Multimodal annotation typically costs more per item than single-format labeling, since maintaining consistency across image, video, text, and audio in one pipeline requires broader annotator expertise and more QA touchpoints. This type of annotation makes sense for models that rely on more than one data type, since inconsistent labeling in even one format can weaken the model's overall performance.
Double-pass annotation is a quality assurance (QA) method where two annotators label the same data independently, and any discrepancies are reviewed by a third, more experienced annotator, significantly reducing labeling errors compared to single-pass review.
Maintaining data quality at scale requires a few consistent checks: a second annotator reviewing the same data to catch discrepancies, clear guidelines for handling edge cases consistently across the team, annotators matched to the specific domain of the data, and a defined process for resolving ambiguous cases.
The main difference between in-house and outsourced data labeling is who owns recruiting, training, and managing annotators. In-house teams give you more control, but that ownership takes time and slows growth. Outsourced data labelling services handle recruitment, training, and quality assurance for a fixed monthly fee, giving you access to specialized annotators and established processes without building that expertise internally.
Social media platforms use a combination of automated AI filters and outsourced human moderation teams to protect online users from harmful content and meet strict regulatory requirements.
Content is first screened by automated filters and user reports, then reviewed by moderators when additional context or a policy decision is required. Trained specialists work with existing software tools and moderation queues to process user reports, evaluate flagged content against established policy guidelines, and remove harmful material.
Trust and Safety outsourcing involves hiring a specialized external team to manage the safety and integrity of an online platform, working alongside in-house teams to shield organizations from liability, fraud, and abuse.
While standard content moderation focuses specifically on evaluating user posts and comments within these workflows, broader Trust and Safety operations handle the platform architecture, including security threats and legal compliance.
Any platform that facilitates public media sharing should prioritize user Trust and Safety, however, social media, e-commerce, and online gaming are the main industries outsourcing content moderation operations at present.
All online platforms require content moderation to filter out harmful material, protect users against spam and abuse, and ensure ongoing legal compliance. Typically, content platform owners start with in-house moderation, but move to an outsourced partner once content volume and arrival patterns start to exceed the capacity of the founding team.The right time to scale up depends on traffic volume and the potential brand risk of unmoderated content, as even a single bad post can cause lasting brand damage.
Outsourced content moderation works by integrating a dedicated team of external specialists directly into a client's existing moderation workflows to handle 24/7 review. While the client maintains full control over the community policies and guidelines, the outsourcing partner handles the sourcing, training, and daily supervision of all moderators.
Outsourced content moderation covers all media shared on a platform, including text, comments, images, voice notes, live streams, videos and social media content.
Content moderation outsourcing is the practice of hiring an external moderation team to review and enforce moderation policies across social media platforms, online communities, and user-generated content environments.These teams monitor user-generated content including text, images, videos, and comments against strict community guidelines to remove harmful material and ensure regulatory compliance.
Outsourcing makes sense for startups when at least 1 or 2 people are forced to dedicate their time to a non-core function. Shifting content moderation or data labeling tasks to a partner gives scaling companies immediate access to specialist best practices and expert teams without the internal costs and management overheads.
A managed services model works best for scaling companies because it removes the heavy management overhead that comes with models like staff augmentation. With a managed model, businesses are able to quickly incorporate expert processes and large teams at fixed prices.
A company should outsource operations when repetitive, high-volume tasks prevent internal staff from focusing on product development and strategy, or when a business requires 24/7 global coverage and local recruitment cannot keep up.
Industries that require ongoing AI validation include those that use AI to make high-stakes decisions and calculations that affect human health and safety, financial security, and legal compliance. As real-world data is constantly changing, continuous evaluation is required to monitor algorithms for model drift, hallucinations, accuracy, and bias.
Organizations test AI models through a combination of LLM-as-a-judge, RAG Validation (Retrieval-Augmented Generation) and human verified “golden datasets”. Testing aims to evaluate accuracy and safety, as well as check for hallucinations, bias and data leakage.
AI systems frequently fail in production due to the massive gaps between how they are tested and how they are expected to perform in real-world environments. During testing, AI systems are evaluated using structured, static data sets, whereas in production, data is dynamic and unpredictable.
Human-in-the-loop AI (HITL) AI validation is an evaluation framework that involves human judgement, auditing and editing of automated decisions to ensure accuracy. This hybrid AI-human approach reduces errors and hallucinations and maintains regulatory compliance.
To do this, companies set up a safety net around the AI. When the model gets confused or handles a high-risk task, the system automatically escalates that query to a human for review. Randomized audits are also run on successful tasks to catch hidden mistakes and keep operations working safely while helping the model learn over time.
The metrics that matter most in production include escalation rate, operational consistency, failure severity and recovery, and business impact metrics. Today, successful AI model evaluation focuses on the metrics organizations use in production.
Companies evaluate Large Language Models (LLMs) in production environments using a combination of human in the loop metrics, automated evaluations and LLM-as-a-judge frameworks. AI evaluations are used to measure accuracy and safety, as well as the prevalence of hallucinations and bias in outputs.
AI benchmark scores are insufficient in evaluating AI models as they tend to be based on clean, static data rather than the unstructured, dynamic data used in real-world environments. This means that AI models are not trained to handle the edge cases, unpredictable user behaviour and context ambiguity that often arise outside of controlled test environments.
AI evaluation is the process of measuring how reliably, safely and accurately an AI model can perform the tasks it is designed to carry out. Because AI model outputs are probabilistic, evaluation needs to be rigorous to flag hallucinations, bias, inaccuracies and user safety issues.
Outsourced teams are structured into units including specialists, a team lead, and quality assurance analysts.
These teams are overseen by a Service Delivery Manager (SDM) who handles daily operations and acts as the primary point of contact in the client's time zone. To support global operations, teams work across offices in the US, Europe, and Asia.
Launching an outsourced team with BUNCH typically takes between 24 hours and four weeks from the date the contract is signed.
The exact timeline will depend on the scope of work, the level of specialization required, and the availability of client guidelines and training materials.
The onboarding process includes defining workflows, setting performance targets, preparing training materials, and configuring the team before operations go live.
BUNCH builds outsourced teams by recruiting specialists, assigning dedicated management layers, and creating workflows around each client’s specific operation.
We manage everything from hiring and training to quality assurance, reporting, and day-to-day team performance across functions like data labeling, content moderation, customer support, and community management.
Outsourcing works by delegating a specific business process to an external partner who supplies the people, management, and infrastructure to run it.
Outsourcing partners help growing companies solve three core problems: access to specialized processes and expertise, maintaining quality, and scaling teams.
Companies benefit most from outsourcing tasks that are repetitive, require human judgment and rely on high-quality data.
While outsourcing partners used to focus on speed and cost-cutting, modern companies prioritize data quality and accuracy to ensure the best results.
In a managed services model, an outsourcing partner takes complete ownership and accountability for a core business function, whereas in staff augmentation, a business uses experts employed and managed administratively by the partner, but integrated into the client’s internal team.
A managed services model is a partnership where a business hands over complete ownership and accountability of a core function to an outsourcing house.
The partner takes care of everything from daily operations and staff management to compliance and client escalations on behalf of the business.
Companies outsource operations to access specialized talent and established processes, reduce overall costs, and scale faster without building large internal teams.
After a short scoping call, we can propose a workflow and staffing plan quickly.
Yes, this is where structured rubrics, escalation paths, and documented processing practices matter most. BUNCH documents GDPR-aligned processing practices.
Validation checks rules/requirements; verification confirms correctness vs trusted sources. We can implement both depending on your use case.
We are a managed services provider: we run trained human validation teams and integrate into your existing stack. If you already use tools (testing, monitoring, labeling platforms), we complement them with human judgment and consistent QA.
Most outsourcing companies have good entry prices, but include hidden costs like onboarding fees, management overhead, software licensing, and ongoing staff turnover costs.
With a managed services model like BUNCH, however, you pay a fixed monthly price that's all-inclusive, with no unexpected costs or set up fees.
Yes, we offer payment options in stablecoins for our clients. Please be aware that there may be regulatory requirements prior to engaging in crypto payments depending on the client's jurisdiction.
Clients can always cancel the contract during the one-month probation period if they are not satisfied with our services. However, as of 2024, it's worth noting that no client has yet exercised this option.
The minimum contract duration is one year for recurrent campaigns. However, most of our services include a one-month probation period during which either party can cancel the contract at any time without further liabilities if they are not satisfied with the service.
At BUNCH, we provide a "Managed Services" model, focusing on operational excellence and efficiency without requiring client oversight. Unlike the common industry practice of staff augmentation, which involves client involvement in hiring and management, we handle all aspects of service delivery. This includes talent selection, training, productivity management, and data security.By assuming full responsibility for these functions, we allow our clients to concentrate on their core business activities, offering them peace of mind regarding operations.
Our approach not only ensures high-quality service and compliance but also maintains strict data privacy and security standards, contributing to better overall business outcomes.
When we founded BUNCH, we studied in detail the BPO market in major developing countries like the Philippines, Indonesia. We were living in South-East Asia for years and were very aware of the unmatched talent and human quality of Filipinos and Indonesians. Truly unique in this world.
We were actually shocked to find out how most BPOs marketed their services. It was common to find pictures of "agents" in cubicles wearing low-end headsets and portrayed as affordable overseas labor. The entire sales pitch was to show how much companies could save by outsourcing talent to the Philippines, detailing salaries, potential savings, and even how frequently a Filipino gets sick, or when to trust them.
Employee engagement was often about fast-food birthday parties and company mascots. Somehow, most considered their own talent as a bunch of kids.
We found that approach deeply condescending.
BUNCH was born as a hub to connect the new generation of skilled tech talent with tech jobs in global tech hubs. We opened colorful offices without cubicles. Our talent is addressed as Tech Specialists and everyone is treated as the professionals working in AI, SaaS, Fintech, and Social Media that they actually are.
We are very particular about the glorification of our talent. BUNCH was then born as "A bunch of tech specialists doing crazy things with high tech."
Currently, BUNCH is a self-funded, independent private company and we are not actively seeking investments. However, we are open to discussions with institutional investors. If you are interested, please reach out to us via email at founders@meetbunch.com.
BUNCH was founded in 2017 by Carlos Puig and Rodrigo Cardenete.
We specialize in labeling training data at scale, which is crucial for the machine learning programs behind all AI models today. In fact, some of the models we train are likely used in products you use every day. We also utilize AI to optimize our processes, oversee quality assurance tasks, and assist our agents with various tasks. These AI-enabled processes are integral to our operations. Whether this qualifies us as an AI company may depend on your perspective.
All our labelers, moderators, and agents are either full-time employees or full-time independent contractors. We do not use crowdsourced talent because maintaining high levels of accuracy, commitment to volume, and meeting tight deadlines are crucial for our clients. Additionally, employing full-time talent ensures ethical working conditions, a standard we uphold for everyone at BUNCH and proudly extend to our clients.
While calls constitute a small portion of our operations, much of our customer service is conducted via chat or ticket systems. In the Philippines, BPO companies are often referred to as "call centers", a term familiar to older generations. Our primary focus is on data labeling and trust & safety—complex areas that are sometimes simplified in casual conversations as "call center work."
Our workforce setup is diverse, with some specialists working remotely and others on-premises. We have team members in Indonesia and Vietnam, though the majority are based in the Philippines.
Yes. Currently, we have a headquarters office in BGC, Manila, and a production center in Bacoor, Cavite, the Philippines. Some teams work remotely, while other members work on premises, depending on the scope of work, data security, and type of activity.
Most of our team members are based in the Philippines, including our top management and founders.
Frequently Asked Questions
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Multimodal Annotation
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