AI outsourcing in the Philippines gives AI companies two things: people who support your users, and people who label data and rate model outputs at the volume training demands.
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Two kinds of work. The first is the human layer around an AI product: user support, onboarding, account help and community moderation. The second is work that feeds the model itself: data annotation and labelling, output evaluation and rating, prompt and response writing, and quality review of training data. Many clients start with one and add the other.
AI outsourcing teams in the Philippines from ProVizion support AI and machine learning companies on two fronts: the people who look after your users, and the people who produce the human judgement your models learn from. We have supported technology clients including Hewlett Packard, Alibaba Group and SEM/EQ, and many of the companies we work with today build AI and SaaS products.
Teams cover user support, onboarding, data annotation, output evaluation and trust and safety, working inside your help desk, labelling platform and Slack. You can run them yourself or have a ProVizion team leader manage rosters, quality and reporting. A small support desk or pilot annotation pod can start within about 48 hours of candidates being chosen; larger programmes take a few weeks.
AI businesses tend to run into the same two bottlenecks. The product grows faster than the team that supports it, so founders and engineers end up answering user questions, chasing failed payments and moderating the community. And the model needs a steady supply of human judgement, labelled data, rated outputs and reviewed edge cases, that a small in-house team cannot produce at the volume training and evaluation demand.
ProVizion builds teams for both. Some clients start with a support desk and later add an annotation pod; others begin with data work and move user support across once the model ships. Either way the people are full-time, trained on your product and guidelines, and work inside your tools.
"Their leadership is knowledgeable, authentic and has exceeded our expectations. As a firm, we have expanded our engagement."
Supporting an AI product is different from supporting ordinary software. Users ask why the model gave a particular answer, report outputs they think are wrong, hit usage limits, question their credit balance and want help writing better prompts. Our agents learn how your product behaves, including its known limitations, so they can explain an unexpected result honestly rather than reading a script.
For products with a heavier technical queue, our technical support outsourcing teams follow the same tiered structure.
Annotation is where accuracy compounds. Our teams label text, images, audio and video to your guidelines: classification, entity tagging, bounding boxes and segmentation, transcription, sentiment and intent. Every project starts with a calibration batch scored against your gold-standard answers, and annotators move onto live work only once they reach the agreed accuracy.
Because the Philippines has a large English-speaking, university-educated workforce, we can usually recruit annotators with relevant backgrounds when a project needs them, such as nursing graduates for clinical text, accountants for financial documents or developers for code.
"This team is characterized for their hard work and communication. They will not stop until they get to the outcome that is agreed upon."
Evaluation work asks people to judge what a model produced: rate responses against a rubric, compare two answers and pick the better one, flag factual errors or unsafe content, or write model responses that show what good looks like. This is the human feedback that fine-tuning and evaluation pipelines depend on, and it needs raters who read carefully and apply guidelines consistently.
We recruit raters for reading comprehension and judgement, run them through your rubric with worked examples, and track agreement between raters so drift is caught early. Team leaders hold regular calibration sessions where disputed items are discussed and the guideline is clarified, and those clarifications are shared with your research team.
Consumer AI products attract misuse, and the community around them needs looking after. Moderation teams review flagged prompts and outputs, enforce your usage policy, handle appeals and keep forums and Discord servers healthy. Escalation rules are agreed with your policy team in advance so difficult cases reach someone who can make the call.
Moderators are briefed on the content they will encounter before they start, rotate between queues and have access to wellbeing support. Looking after the people doing this work is part of keeping its quality high.
As an AI company moves from early adopters to business customers, the work around the product grows: onboarding calls for new teams, usage reviews for larger accounts, renewal reminders, CRM upkeep, invoice follow-up and research for the sales team. Offshore staff take this on so your own people can stay focused on the model and the roadmap. Our SaaS customer support page covers the onboarding and retention side in more detail.
Staff work inside your systems rather than ours, with role-based access you control and can revoke. Every hire signs an NDA and clears a background check, and engagements that touch regulated or personal data can be set up to meet the requirements you are bound by, such as GDPR. Training data and model outputs stay in your environment.
Teams are drawn from a talent network of more than 10,000 professionals, hold full-time roles with health cover and paid leave, and can be managed by your own team or by a ProVizion team leader who runs rosters, QA and reporting. Engagements typically run three to twelve months, which suits both an ongoing support desk and a fixed annotation project.
Two kinds of work. The first is the human layer around an AI product: user support, onboarding, account help and community moderation. The second is work that feeds the model itself: data annotation and labelling, output evaluation and rating, prompt and response writing, and quality review of training data. Many clients start with one and add the other.
Yes. Teams work in the tools you already use, whether that is Labelbox, Scale's platform, Label Studio, CVAT, an internal tool or a spreadsheet workflow. Access is role-based and stays inside your environment, so data does not move onto our systems.
Every project starts with your guidelines and a calibration batch scored against gold-standard answers. Annotators who reach the agreed accuracy move onto live work, a share of each day's output is reviewed by a QA lead, and disagreements feed back into the guidelines. Accuracy and throughput are reported weekly.
Yes, with the right setup. Moderators are briefed on the content they will see before they start, rotate between queues to limit exposure, and have access to wellbeing support. We agree escalation rules with your policy team so edge cases reach a person who can decide them.
A one to three person support desk or pilot annotation pod can often be working within 48 hours of candidates being chosen. Larger programmes, or projects that need specialist backgrounds such as coding, medicine or law, take a few weeks to recruit and calibrate.
Two ways to start. Book a short call, or request a proposal and we'll come back with a recommended team structure.