ABExperiment

Spec sheet

Purpose

ABExperiment is the DataKnobs optimization layer for testing experiences, models, prompts, and decision Knobs against business, quality, risk, and cost outcomes.Read 11 Oct 2026 · dataknobs.com

Intended users

The maker identifies product managers, data scientists and ML engineers, and marketing teams as users.Read 11 Oct 2026 · dataknobs.com

Integrations

The maker's unified platform page names OpenAI and Gemini as example LLM backends to compare.Read 11 Oct 2026 · dataknobs.com

Support

The ABExperiment page invites visitors to request an executive briefing by emailing [email protected].Read 11 Oct 2026 · dataknobs.com

Headquarters

The maker lists Redmond, Washington, USA as its location.Read 11 Oct 2026 · dataknobs.com

AI automation

The maker says autonomous AI agents can generate variant designs or conversational flows and simulate user interactions for preliminary results.Read 11 Oct 2026 · dataknobs.com

Analysis

The maker says LLM-powered analysis summarizes experiment results and produces implementation-ready insights.Read 11 Oct 2026 · dataknobs.com

Assistant experiments

It can compare chatbot prompts, models, retrieval strategies, tools, and assistant responses.Read 11 Oct 2026 · dataknobs.com

Deployment options

The maker says ABExperiment can be deployed independently or combined with KreateWebsites, KreateBots, Data Products, EKIP, Knobs, and Kontrols.Read 11 Oct 2026 · dataknobs.com

Evidence loop

Experiment evidence can be used to promote configurations into Data Products, KreateWebsites, KreateBots, and EKIP.Read 11 Oct 2026 · dataknobs.com

Experiment workflow

Teams define hypotheses, variants, exposure rules, and success metrics, then use evidence to promote configurations.Read 11 Oct 2026 · dataknobs.com

Governance

The maker says experiments are connected to the same Data Product, lineage, policy, and feedback loop, with Kontrols handling policy, approvals, compliance, and release conditions.Read 11 Oct 2026 · dataknobs.com

Model experiments

It can compare approved models and infrastructure on quality, latency, safety, cost, task completion, and business value.Read 11 Oct 2026 · dataknobs.com

Optimization loop

Experiment evidence can be used to promote better configurations into Data Products, KreateWebsites, KreateBots, and EKIP.Read 11 Oct 2026 · dataknobs.com

Outcome measures

It measures outcomes including conversion, engagement, completion, quality, compliance, operational efficiency, cost, latency, and downstream impact.Read 11 Oct 2026 · dataknobs.com

Security and controls

The platform describes Kontrols as enforcing governance, privacy, compliance, accessibility, policy, and release gates.Read 11 Oct 2026 · dataknobs.com

Security and governance

The platform page says experiment hypotheses, variants, exposures, outcomes, and promotion decisions connect to Data Product lineage, policy, and feedback loops.Read 11 Oct 2026 · dataknobs.com

Support/contact

The ABExperiment page invites visitors to request an executive briefing through [email protected].Read 11 Oct 2026 · dataknobs.com

Unified testing

The maker describes a central dashboard for tests spanning website interfaces, conversational assistants, LLM features, and data processing pipelines.Read 11 Oct 2026 · dataknobs.com

Website experiments

It can compare website layouts, journeys, conversion paths, content, calls to action, forms, and navigation.Read 11 Oct 2026 · dataknobs.com

What it tests

It can test website layouts, journeys, conversion paths, content, bot prompts, models, retrieval, tools, recommendations, weights, thresholds, and scoring logic.Read 11 Oct 2026 · dataknobs.com

Where Specifiction places it

• A/B Testing Tools#30 of 527.6