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The human experts your AI models need to get smarter
We provide certified annotation, expert feedback, and model evaluation from a vetted network of senior professionals—with a benchmark-measured quality scorecard on every batch.
Certified experts and a quality score on every batch
Certified expert network
Every expert is certified in their domain before they touch your data, giving teams AI data services and AI services for expert-led annotation and evaluation. This model combines measurable quality with flexible training capabilities, backed by deep domain knowledge and a network equipped to support domain-specific annotation needs. Expert-led annotation and evaluation also strengthen fine-tuning for specialist or enterprise models.
Benchmark-measured data quality
Every batch of annotated data ships with inter-annotator agreement rates, gold-set accuracy, error analysis, and expert credentials per task. Our expert network brings deep domain knowledge to specialist annotation work, essential for supervised machine learning models. We build model and dataset benchmarks as a product, which is why our quality claims arrive pre-measured. That same expert network is part of our training capabilities.
Structurally neutral
No lab owns a stake in FullStack, no lab has invested in us, and we don't compete with your model. Your roadmap stays confidential by construction rather than by promise.
Synthetic data, expert-verified
We generate synthetic training data where it's efficient and apply expert verification where accuracy matters. For certain tasks, well-designed synthetic data can match or exceed the performance of real data when specialists validate it.
Our client impact in action
The quality scorecard, published
The scorecard format is the proof: inter-annotator agreement, gold-set accuracy, error analysis, expert credentials per task, and throughput—the same document on every batch. Most vendors describe their quality process. We publish the artifact, because building the measurement is one of the things we sell.
Our own annotation pipeline
FullStack is building its expert network and platform to produce the training data and benchmarks for its own specialized models engagements, with AI evaluation scorecards for each batch, then publishing the pipeline metrics: quality scores, throughput, and expert certification stats.
That gives teams clearer proof on annotated outputs, labeled data, and what the scorecard confirms. Labels are crucial for making data teachable. This supports responsible AI data workflows for artificial intelligence programs and AI training data management, including high-quality labeled data for compliant model development.
How we route our own AI stack
FullStack is building and running its own gateway across internal AI usage on Connect and Labs tooling to produce AI training data for specialized models engagements, along with benchmarks that support artificial intelligence model development with high-quality labeled data and plans to publish the resulting metrics.
AI training data teaches models patterns from examples. Manual annotation is slow and error-prone, and labeling is often the most expensive part of data preparation.
What our clients are saying
Run the pilot. Read the scorecard.
First batch in 2–4 weeks*
You get agreement rates, error analysis, expert credentials per task, and throughput before committing to volume.
Judge us against your incumbent
Where the same tasks can be run twice, we'll show you the head-to-head—the fair way for a lab qualifying a new specialist data annotation supplier to compare us against what it already has. Buyers in this market have been promised experts and delivered crowds, so measured quality on your own tasks is the only fair test.
We focus on expert-led data work
Routine, high-volume pre-labeling is being automated away by foundation models. If that's what you need, a BPO annotation vendor will serve you better and cheaper. We work at the expert layer.
The platform is in development
Our expert-network platform is being built now, seeded by Connect's existing vetted-professional network. Delivery today runs as managed pods on that foundation—worth knowing whether you're an enterprise training your first specialist model or a lab qualifying a new supplier.
Explore FullStack's expert data services
- Domain-credentialed pools: code, healthcare, legal, finance
- Expert annotation across multiple modalities for AI training and large language models
- RLHF and expert preference data for reinforcement learning
- Model and dataset evaluation for effective AI models
- Benchmark design and development
- Rubric co-design and quality calibration for sentiment analysis
- RL environment and verifier construction
- Synthetic data generation with expert verification
- Flexible data collection tailored to different validation and modeling needs
- Locale-specific data in 400+ language variants, including native Spanish and Portuguese language data
Partner with FullStack and judge us on the data
Neutral by construction, at a bench nobody else has tapped
An annotation partner with AI model-development expertise
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