Human judgment at scale
Trained operators provide the labeling, evaluation, and review that models depend on.
BPO Service
AI Outsourcing Services
Direct answer
AI outsourcing delegates the human work behind AI systems — data labeling, dataset QA, model output evaluation, RLHF and preference ranking, prompt testing, and human-in-the-loop review — to a trained team. Actigy BPO runs these to documented guidelines with QA and quality metrics, giving AI and data teams reliable human judgment at scale.
What Actigy reports
ThroughputItems per SLA
Inter-annotator agreementMeasured
QA accuracySampled
CalibrationContinuous
What AI operations work can be outsourced?
Most AI systems depend on human work that does not scale by hiring engineers: labeling and annotating data, checking dataset quality, evaluating model outputs, ranking responses for preference tuning, testing prompts, and reviewing content in the loop. Actigy provides trained operators for this work.
Quality comes from clear guidelines, calibration, and QA. Actigy runs labeling and evaluation against your annotation guidelines, measures inter-annotator agreement, and feeds disagreements back into calibration. Model architecture and research stay with your ML team; Actigy supplies the human-judgment layer.
Capabilities included
What Actigy handles
Data labeling & annotation
Text, image, audio, and document annotation to your guidelines and schema.
Dataset QA
Existing datasets reviewed and corrected for quality, balance, and consistency.
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 tested for quality, edge cases, and regression.
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.
Who this is for
- 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
Scenarios
Common situations we solve
If any of these sound familiar, outsourcing AI operations to Actigy is worth a conversation.
You need labeled data faster than you can hire.
We add calibrated annotation capacity with measured quality.
Your AI feature needs human review in production.
We provide human-in-the-loop review to your thresholds.
You're running an RLHF or fine-tuning program.
We supply ranking and evaluation at scale.
Trust & safety review is inconsistent.
We review content to your policy with documented decisions.
Is this the right fit?
When Actigy BPO 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
When Actigy BPO may not be the right fit
- You need ML research scientists to design and train models — Actigy 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
Why Actigy
Why outsource ai outsourcing to Actigy BPO
Measured quality
Inter-annotator agreement and QA sampling keep labels and evaluations consistent.
Guideline-driven
Work follows your annotation guidelines and rubrics, with calibration on edge cases.
Scales with your models
Capacity flexes with data collection, training runs, and production review volume.
Delivery method
How Actigy launches your ai outsourcing team
Every engagement follows the same pilot-first method, adapted to the controls your process requires.
- 01
Process audit
We map the current workflow, volumes, systems, exceptions, and quality bar so scope and staffing are based on evidence, not guesswork.
- 02
SOP & KPI design
We document standard operating procedures and define the KPIs and SLAs we will be measured against before anyone touches live work.
- 03
Team selection
We assemble operators and team leads matched to your domain — finance, clinical, compliance, technical — and your tooling.
- 04
Training & knowledge transfer
We run structured onboarding against your SOPs, edge cases, and systems, with sign-off before the team carries production volume.
- 05
Pilot
A controlled pilot validates quality, throughput, and turnaround against the agreed KPIs. We tune the process before scaling.
- 06
Scale
We ramp the team to full volume with capacity planning, coverage models, and the reporting cadence agreed up front.
- 07
Continuous improvement
QA sampling, root-cause reviews, and monthly business reviews keep error rates down and throughput predictable over time.
Related services and industries
Services
Industries
Resources
Visibility
What you'll see every month
Outsourcing AI data operations should make quality more visible, not less. Actigy reports on the numbers that matter and reviews them with you on a fixed cadence, so the operation stays accountable. The same discipline applies whether you run lean or at enterprise SaaS & Software scale.
- A QA sample of completed work, scored against the accuracy bar agreed at go-live
- SLA attainment — turnaround and throughput measured against your targets
- Volume, backlog, and exception trends, so capacity stays ahead of demand
- Root-cause notes on any error, with the SOP change made to prevent a repeat
- A monthly business review with your point of contact and the team lead
Engagement model
How pricing and engagement work
Actigy prices AI data operations on a transparent staffing model tied to scope, volume, and complexity — the cost-to-quality ratio, not an opaque per-transaction markup. Many teams run it alongside qa outsourcing and technical support outsourcing under one delivery team, with a single point of contact.
Start with a pilot
A scoped, paid pilot proves quality and throughput before you commit to full volume.
Staffing-based pricing
You see the team, the roles, and the cost. Capacity flexes up or down with your volume.
You own the documentation
SOPs and process knowledge stay yours, which keeps switching costs low and cuts key-person risk.
Clean exit and transfer
If you wind the engagement down, Actigy returns current documentation and supports knowledge transfer.
See how Actigy would run your AI operation
Book a consultation and we'll assess scope, complexity, staffing, and delivery cost — then propose a pilot to prove quality before you scale.
How it works
AI plus trained people: human-in-the-loop
FAQ
Frequently asked questions
What is AI outsourcing?
AI outsourcing delegates the human work behind AI — data labeling, dataset QA, model output evaluation, RLHF and preference ranking, prompt testing, and human-in-the-loop review — to a trained, QA-controlled team. Actigy runs this to your guidelines and rubrics while your ML team keeps model and research ownership.
Does Actigy build or train AI models?
No. Actigy provides the data and evaluation operations that models depend on, not research or model engineering. Your ML team owns architecture, training, and research; Actigy supplies reliable human judgment — labeling, evaluation, and review — at scale.
How is labeling and evaluation quality controlled?
Operators are calibrated on your guidelines with gold examples, inter-annotator agreement is measured, and work is QA-sampled. Disagreements and edge cases are fed back into calibration, so quality improves and stays consistent across the team.
Can Actigy support human-in-the-loop production review?
Yes. Actigy reviews live AI outputs, corrects or escalates them to your thresholds, and documents decisions, providing the human layer that keeps production AI features safe and accurate.
What data types can Actigy annotate?
Actigy supports text, image, audio, and document annotation, as well as model-output evaluation and content moderation, all to your schema and safety policy. The scope is matched to your data and quality requirements during the process audit.
How much does AI data work cost?
Actigy prices AI data work per FTE — a transparent monthly rate per role, set by the role and your domain, not per task or per hour. A Tech Lead owns calibration and QA; choose a managed team or staff augmentation. We work in your annotation and evaluation tools and quote after a short process audit.
Can you outsource data labeling and AI training data?
Yes. Data labeling, dataset QA, model-output evaluation, RLHF ranking, prompt testing, and human-in-the-loop review are all outsourceable to a calibrated, QA-controlled team. Your ML team keeps model architecture, training, and research.
Outsource the process. Keep control of the outcome.
Tell us what process you want to outsource. Actigy will assess scope, complexity, staffing model, and delivery cost.