Make every AI and data decision controlled, explainable, and audit-ready
DataKnobs helps bank governance and risk leaders translate obligations and policies into executable controls, constrain model and agent authority, trace decisions to outcomes, and tune independent risk knobs without slowing responsible innovation.
Governance Mandate
Know what is allowed, what happened, why, and what to change.
Kreate
Build
Kontrols
Govern
Knobs
Optimize
Output
Risk-tiered AI and data products with defensible evidence
The governance challenge
AI changes faster than manual governance can review it.
- Policy is trapped in documents. Obligations, interpretations, standards, procedures, and control tests are disconnected from runtime behavior.
- AI expands decision risk. Models and agents can retrieve sensitive data, generate communications, recommend treatment, invoke tools, and influence customers.
- Evidence is fragmented. Teams struggle to reconstruct which data, model, prompt, policy, approval, and human action produced an outcome.
The governance response
Embed control, accountability, and evidence into the operating system.
- Kreate builds governed data products, compliance signals, monitoring workflows, assistants, and agents.
- Kontrols defines permitted data, tools, actions, autonomy, approvals, quality gates, and required evidence.
- Knobs makes thresholds, authority, escalation, sampling, risk tolerance, and policy settings explicit and adjustable.
Platform
One governance lifecycle. Three reinforcing layers.
KREATE operationalizes governed capabilities. KONTROLS defines boundaries and evidence. KNOBS lets authorized owners adjust risk posture and test whether controls work.
- •Convert regulations, policies, procedures, complaints, calls, and transactions into structured obligations and risk signals
- •Build complaint detection, disclosure review, fair-treatment monitoring, control testing, and issue-management workflows
- •Use AI agents within harnesses that constrain data, tools, actions, approvals, and recovery
- •Map obligations to policies, controls, tests, owners, evidence, issues, and remediation
- •Preserve data-to-model-to-agent-to-decision-to-outcome lineage
- •Apply risk-tiered approvals, human review, monitoring, exceptions, incident response, and rollback
- •Tune complaint, fraud, escalation, disclosure, confidence, and human-review thresholds
- •Vary model, data, prompts, tools, autonomy, sampling, and monitoring intensity independently
- •Validate changes against consumer harm, compliance, fairness, quality, operations, and cost
Data Flywheel
Data products are the engine that makes the enterprise flywheel spin.
Once trusted data products are in place, every interaction can create better signals, better AI, better product experiences, and more useful enterprise data. That compounding loop is the Data Flywheel.
The compounding loop
Reusable, trusted assets become the foundation for AI workflows.
Models, agents, and analytics consume cleaner context and improve decisions.
Usage, feedback, outcomes, and operational events create new learning data.
Knobs tune the loop for accuracy, cost, speed, safety, and business performance.
How to Enable a Data Flywheel
Data flywheels do not happen automatically. They are engineered.
DataKnobs enables enterprise data flywheels through four reinforcing capabilities: a semantic foundation, a data product factory, an AI enablement layer, and a feedback intelligence system. Together, they turn enterprise interactions into continuously improving intelligence.
Semantic Data Foundation
Understand enterprise data through metadata, entities, relationships, lineage, trust, and Information Memory so AI systems have business context from the start.
Data Product Factory
Build reusable, governed, discoverable, and API-ready data products such as Customer 360, Risk Profile, Taxpayer Summary, and other domain intelligence assets.
AI Enablement Layer
Power Agent AI, Generative AI, RAG, analytics, and copilots with trusted context, governed data products, and business semantics.
Feedback Intelligence System
Capture usage, decisions, outcomes, and user interactions so every cycle improves the next generation of data products and AI systems.
Information Memory creates understanding. Data Products create reusable intelligence. Feedback creates learning.
The result is a Data Flywheel that compounds enterprise advantage instead of restarting from scratch with every AI initiative.
Learn How to Enable a Data Flywheel →The DataKnobs Difference
Risk cannot be managed with one threshold.
Use orthogonal knobs to control each dimension explicitly.
A knob is an authorized, versioned variable that changes system behavior. Orthogonal knobs separate independent risk dimensions: data access, model behavior, agent authority, customer impact, monitoring, and escalation: so tighter control in one area is not mistaken for lower risk everywhere.
Control what AI is permitted to do.
Permitted data, tools, actions, transaction limits, autonomy, approvals, segregation of duties, escalation, and reversibility.
Control how judgments are made and reviewed.
Models, prompts, retrieval, evidence requirements, confidence thresholds, adverse-action logic, human review, and exception routing.
Control how deeply the system is tested and observed.
Sampling, monitoring intensity, evaluation suites, fairness slices, alert thresholds, evidence retention, review frequency, and release gates.
Controls must be adjustable, attributable, and testable.
A policy says what should happen. A Kontrol enforces the boundary. A Knob provides an authorized change point. Evaluation proves whether the setting produces acceptable outcomes across products and customer groups.
Example: Raise the complaint-classification threshold while holding the model and data constant. Measure missed complaints, false alerts, protected-class differences, operational workload, remediation delay, and customer harm separately.
Axis 1
Consumer Impact
Fairness, transparency, suitability, complaints, adverse effects, remediation.
Axis 2
Compliance
Obligations, disclosures, consent, privacy, retention, policy adherence.
Axis 3
Model & Agent Risk
Validity, drift, hallucination, authority, tool use, autonomy, reversibility.
Axis 4
Operational Risk
Access, resilience, third parties, incidents, workload, cost, recovery.
Governance Operating Model
From periodic review to continuous, evidence-backed assurance.
DataKnobs links obligations, policy, control design, technical enforcement, testing, runtime monitoring, customer outcomes, issues, and remediation in one traceable lifecycle.
Risk Inventory
- Inventory AI systems and data products
- Classify data, decisions, authority, and impact
- Name business, risk, model, and control owners
Evidence Loop
- Monitor decisions, outcomes, and control health
- Test controls and orthogonal knobs
- Record evidence, exceptions, and remediation
Executive Outcomes
- Defensible customer treatment
- Risk-adjusted AI authority
- Faster examinations and remediation
Capabilities
Capabilities for banking governance and risk
Obligation & Policy Intelligence
Structure regulatory text, map obligations to enterprise policy, identify gaps, assign ownership, and track change.
Consumer Compliance Monitoring
Review communications, disclosures, calls, chats, complaints, and treatment signals against approved UDAAP, TILA, privacy, and fair-treatment control frameworks.
Governed Agents & Harnesses
Constrain agent data, tools, actions, autonomy, approvals, escalation, recovery, and evidence capture.
End-to-End Decision Lineage
Connect sources, transformations, models, prompts, tools, policies, reviewers, decisions, customers, and outcomes.
Control Testing & Assurance
Combine deterministic tests, model evaluation, human review, fairness analysis, red teaming, and outcome monitoring.
Risk Knobs & Scenario Testing
Tune thresholds, authority, approvals, sampling, and monitoring; test tradeoffs before controlled promotion.
Use Cases
Measure the outcomes executives and domains share
Use one evidence model across the portfolio while allowing each domain to select the knobs, controls, and targets appropriate to its risk.
Financial Intelligence
Enterprise Automation
- •Call Audit AI
- •Workflow automation
Regulatory + Compliance
Knowledge Intelligence
- •Document understanding
- •Decision support systems
How it works
Start with one material risk, prove the control chain, then scale assurance.
Define the risk and obligation
Identify affected customers, decisions, products, regulations, policies, failure modes, owners, risk tier, and evidence standard.
Implement the control chain
Connect obligations to policies, executable Kontrols, knobs, testing, approvals, monitoring, issues, remediation, and retained evidence.
Validate and continuously assure
Test independent risk knobs, compare outcomes, approve settings, deploy progressively, monitor drift, and update controls when conditions change.
The DataKnobs Thesis
In the GenAI era, the product is not the model it's the data it produces.
Dataknobs turns AI outputs into validated data products that actually work in real workflows.
AI generates data but not value.
Raw LLM outputs are noisy, inconsistent, and unvalidated. Without a product layer, AI remains a prototype not a reliable enterprise system.
AI-native data products by Dataknobs solve it.
Kreate, Kontrols, and Knobs wrap AI outputs in governance, validation, and workflow integration turning model outputs into production-grade data products.
The DataKnobs Platform at a Glance
How Kreate, Kontrols, and Knobs work together to take raw enterprise data through transformation, governance, and optimization producing data products that are usable, auditable, and production-ready.
AI-Native Data Products From Output to Value
AI can generate information at scale, but without validation, structure, and workflow integration that output never becomes a decision-ready asset. DataKnobs bridges that gap with AI-native data products built for real enterprise workflows.
Why Dataknobs
Give risk leaders a defensible line from obligation to customer outcome
- •Trace obligations through policy, control execution, decisions, customer treatment, and remediation
- •Scale Kontrols with sensitivity, authority, impact, reversibility, and regulatory consequence
- •Use Orthogonal Knobs to isolate which setting changed risk: and what it affected elsewhere
- •Reuse approved controls, evidence patterns, and risk configurations across banking products
Choose one material banking risk use case
We will map the obligation, policy, Kontrols, orthogonal knobs, tests, approvals, monitoring, outcomes, and audit evidence.