DATA ANNOTATION & MODEL TRAINING

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.

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We’ll explore your technology goals and challenges
You’ll get expert insights on the best path forward
We’ll outline next steps to bring your solution to life
PROMISED EXPERTISE

Certified experts, and a quality score on every batch.

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Certified expert network

Every expert is certified in their domain before they touch your data — engineers, physicians, attorneys, scientists, and finance professionals, predominantly mid and senior level, working part-time and asynchronously.

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Benchmark-measured quality

Every batch ships with inter-annotator agreement rates, gold-set accuracy, error analysis, and expert credentials per task. We build model and dataset benchmarks as a product, which is why our quality claims arrive pre-measured.

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Structurally neutral

No lab owns a piece of us, no lab has invested in us, and we don't compete with your model. Your roadmap stays confidential by construction rather than by promise.

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Synthetic data, expert-verified

We generate synthetic data where it's efficient and apply expert verification where accuracy matters — the cost-quality frontier rather than a religious position on either side.

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Enterprise software platforms

Develop the systems that run your business.

  • ERP and CRM development
  • ATS and internal operations tools
  • Workflow automation systems
  • Enterprise system integrations
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Intelligent software architecture

Design software foundations built for long-term scale.

  • Scalable system architecture
  • Performance and reliability engineering
  • Cloud-native infrastructure design
  • AI-enabled engineering workflows
We'll scope a real batch of your actual work, not a sample task.
You'll get it back with a full quality scorecard attached.
We'll show you the head-to-head against your incumbent where we can.
Case Studies

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 standing up its expert network and platform to produce the training data and benchmarks for its own Specialized Models engagements, then publishing the pipeline metrics: quality scores, throughput, and expert certification stats.

We Routed Our Own AI Stack

FullStack is building and running its own gateway across internal AI usage on Connect and Labs tooling, and will publish the real numbers: cost reduction, quality retention, latency, and failover uptime through actual provider outages.

MOVING SAFELY

Run the pilot. Read the scorecard.

Every engagement opens with a paid pilot: a fixed-scope batch of your real workload, delivered through the platform by certified experts, returned with a full quality scorecard.
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First batch in 2–4 weeks*

You get agreement rates, error analysis, expert credentials per task, and throughput before committing to volume.

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Judge us against your incumbent

Where the same tasks can be run twice, we'll show you the head-to-head. Buyers in this market have been promised experts and delivered crowds, so measured quality on your own tasks is the only fair test.

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We don't compete down-market

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.

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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, which is worth knowing before you plan around it.

*These are typical time estimates and actual times may differ based on project complexity and scope.
COMPREHENSIVE SOLUTIONS

Explore FullStack's expert data services.

Frontier labs have largely exhausted the open web for pre-training. The gains now come from post-training — RLHF, expert preference data, RL environments, rubrics, and verification — none of which exists in any corpus. It has to be made by people who know things.
  • Expert annotation across multiple modalities
  • RLHF and expert preference data
  • Model and dataset evaluation
  • Benchmark design and development
  • RL environment and verifier construction
  • Rubric co-design and quality calibration
  • Synthetic data generation with expert verification
  • Native Spanish and Portuguese language data
  • Domain-credentialed pools: code, healthcare, legal, finance

Partner with FullStack and judge us on the data.

Frontier and Near-Frontier Labs

Neutral by construction, at a bench nobody else has tapped

For labs buying post-training data at scale, we offer structural neutrality, a security and confidentiality posture built as a first-order deliverable, and senior bilingual experts from a region we've spent years recruiting in.
Enterprises Training Their Own Models

An annotation partner that also builds models

For first-time annotation buyers, we label your proprietary data, verify synthetic data where it's efficient, and hand you benchmark-grade quality measurement — from the team that also trains the model.
our blog

Featured articles

What DeepSeek’s DSpark means for LLM performance in production

DeepSeek’s DSpark speculative decoding makes V4 models up to 85% faster per user. Learn what that means for your LLM stack in production.
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OpenAI's Terra, Luna, and Sol vs. Anthropic: Which wins what?

We benchmarked OpenAI and Anthropic on real engineering tasks. See how they compare on reliability, cost, and speed.
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Sonnet 5 is here: Which jobs it wins—and where Opus still leads

FullStack ran Sonnet 5 and two Opus builds through real enterprise tasks. There was no single winner—but a clear routing strategy that cuts costs without sacrificing results.
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