The Toolkit for AI R&D

Building reliable AI products requires more than just models—it requires infrastructure. Yet, modern teams are stuck patching together fragmented tools.

The Shift: From Research to Product

We are seeing a fundamental shift in how AI is built. The old way involved scattered data living in spreadsheets, brittle evaluations based on "vibe-checks", and slow iteration cycles that required bespoke engineering.

The Pipelines Way is different:

• Unified Schemas: Enforce strict data structures for consistent, high-quality inputs instead of messy spreadsheets.
• Rigorous Evals: Trust your metrics with structured rubrics and RLHF-ready outputs instead of random sampling.
• Rapid Cycles: Iterate in hours, not weeks. No new infra required.

Core Capabilities

Pipelines unifies Data Collection, Workforce Management, and Evaluation into a single, extensible platform.

A. Data Collection

Create high-quality training datasets in days. Define strict schemas for text, files, and rankings to eliminate post-collection cleaning. Support complex workflows with multi-step instructions and instant export for fine-tuning.

B. Human-in-the-Loop Management

Scale operations without the management overhead. Seamlessly route tasks to internal experts or managed crowds, and monitor throughput and quality agreement scores in real-time.

C. Evaluation & Observability

Stop guessing. Know exactly how your models perform with structured evals, side-by-side (SxS) comparisons, and automated LLM-as-a-judge workflows to scale your review process efficiently.

How It Works

1. Define Your Standard: Create a rigorous schema for "quality."
2. Orchestrate the Work: Assign tasks to your team or external annotators.
3. Collect & Measure: Watch results flow in and score outputs against your rubric.
4. Close the Loop: Feed high-signal data directly back into development.

Just as CI/CD transformed software engineering, Pipelines defines the rigorous workflow for AI systems.