Here is feature sets, value propositions for Knobs. It has capabilities for Experimentation, Diagnostics and Evaluation organized as

• ABExperiment → experimentation platform
• KnobScope → diagnostics & observability layer
• ResultBench → evaluation & benchmarking framework

🧩 DataKnobs Suite Overview

Tagline:

“DataKnobs — Turn the right knobs for better AI performance.”

Unified Message: Each product in the DataKnobs suite helps teams experiment, understand, and validate machine learning and LLM-based systems — from idea to insight to impact.

⚙️ 1. ABExperiment — Experimentation Platform

Positioning:

“Run, compare, and optimize your AI and ML experiments with precision.”

Core Features:

• 🧠 Experiment Orchestration: Define and run A/B and multivariate experiments across 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.
• 🧩 Integrated with LLM & ML Pipelines: Works with OpenAI, Hugging Face, or custom APIs.
• 📊 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.

Messaging Pillars:

• “Experiment with confidence.”
• “From hypothesis to statistically valid result — faster.”
• “The foundation of data-driven AI improvement.”

🔍 2. KnobScope — Diagnostics & Observability

Positioning:

“See inside your AI systems — understand why models behave the way they do.”

Core Features:

• 🩺 End-to-End Tracing: Visualize 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 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.

Messaging Pillars:

• “Understand before you optimize.”
• “From black box to glass box.”
• “Diagnose issues in minutes, not days.”

🧪 3. ResultBench — Evaluation & Benchmarking

Positioning:

“Measure what matters — evaluate your models and experiments with consistency.”

Core Features:

• 📏 Benchmark Repository: Curate and reuse evaluation datasets and criteria (e.g., truthfulness, toxicity, relevance).
• 🧮 Multi-Metric Evaluation: Quantitative (BLEU, ROUGE, accuracy) + qualitative (LLM-as-judge, human evals).
• ⚖️ 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: Use GPT-based graders for subjective tasks (helpfulness, tone, creativity).
• 📊 Benchmark Dashboards: Visual summaries of performance across releases and datasets.

Messaging Pillars:

• “Make every result measurable.”
• “Your single source of truth for model quality.”
• “Close the loop between experimentation and impact.”

🎯 Unified Messaging Framework

✨ Example Unified Positioning Paragraph

DataKnobs is the control panel for intelligent experimentation and model optimization. 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. Together, they form a continuous loop of experimentation → diagnosis → evaluation — empowering teams to build AI systems that are both performant and trustworthy.