ProofLab - Managed AI Evaluation & Red-Teaming
GigaRev ProofLab provides managed red-teaming, benchmarks, and expert datasets for production-grade GenAI systems. Ship reliable, secure, and measurable AI.
Ship reliable, secure, and measurable GenAI systems with expert-led evaluation infrastructure, domain-specific benchmarks, and high-quality training data.
The Problem
Why traditional QA fails for Generative AI.
85% Failure Rate | 10x Cost of Fix
- Unproven Safety: We can demo the assistant, but we can't prove it's safe or correct.
- Ad-Hoc Testing: We don't have repeatable evals—we just try prompts.
- Lack of Experts: We need domain experts for targeted data, not generic annotation.
- Privacy Blocker: Security + privacy constraints block using low-trust vendor labor.
Trust Pillars
- Built for Customer-Facing AI
- Rubric-Driven Quality Control
- Secure-by-Design Workflows
- Measurable Reliability Scorecards
Core Capabilities
Production-grade infrastructure for the next generation of AI systems.
Red Team Studio
Find failures before your customers do
Adversarial testing to expose jailbreaks, policy bypass, prompt injection, and unsafe outputs.
GoldSet Benchmarks
If you can't measure it, you can't ship it
Golden datasets + grading rubrics + continuous regression to track quality over time.
Domain Data Sprints
Target the exact failures hurting quality
SME-authored SFT / preference / reasoning data targeted to your failure modes.
Agent Reliability Testing
Test multi-step workflows, not just single answers
Comprehensive testing for AI agents that execute complex, multi-turn workflows.
VoiceOps & Multimodal Data
High-quality data for voice agents and multimodal copilots
Specialized labeling, QA, and evaluation for voice and multimodal AI systems.
CodeBench
Evaluate correctness and security for code-generating systems
Comprehensive evaluation for AI code generation, including correctness and security.
Rubric & Prompt Studio
Define "good" once—then scale it across every model and release
Design prompts, task specs, and grading rubrics that make GenAI measurable.
Reasoning Data Studio
Improve multi-step correctness—not just fluent answers
Build evaluation-grade reasoning datasets with chain-of-thought, structured rationales, and decision traces.
Robotics Data Programs
Embodied datasets captured, segmented, and quality-controlled end to end
End-to-end robotics data programs: capture protocols, teleoperation datasets, action segmentation, and evaluation suites.
Methodology
- Scope & Risk Map: Define use cases, policies, threat model, and success metrics
- Design: Create prompts, rubrics, task specs, and acceptance criteria
- Produce: SME creation + calibrated evaluation + QA layers
- Report: Deliver scorecards, failure taxonomy, and prioritized fixes
- Iterate: Regression suite + monthly improvements cycle
Technical FAQ
Do you support onshore-only or US-person-only pods?
Yes. We offer dedicated onshore pods with US-person-only teams for organizations with strict data residency and personnel clearance requirements.
Can you work with sensitive customer data?
Absolutely. Our secure-by-design workflows include access controls, audit trails, redaction workflows, and isolated environments for handling sensitive data.
Do you provide rubrics + evaluation harnesses we can run internally?
Yes. All rubrics, evaluation frameworks, and datasets we create become your assets. We provide full documentation and runbooks so your team can run evaluations independently.
How do you measure annotator/evaluator quality?
We use calibration sets, inter-annotator agreement metrics, and ongoing quality audits. Every evaluator is trained on your specific rubrics and measured against gold standards.
Who owns the datasets and derived artifacts?
You do. All datasets, rubrics, test suites, and evaluation artifacts we create are fully owned by your organization with no usage restrictions.
Book a ProofLab Scoping Call to evaluate your AI systems.