Understand how KREATE, KONTROLS, KNOBS, and ABExperiment work together.
This workshop is for teams evaluating or adopting DataKnobs. We map the platform to your data, apps, identity, governance, and operating model, then use end-to-end solution walkthroughs to make the architecture concrete.
The platform model
Build → Govern → Control → Learn.
The workshop focuses on the interfaces between products, because enterprise value comes from the operating system around the AI: not from a collection of disconnected tools.
KREATE
Creation and assembly layer for AI/data products, assistants, agents, web experiences, and workflow components.
- Context/data integration
- RAG and tool orchestration
- Application/workflow assembly
- Reusable delivery patterns
KONTROLS
Governance and trust layer that makes policies, evidence, lineage, approvals, privacy/security, and risk checks operational.
- Policy checks
- Lineage & audit evidence
- Human review
- Security/privacy guardrails
KNOBS
A way to expose explicit, measurable, adjustable levers across selection, creation, control, and runtime behavior.
- Thresholds and routing
- Data/context selection
- Behavior and release controls
- Config-driven change
ABExperiment
Experimentation and evaluation layer for systematic comparison rather than opinion-driven AI tuning.
- Prompt/model comparisons
- Offline evals
- A/B experiments
- Regression and rollout gates
Enterprise context and data sit at the center. KREATE activates that context into products and workflows; KONTROLS governs the system; KNOBS exposes adjustable levers; ABExperiment measures what works and feeds the next controlled change.
Solution patterns
See the same capabilities reused across different domains.
The point of a platform is not to force every solution into the same UX; it is to reuse the difficult capabilities underneath while tailoring the workflow to the business outcome.
AI assistants
Stocks, tax, financial planning, research, internal knowledge, and domain-specific decision support.
AI agents
AI Webmaster, ecommerce analysis, operations agents, scheduled tasks, monitoring and workflow automation.
Governed enterprise workflows
Complaint management, compliance analysis, review/approval loops, evidence capture, and escalation.
Data products
Company performance, data-center health, location intelligence, legal obligations, catalog intelligence, and other reusable decision assets.
Digital experiences
Data-driven websites, catalog/location pages, SEO, accessibility, publishing, and AI-maintained experiences.
Experimentation
Website, prompt, model, output, and decision-workflow experiments with measurable outcomes.
Two end-to-end deep dives
From source data to decision workflow and feedback loop.
We use two contrasting solutions to show how the same platform concepts adapt to analytical and operational use cases.
Deep dive 1 : Stocks AI Assistant + Data Product
A decision-support experience that combines structured market/company data, earnings content, derived metrics, retrieval, analysis, and user questions.
- Data product: normalized facts, entities, performance/momentum and other derived signals
- KREATE: assistant UX, RAG, tools and analytical workflows
- KONTROLS: finance-oriented policy, evidence, lineage and review
- KNOBS: thresholds, scoring/configuration and runtime control
- ABExperiment: summary quality, prompt/model comparison and controlled improvement
Deep dive 2 : Complaint Management with AI
An operational workflow that ingests customer interactions, identifies potential complaints, classifies issues, routes review, and improves over time.
- KREATE: classification, extraction, summarization, routing and workflow assembly
- KONTROLS: policy checks, audit trail, privacy and human review
- KNOBS: decision thresholds, routing, escalation and response controls
- ABExperiment: quality, false-positive/false-negative tradeoffs, regression tests and rollout
- Operating model: evidence, reviewer feedback, monitoring and change control
Suggested agenda
Adapt the depth to the audience.
A leadership-heavy session emphasizes decisions and adoption. A technical session spends more time on interfaces, schemas, evaluation, integration, and controls.
Architecture & philosophy
Context/data at the center; KREATE, KONTROLS, KNOBS, and experimentation around the product lifecycle.
Product deep dive
Interfaces, responsibilities, deployment patterns, governance, runtime configuration, and evaluation.
Solution walkthrough 1
Stocks AI Assistant + data product from raw sources to governed, measurable decision support.
Solution walkthrough 2
Complaint management from interactions to classification, review, action, evidence, and continuous improvement.
Your adoption blueprint
Map identity, data access, integration, hosting, governance, evaluation, rollout, ownership, and the first solution backlog.
Enterprise adoption blueprint
Finish with your architecture, not ours.
We use the final portion of the workshop to map DataKnobs capabilities into the environment and decide what should be reused, integrated, configured, or built.
Context & data
Systems of record, documents, catalogs, APIs, metrics, permissions, lineage, freshness, and retrieval boundaries.
Runtime & integration
Identity, APIs, tools, workflows, hosting, queues/jobs, model providers, observability, and operating ownership.
Trust & improvement
Policies, evidence, eval datasets, acceptance thresholds, human review, experiment design, release gates, and rollback.
Use the workshop to decide your first DataKnobs adoption path.
Share your stack, primary use case, team roles, governance constraints, and desired outcome. We’ll tailor the deep dives and architecture discussion.