Each product in the DataKnobs suite helps teams experiment, understand, and validate AI-driven solutions and LLM platforms — from concept to clarity to results.
DataKnobs serves as your hub for smart experimentation and model tuning. Our platform delivers a unified toolkit powered by a clear vision: Test it. Understand it. Prove it.
⚙️ 1. ABExperiment: The Experimentation Platform
Execute, assess, and refine your AI and ML experiments with accuracy.
Experiment with confidence. ABExperiment lays the groundwork for data-driven AI enhancements, enabling quicker movement from hypothesis to statistically sound outcomes.
Core Features:
- Experiment Orchestration: Design and execute A/B and multivariate tests on models, prompts, or features.
- Dynamic Parameter Knobs: Adjust hyperparameters, prompts, or routing strategies without redeployment.
- Automatic Experiment Tracking: Logs metrics, versions, and configurations for reproducibility.
- Statistical Significance Engine: Built-in Bayesian or frequentist methods for measuring lift and confidence.
- Continuous Experimentation: Automate iterative testing with feedback loops from production telemetry.
- Integrated with LLM & ML Pipelines: Works with OpenAI, Hugging Face, or custom APIs.
🔍 2. KnobScope: Diagnostics & Observability
Gain insight into your AI — discover what drives model behavior.
From black box to glass box. Diagnose quickly—gain insight before optimizing and resolve issues in minutes, not days.
Core Features:
- End-to-End Tracing: Visualize the complete prompt → model → output → feedback pipeline.
- Error Attribution: Identify failure modes by model, dataset, or user segment.
- Behavioral Profiling: Detect drift, bias, hallucinations, or performance regressions.
- Context-Level Logging: Capture intermediate reasoning and tool-use in complex agent workflows.
- Real-Time Monitoring: Stream metrics and traces from live inference or test runs.
- Privacy-Aware Logging: Control what data gets recorded or redacted.
🧪 3. ResultBench: Evaluation & Benchmarking
Track what counts — assess your models and tests with reliability.
Make every result measurable. ResultBench unifies model evaluation, seamlessly connecting testing to real-world impact.
Core Features:
- Benchmark Repository: Manage and repurpose evaluation sets and metrics (e.g., accuracy, fairness, relevance).
- Multi-Metric Evaluation: Apply quantitative (BLEU, ROUGE, accuracy) and qualitative (LLM-as-judge, human review) techniques.
- Side-by-Side Comparison: Evaluate model or experiment variants across controlled datasets.
- Automated Scoring Pipelines: Plug in to post-experiment scoring via APIs.
- LLM Evaluation Integration: Leverage AI graders for subjective areas such as helpfulness, tone, or creativity.
- Benchmark Dashboards: Get visual summaries of performance across releases and datasets.
The DataKnobs Loop: Experiment → Diagnose → Evaluate
The DataKnobs suite forms a seamless cycle of ongoing enhancement:
- With ABExperiment, you can design and run structured tests.
- With KnobScope, you can trace and diagnose model behavior.
- And with ResultBench, you can measure outcomes with consistent, reliable benchmarks.
They enable teams to create AI solutions that deliver both reliability and high performance.