1
Frame the outcome
Define the user, decision, workflow, pain point and measurable acceptance criteria.
Customer delivery model
From business problem to governed AI in production.
DataKnobs does not begin with “Which model should we use?” We begin with the decision or workflow that needs to improve, the data and context available, the users who will act on the result, and the operational constraints. Then we design the smallest useful solution that can grow into reusable capability.
Delivery framework
Six stages from discovery to continuous improvement.
The stages are sequential enough to create accountability, but iterative enough to support experimentation. A small proof point can move quickly; a regulated enterprise workflow may require deeper evidence at each gate.
2
Map context & data
Identify systems, documents, signals, permissions, quality issues and lineage requirements.
3
Design the solution
Choose the right mix of predictive ML, LLM/RAG, agent workflows, rules, UX and integrations.
4
Build + govern
Use KREATE to assemble the experience and KONTROLS to embed policy, safety and auditability.
5
Validate in workflow
Test quality, reliability, cost, latency, edge cases and human acceptance—not only model metrics.
6
Operate + improve
Monitor production, expose KNOBS, compare variants and reuse what works across the portfolio.
KREATE · KONTROLS · KNOBS
The platform mirrors the delivery lifecycle.
The three capabilities are not separate slogans. They correspond to the practical work required to build, govern and improve an AI product.
Create the product and context layer
Assemble data products, assistants, agents, websites, retrieval flows, APIs and operational experiences using reusable components.
- Data and knowledge preparation
- AI applications and workflow assembly
- Enterprise integrations
- Productized user experiences
Govern the system
Define the policies and evidence required for a production AI workflow to be trusted, reviewed and operated.
- Data and model lineage
- Evaluation and quality gates
- Access, privacy and policy checks
- Human approval and audit trails
Expose measurable levers
Make important choices explicit so teams can experiment, diagnose and tune the system without rebuilding it.
- Data and context selection
- Prompt/model/tool configuration
- Agent autonomy and permissions
- Thresholds, rollout and experiments
Production gates
A good demo is not the same as a production-ready workflow.
DataKnobs evaluates the system at several levels so a locally impressive model does not become an operational problem after deployment.
Data acceptance
Coverage, quality, freshness, permissions, lineage and known gaps.
Model / agent acceptance
Accuracy, groundedness, robustness, tool behavior and failure modes.
Workflow acceptance
Human usability, escalation, cycle time, false-alert burden and decision quality.
Business acceptance
Whether the workflow creates enough value to justify cost, change and operational risk.
Example: “high accuracy” can still fail the business gate.
A complaint detector that flags too many normal interactions may overwhelm reviewers. An autonomous agent that produces high-quality recommendations but cannot explain or audit its actions may fail governance. Production validation therefore connects technical metrics to the operating workflow.
How an engagement evolves
Start narrow enough to learn. Design broad enough to reuse.
Customers can enter through a proven use case, a single platform capability, a custom delivery project or a broader platform program. The delivery model is designed so these paths can converge over time.
Solution-first
Use when the business workflow is clear. Start from a proven solution pattern, adapt it and validate value quickly.
Capability-first
Use when several use cases share a gap such as governance, website generation, RAG, evaluation or experimentation.
Portfolio-first
Use when an enterprise needs common architecture, controls and operating standards across multiple AI initiatives.
Common questions
How the delivery model works in practice.
Does DataKnobs start with a model or a business problem?
We start with the business decision or workflow, the users, enterprise context and constraints. Model selection follows from those requirements.
How are governance and evaluation handled?
KONTROLS defines policy, risk, traceability and production checks; KNOBS exposes measurable configuration so quality can be tested and improved. Governance is part of the design, not a final review step.
Can a first use case become reusable capability?
Yes. The goal is to reuse successful data, integration, evaluation, control and workflow patterns so the next use case starts farther ahead.
Choose the right starting point for your organization.
Compare platform, product, proven solution and expert-delivery paths—or bring a specific workflow and let the delivery model determine the smallest useful first step.