Every company has an origin story, but not every origin story is one of continuous evolution and strategic repositioning. BergLabs’ journey from a data annotation service to a full-stack AI operations enterprise represents a fundamental shift in how we view data work and its role in building AI systems that matter.
When we founded BergLabs in 2022, we entered a market that looked deceptively simple: companies needed data labeled, and we could provide that service at scale. We were right about the market need, but we were wrong about what the work actually required. What we discovered-through thousands of projects, billions of labels, and conversations with the world’s most demanding AI organizations-transformed how we think about data work entirely.
Our Founding Story: Starting with Annotation
2022 was an inflection point for AI. GPT-3 was generating interest. Large language models were moving from research papers to actual products. Computer vision was solving real business problems. But all of these systems needed training data, and the tools and processes to create that training data at scale were immature.
We started with a simple value proposition: we’d build a team of annotators, create processes, and deliver labeled data on time and on budget. We were right about the need. Demand was genuine and substantial.
But we quickly learned that annotation-the simple labeling of images, text, or videos-was only one
component of what our customers actually needed. A company would come to us asking for imageclassification, and what they really needed was help understanding their data quality issues, designing their labeling strategy, and building reproducible processes. Another customer needed text annotations, but what they actually needed was guidance on how to structure their guidelines to match their ML pipeline requirements.
We realized we were sitting in the middle of a more fundamental problem: the entire process of translating business requirements into labeled data that trains effective AI systems was broken. Most organizations approached it chaotically. They’d label data, train models, find the results inadequate, go back and re-label, iterate slowly. The process lacked strategic design.
So we made a decision: we’d stop just providing annotation as a commodity service and start architecting the entire workflow around data quality, process efficiency, and model outcomes.
The Evolution: From Annotation Shop to AI Training to Managed Outcomes to Enterprise Partner
This evolution wasn’t a pivot—it was an expansion built on deep operational expertise we’d accumulated.
Phase 1: Annotation at Scale (2022-2023)
We proved we could deliver high-volume annotation work reliably. We built infrastructure for managing large distributed teams. We developed processes for quality control. We learned to handle multiple languages, formats, and policy frameworks. By the end of 2023, we’d labeled billions of data points and served dozens of customers.
The learning here was operational: what does it take to maintain quality at scale? How do you coordinate thousands of annotators? How do you handle policy changes, quality issues, and customer urgencies simultaneously?
Phase 2: AI Training Operations (Late 2023-2024)
As we worked with more sophisticated customers-particularly those training large language models and specialized AI systems-we realized that annotation alone wasn’t sufficient. These organizations needed:
- Strategic guidance on what data to collect and label
- Insights into data patterns and quality issues
- Feedback loops from model performance back to annotation
- Iteration and refinement processes
We started offering consulting on data strategy. We’d analyze their existing data, identify gaps, and recommend collection and labeling priorities. We’d review their annotation guidelines and suggest improvements based on model-training best practices. We built feedback loops where model performance metrics informed our understanding of which annotation decisions needed refinement.
This phase taught us that data work is iterative and deeply connected to model outcomes. A label that seems correct in isolation might be wrong in the context of your model architecture. Guidelines that work for one use case fail for another.
Phase 3: Managed Outcomes (2024-Present)
The natural evolution of understanding data quality’s impact on model outcomes was to organize around outcomes rather than activities. Instead of selling “100,000 annotations,” we started proposing “achieving 94% validation accuracy on your test set” or “enabling your model to handle edge cases with 98% confidence.”
This shift was radical because it changed the entire commercial relationship. We were no longer paid for work done; we were accountable for results achieved. This made us care deeply about everything upstream of annotation: data strategy, guideline design, quality assurance, and feedback loops.
We developed SLA frameworks around outcomes. Can we commit to 95%+ accuracy in our delivered labels? Can we guarantee 99.9% compliance with your content policy? Can we achieve this within a specific timeline and cost structure? These become the contracts we operate under.
Managed outcomes forced us to become experts in:
- Upstream data strategy and collection
- Root cause analysis of quality issues
- Process optimization
- Continuous improvement and feedback loops
- Risk management (because missing an outcome carries real consequences)
Phase 4: Enterprise AI Operations Partner (Current)
Today, we operate as a full-stack partner for organizations building and operating AI systems. We’ve moved far beyond annotation. We now help enterprise customers with:
- Content moderation at scale
- Data preparation and quality assurance
- Model evaluation and benchmarking
- Operations infrastructure for AI systems
- Training and validation data management
- Edge case identification and handling
- Regulatory compliance and audit support
The annotation work is still there, but it’s positioned as one component of a comprehensive data operations function, not as the primary service. When a customer engages with us, they’re engaging a team that understands their entire data lifecycle, not just the labeling component.
The Three Pillars: Boost for AI, Build for AI, Bridge for AI
As we’ve evolved, we’ve articulated our service portfolio around three strategic pillars that reflect how organizations use data work to improve AI systems:
Boost for AI represents acceleration services that help organizations move faster:
- High-velocity annotation for rapid model iteration
- Quick-turnaround data quality assessments
- Emergency annotation for time-sensitive projects
- Rapid scaling when internal capacity is bottlenecked
Boost is about removing bottlenecks. When you’re building a model and need to iterate rapidly, we can scale annotation capacity in days rather than months. This velocity advantage compounds-faster iteration cycles mean more learnings per unit time, which typically means better final models.
Build for AI represents foundational services that help organizations build better systems:
- Data strategy consulting (what should you collect and label?)
- Guideline design and optimization
- Quality assurance framework implementation
- Process design for sustainable labeling operations
- Training and team development for customer annotation teams
Build services are about establishing best practices and sustainable processes. Rather than just providing annotation labor, we help customers design their entire data infrastructure. This might mean helping them decide to build in-house capability for certain data types while outsourcing others, or designing quality frameworks that maintain consistency as their labeling volume scales.
Bridge for AI represents operational continuity and governance:
- Content moderation and safety operations
- Data governance and compliance
- Ongoing quality monitoring and auditing
- Feedback loops between operations and model performance
- Scaling infrastructure for production systems
Bridge services are about sustaining AI systems in production. Once a model is deployed, the data work doesn’t stop. You need continuous monitoring, quality assurance, and adaptation as real-world data distribution shifts.
These three pillars work together. A customer might use Boost to accelerate initial model development, Build to establish sustainable processes, and Bridge to maintain production operations. This positions us not as a vendor but as a strategic partner in the customer’s AI journey.
Key Milestones: 300% YoY Growth, 1,250+ Specialists, 4 Global Centers, Expansion to SFO and UAE
Growth metrics tell one part of the story, but they reflect operational and strategic success.
300% Year-over-Year Growth demonstrates market validation and our ability to scale. This isn’t revenue growth alone-it reflects the volume of data we’re processing, the number of customers we’re serving, and the complexity of work we’re handling. Sustaining 300% growth while maintaining quality and customer satisfaction is operationally demanding and reflects our infrastructure and team excellence.
1,250+ Specialists across our team represent the human foundation of our operation. These aren’t data entry workers-they’re specialized professionals with expertise in content moderation, data quality, domain-specific labeling, and process optimization. We’ve invested heavily in recruitment, training, and retention because we recognize that the quality of our team directly translates to the quality of our work.
4 Global Operations Centers (with recent expansion to San Francisco and the UAE) reflect our commitment to geographic distribution, local expertise, and 24/7 operational capacity. Our centers in India, Southeast Asia, San Francisco, and the Middle East allow us to:
- Maintain continuous operations across time zones
- Tap into local expertise for region-specific content
- Optimize labor efficiency while maintaining quality standards
- Provide geographic redundancy for business continuity
The expansion to San Francisco positions us closer to the AI lab ecosystem in the Bay Area. The UAE expansion reflects growing demand from MENA region organizations and positions us for expansion into Asia.
Scale Beyond Raw Numbers
What these numbers represent operationally:
- Infrastructure to handle billions of labels monthly
- Process maturity to maintain 95%+ quality at scale
- Team capability across 15+ languages
- Domain expertise across 5+ vertical markets
- Technology platforms (BergFlow, BergWork, BergAuto) that enable unprecedented visibility and control
The Team: MAANG Alumni, Elite Academy Graduates, Previously Raised $83M
Our competitive advantage has always been people. We’ve deliberately assembled a team with:
Technical Leadership: Former engineers from Meta, Google, Amazon, Apple, and Microsoft bring deep expertise in scaling systems, building infrastructure, and solving complex technical problems. They understand what it takes to build AI systems that work in production.
AI Research Background: We have team members with PhDs from MIT, CMU, and Stanford who bring cutting-edge AI knowledge to practical problems. They’re not just implementing existing techniques-they’re applying research-level thinking to operational challenges.
Operations Excellence: We’ve recruited leaders from operations-intensive industries-financial services, e-commerce, manufacturing-who understand how to build and manage large-scale processes with precision and quality control.
Domain Expertise: Our team includes former content moderators, data scientists, product managers, and subject matter experts across our verticals. This insider knowledge is invaluable when designing processes and solving edge cases.
Previous Capital Success: Our founding team has previously raised and managed $83M in capital, bringing financial discipline, investor relationships, and fundraising experience that inform how we scale and allocate resources.
This team composition allows us to operate at a level of sophistication that’s difficult to replicate. We’re not just executing labor-intensive work-we’re bringing strategic thinking, technical rigor, and domain expertise to every engagement.
Where We’re Headed: Robotics, Frontier AI, and the Future of Human-AI Operations
Looking forward, we’re positioned at the intersection of several converging trends that create massive opportunity.
Robotics Training Data: As robotics moves from controlled environments to real-world deployment, the need for high-quality training data explodes. Robots need to understand complex visual environments, make decisions in ambiguous situations, and handle edge cases. The data requirements are orders of magnitude beyond current AI training. We’re investing in robotics-specific annotation and simulation infrastructure.
Frontier AI Model Training: The companies building the most advanced AI models-whether large language models, multimodal systems, or specialized AI for robotics-face unprecedented data challenges. They need partners who can design sophisticated data strategies, handle massive scale, and maintain quality at the frontier of what’s possible. We’re positioning ourselves as the go-to operations partner for frontier AI labs.
Specialized Operations Infrastructure: We’re moving beyond just providing annotation labor toward building platform infrastructure for AI operations. BergFlow, our proprietary platform, is evolving to become the operating system for data-centric AI development. It will eventually enable customers to manage their entire data lifecycle-collection, annotation, quality assurance, compliance, feedback loops-through our platform.
Human-AI Collaboration at Scale: As AI systems become more sophisticated and integrated into critical operations (content moderation at scale, financial transaction processing, clinical decision support), the sophistication of human-AI collaboration becomes crucial. We’re investing in understanding and optimizing how humans and AI systems work together most effectively.
Regulatory and Compliance Infrastructure: As regulations around AI, content moderation, and data governance intensify globally, organizations will need partners who understand compliance requirements and can build operations that meet them. We’re building compliance-first operations infrastructure.
These directions all leverage our core competency-managing human expertise and AI systems to solve complex data and operations problems at scale-while expanding into higher-value applications.
