definition before interpreting a score.
Guideline-driven
Work follows your annotation guidelines and rubrics, with calibration on edge cases.
Scales with your models
Plan capacity around data batches and review demand, allowing for calibration, QA and the availability of trained operators.
Delivery method
Pilot plan for AI outsourcing
The team pilots AI outsourcing on an agreed data sample, scored against your gold standard, before any team scales. The plan below uses three stages, and you decide at the scale gate.
| Stage | What happens |
|---|---|
| Guidelines and calibration | Review data types, schema, volumes, tools and access rules. Agree the standard operating procedure (SOP), quality rubric and key performance indicators (KPIs). Calibrate operators on client-approved reference examples before processing the pilot sample. |
| Sample pilot | Process an agreed sample and compare it with your reference standard. Measure label quality, agreement, throughput and rework. Send ambiguous examples to your owner for a decision and version any guideline changes. |
| Scale gate | Your team reviews the thresholds, sample limits and unresolved edge cases. Approve further batches or review hours with a staffing plan and QA cadence. Accepted annotation work is not, by itself, approval to train or deploy a model. |
See the full delivery method: How the pilot works
Related services and industries
Compare data annotation outsourcing companies for labeling, model-output review and managed QA. For record processing without an ML rubric, see the separate data entry provider comparison.
Services
Industries
Resources
Visibility
Which AI data quality measures are reported?
Agree reporting by batch or review interval. The team reports the quality measures selected for the task, together with throughput, turnaround and unresolved guideline questions. Include the sample size, reference standard and rubric version so your SaaS & Software or ML team can interpret the results.
- QA sample size, acceptance rules and pass rate by task type
- Inter-annotator agreement and disagreements requiring adjudication
- Accepted output, rework, backlog and turnaround against the agreed batch plan
- Edge cases and rubric changes, with your decision owner and the guideline version recorded
- Calibration reviews with the team lead and your data or policy owner at the agreed interval
Engagement model
How is AI outsourcing priced?
Actigy BPO prices AI data work per FTE by role and domain, not per label or review task. Data volume, task complexity, calibration time and QA depth inform the team size. The cost-to-quality ratio helps compare that staffing cost with accepted output and rework. Related QA testing or technical support needs should have their own defined tasks and owners.
When comparing an FTE quote with a per-label quote, use the same task definition and acceptance standard. Include supervision, calibration and rejected work. The monthly FTE rate is the billing basis; cost per accepted label is a useful operating measure, not a promised production rate.
How it works
AI plus trained people: human-in-the-loop
Operators assess the outputs routed to them against your thresholds. Within the agreed authority, they accept, correct or escalate a result and record the decision. Your team controls any use of that feedback in training or evaluation. Human review adds evidence; it does not guarantee model accuracy, safety or improvement.
FAQ
Frequently asked questions
What is AI outsourcing?
AI outsourcing can cover different types of work. Actigy BPO provides the human operations: data labeling, dataset QA, model-output evaluation, preference ranking, prompt checks and human-in-the-loop review. Operators follow your guidelines and rubrics. Your ML team keeps model engineering, training and release decisions.
Does the team build or train AI models?
No. The team provides the data and evaluation operations that models depend on, not research or model engineering. Your ML team owns architecture, training and research. The team supplies reliable human judgment at scale: labeling, evaluation and review.
How is labeling and evaluation quality controlled?
Actigy BPO calibrates operators against your guidelines and reference examples, then uses the agreed QA sample and agreement measure. Compare results by task type and rubric version, not just as one aggregate score. Disagreements go to the named reviewer, and your team approves guidance changes. A high agreement score alone does not establish that the reference labels are correct.
Can the team support human-in-the-loop production review?
Actigy BPO can operate a defined production-review queue within your policy and permissions. Specify which outputs require review, review hours, turnaround targets and escalation or fallback behavior before go-live. Your product and policy owners retain risk and deployment decisions. This service is not a guarantee that an AI system is safe or accurate.
What data types can the team annotate?
The team supports text, image, audio and document annotation, plus model-output evaluation and content moderation, all to your schema and safety policy. The published case study covered LiDAR and video annotation. The scope is matched to your data and quality requirements during the process audit.
How much does AI data work cost?
The team quotes monthly FTE rates by role and domain. The data mix, volume, handling time, calibration and review requirements determine proposed staffing; they do not turn the quote into per-task billing. Confirm operator capacity, Tech Lead and QA allocation, review hours and tooling in the scope. Measure cost per accepted output during the pilot to assess the fit.
Can you outsource data labeling and AI training data?
Yes. The team runs data labeling, dataset QA, model-output evaluation, RLHF ranking, prompt testing and human-in-the-loop review with a calibrated, QA-controlled team. Your ML team keeps model architecture, training and research. The pilot starts with one labeling or evaluation queue.
What should you check before an AI data pilot?
Use these checks to settle data access, staffing and decision ownership before the first batch.
Start with a paid pilot
Evaluate a representative sample against your reference standard. Review error types and rework before committing more data.
Staffing-based pricing
The quote names FTE roles and rates. Batch size, calibration and review effort determine the staffing to agree.
You own the documentation
Keep guidelines, reference examples and adjudication decisions accessible to your team, with versions and update ownership agreed.
Clean exit
Define the return of work and guidelines, removal of access and handling of retained data in the exit terms.
Data protection
Approve dataset access, permitted uses and retention before work starts. The provider states ISO 9001 and SOC 2 alignment, not certification or an independent audit.
Time zones
Agree batch handoffs or live-review hours for your time zone. Any extended or 24/7 queue needs explicit staffing and fallback arrangements.
Outsource the process. Keep control of the outcome.
Tell us what process you want to outsource. The team will assess scope, complexity, staffing model, and delivery cost.
Review the sample, quality rules and staffing cost before you approve the AI data pilot.
Scope a pilot
What happens next
The team reviews the workflow before proposing a written scope. You decide whether to start a paid pilot after reviewing it.
Thank you. Your request was received. The team will review the workflow before discussing next steps.