KR + AI FOR YOUR DATA TRANSFORMATION, AUTOMATION & AUTONOMY
AI can’t transform or act on data it doesn’t fully understand.
Your data is one of your most important assets. Knowledge Refinery® (KR) extracts the deterministic knowledge that gives it meaning—the governing rules, relationships and coded logic that create, transform and use it, across data at rest, in motion and in use.
What KR + AI means: KR extracts deterministic knowledge from your coded world. Any AI agent—plus the inference systems each orchestrates—can consume KR knowledge directly through MCP via KR Advisor, providing a deterministic foundation for accuracy, traceability, and guardrails for agentic AI.
As your systems change, refresh KR’s extraction to keep the operational blueprint current and give AI updated deterministic operational knowledge. Through KR Assistant, AI transforms that knowledge into human-understandable explanations that help your team understand complex systems and data, strengthen oversight, and trace conclusions back to the underlying operation.
Clean data is not necessarily complete data.
Schemas can be valid. Catalogs can be current. Known data lineage can be accurate. Data streaming analysis can observe data in motion. But your data picture can still be incomplete.
Data does not exist in isolation. Its meaning and integrity are shaped by relationships, transformations, governing rules, and the application logic that creates and uses it. A view of the data environment alone cannot supply that implementation knowledge.
For AI, that difference matters: records can look clean to the tools observing them and still lack the coded knowledge needed to transform, migrate, or act on them correctly.
Your data lives in three states. Today’s tools don’t see all three completely.
Data-at-Rest
Databases, data lakes, legacy stores
Partially visible: structures and stored data
Data-in-Motion
ETL processes, APIs, streams, data lineage
Limited visibility: movement and observed flows
Data-in-Use
Application code, stored procedures, business rules
Largely invisible: the coded logic that creates, validates and uses data
<<< KR extracts across all three data states — including the coded logic that connects them. >>>
Observation shows what happens. Extraction delivers the operational blueprint.
Observed determinism is evidence of what is observed: structures, movement, and execution.
Extracted determinism is the knowledge pulled from encoded databases and applications that create, transform, store, and use the data—structured into an operational blueprint.
The two are complementary. Observation shows what is visible (execution-based). Extraction provides the operational blueprint that shows the rest of the picture. You have the view from the outside in; KR adds the view from the inside out.
Your KR-delivered data knowledge includes:
- STRUCTURE & MEANING: Schemas, catalogs, data dictionaries, terms
- RULES & LOGIC: Business rules, code paths, operational logic
- RELATIONSHIPS & DEPENDENCIES: Relationships, dependencies, referential integrity
- MOVEMENT & TRANSFORMATION: Data lineage, transformations, duplication, replication
- WORKFLOW & EXECUTION: Workflows, processes
Observation shows what happened. Extraction makes the operation knowable and traceable.
When the picture is incomplete, transformation risk increases.
These aren’t edge cases. They are the predictable costs of changing, consolidating or automating data without seeing the relationships and governing rules behind it.
- Migrations stall. Dependencies and transformations surface late, forcing rework.
- Referential integrity breaks. Relationships across data states are missed or altered during migration and consolidation.
- Replication becomes duplication. Mergers and migrations turn intentional replication into accidental duplicate records that distort analytics.
- Compliance becomes harder to prove. Incomplete data lineage makes it hard to show what changed, why, and whether requirements were preserved.
KR supplies the implementation knowledge AI cannot get from current approaches.
KR extracts and connects the deterministic knowledge embedded across the enterprise assets made available to it, so the meaning, relationships, and traceability survive every change. With KR, you:
- Extract and plan — the specific workflows, data lineage, rules, terms, dependencies, structures, and code paths across data-at-rest, data-in-motion and data-in-use.
- Translate and migrate — trace records and their transformations across applications, and design the ‘to-be’ data environment with KR Data Consolidator, so the relationship map and referential integrity carry into the target environment.
- Replicate on purpose — plan intentional replication and avoid accidental duplication.
- Verify completeness — show whether governing rules, regulatory requirements, and integrity survived transformation, and verify results and execution against your operational blueprint, your knowledge baseline. KR Assistant identifies boundaries, influencing factors and missing assets, and asks targeted questions, so stakeholders can validate what was extracted.
How the approaches compare:
Approach
AI-only
Limitation
Cannot extract governing rules and relationships from incomplete inputs; may fill gaps with approximations
RESULT
Hallucinations, compliance risk, no traceability, no referential integrity
APPROACH
Manual-only
LIMITATION
Cannot reach the needed scale or transparency across complex environments
RESULT
Bottlenecks, incomplete coverage, human error, outdated by delivery
APPROACH
Schema-only
LIMITATION
Captures structures but misses much of the logic in code across all three states
RESULT
Missed dependencies, integrity violations, incomplete referential integrity
APPROACH
KR + AI
LIMITATION
KR’s deterministic knowledge across all three data states, consumed by your team, AI agents (and their inference systems) and correlated with observed evidence
RESULT
Traceable data lineage, referential integrity, and a deterministic knowledge baseline for verifying execution
AI turns incomplete knowledge from an analysis problem into an action problem.
AI is moving from analysis and recommendation into data transformation, workflow execution and, increasingly, autonomous action. The further AI moves from advising people to acting on enterprise data, the greater the cost of a missing rule or relationship, and the more important it becomes to know how much autonomy your business can adopt with confidence.
A probabilistic model can reason over what it is given. It cannot preserve a rule, relationship or transformation it cannot see. When that knowledge is missing, it fills the gap with an ungrounded extrapolation—and in automation, that extrapolation can spread at machine speed and scale.
The goal is for probabilistic AI to consume a deterministic foundation: knowledge extracted from your data and from the systems and logic that create, transform, store, and use the data.
Enable successful, probabilistic AI with a deterministic foundation.
Transform your data without losing what makes it correct.
Seven issued U.S. patents
Model-agnostic
Consumed by your team, AI agents, and the inference systems they orchestrate through MCP
Deterministic foundation for AI accuracy and execution
KR extracts deterministic knowledge across data-at-rest, data-in-motion and data-in-use: an operational blueprint that lets you change with traceability, preserve integrity, and act at machine speed and scale with confidence—whether people or AI produce the results.