Architecture map
Where enterprise context, data products, models, agents, APIs, and user experiences fit together.
Enterprise AI working session
See how DataKnobs moves from data and context to governed AI outcomes.
A hands-on platform and solution workshop for data, AI, engineering, product, risk, and architecture teams. Learn the KREATE–KONTROLS–KNOBS operating model, then apply it to two end-to-end solution architectures.
Half-day or full-day
Architecture + controls + evaluation
Adaptable to your domain
Workshop flow
Context & dataSources, policies, users, decisions, and operating constraints.
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KREATE · KONTROLS · KNOBSCreate the capability, govern the boundaries, expose measurable levers.
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Production outcomeAssistant, agent, data product, workflow, or governed automation.
What participants should leave with
A working mental model: not a catalog tour.
The session is designed to help a team decide how an AI capability should be built, controlled, evaluated, deployed, and improved inside its existing enterprise environment.
Control model
Which policies, approvals, lineage, privacy boundaries, and human-review points belong in the workflow.
Evaluation model
How to evaluate data, retrieval, prompts, models, agents, business outcomes, and operational burden.
Adoption plan
A candidate starting use case, deployment boundary, integration path, and next-step implementation plan.
DataKnobs platform model
Three responsibilities: create, govern, improve.
The workshop now centers the actual DataKnobs trio. ABExperiment and evaluation tooling are shown as ways KNOBS can be measured and tested: not as a fourth peer pillar.
Create the capability
Turn enterprise data and context into reusable data products, digital experiences, assistants, agents, and workflow services.
- Data products and signals
- Websites, portals, APIs, and reports
- RAG assistants and tool-using agents
- Workflow orchestration and automation
Govern the execution
Define what the system may access, say, decide, and do: and retain evidence of how those boundaries were applied.
- Lineage, identity, policy, privacy
- Human review and approval gates
- Auditability and evidence
- Risk and compliance checks
Make behavior adjustable
Expose the choices that materially affect quality, cost, risk, speed, and autonomy so they can be measured rather than buried in code.
- Data and context selection
- Prompt, model, retrieval, thresholds
- Tool access and agent autonomy
- Evals, A/B tests, rollouts, optimization
The operating loop
Build once. Learn continuously.
Participants see how the trio works throughout the lifecycle rather than as three disconnected product demos.
Discover
Define decision + evidence
Clarify users, context, risk, data, and measurable business acceptance.
Create
Build with KREATE
Assemble the data product, application, assistant, agent, or workflow.
Govern
Apply KONTROLS
Set policy, identity, approvals, lineage, privacy, and execution boundaries.
Measure
Evaluate KNOBS
Compare prompts, models, context, thresholds, cost, latency, and autonomy choices.
Operate
Improve with evidence
Monitor production behavior and adjust approved knobs without redesigning the entire system.
Default deep dives
Two very different solutions. The same platform responsibilities.
The workshop keeps the original Stocks AI Assistant and Complaint Management examples because they demonstrate both public/market intelligence and a governed enterprise workflow.
Stocks AI Assistant & Data Product
Explore how market data, filings, earnings calls, scores, and user questions become a governed decision-support experience.
- Structured + unstructured data product design
- RAG and tool patterns for company research
- Quality/evaluation for summaries and signals
- Knobs for models, prompts, data windows, thresholds, and explanation depth
Market data + filings + earnings → governed data product → assistant/tools → evaluation → user-facing decision support.
Complaint Management with AI
Explore how calls, chats, tickets, policy context, classifications, and human review become a production workflow in a regulated environment.
- Classification, extraction, root-cause themes, and routing
- Policy, PII/privacy, lineage, approvals, and audit evidence
- Threshold knobs for complaint/non-complaint decisions
- Model evaluation plus operational acceptance and escalation burden
Customer interaction → classification/extraction → controls + human review → routing/action → monitoring and improvement.
Agenda
Half-day for alignment. Full-day for working design.
Exact timing is tailored to the team. The full-day format adds deeper architecture work, group exercises, and an adoption blueprint.
PART 1
Business outcome & context
Map users, decisions, data, risks, workflow constraints, and acceptance criteria.
PART 2
Trio deep dive
KREATE, KONTROLS, KNOBS and how they connect to existing enterprise platforms.
PART 3
Solution architecture #1
Stocks/company intelligence: data product, assistant, evaluation, and runtime controls.
PART 4
Solution architecture #2
Complaint management: regulated workflow, policy evidence, thresholds, and human review.
PART 5
Your adoption blueprint
Choose a starting workflow and document architecture, controls, knobs, evaluation, and rollout steps.
Deliverables
Make the session usable the next day.
For private workshops, the session can be oriented around the customer's own architecture and result in working artifacts rather than generic training notes.
- Target workflow and business acceptance definition
- Reference architecture and deployment boundary
- Data/context inventory and integration questions
- KONTROLS checklist and approval points
- Candidate KNOBS and evaluation plan
- Implementation priorities, risks, and next steps
Use your architecture, data, and workflow as the workshop case.
Share the team roles, target use case, existing cloud/data environment, governance constraints, and desired outcome. DataKnobs can tailor the session and replace either default deep dive when another example is more relevant.
FAQ
Workshop questions.
Is this a generic AI training course?
No. The workshop uses DataKnobs platform responsibilities and concrete enterprise solution architectures to help teams make design and adoption decisions.
Can the deep dives be changed?
Yes. Stocks AI and Complaint Management are useful defaults, but a private workshop can substitute a workflow that better matches the team's domain.
What should participants leave with?
The target outputs are an architecture map, controls/evaluation checklist, candidate knobs, and a practical adoption plan for a selected workflow.