TAKL · Architecture walkthrough

The enterprise footprint

How an AI agent's questions are routed through a governed knowledge layer, and how that resolves into three components: Access Intel, AI Economics Routing Prioritization, and Certified Knowledge Blocks.

Architecture deep dive →

End-to-end footprint

Unstructured content on the left, federated structured data on the right, the Neo4j and Collibra knowledge layer in the center, and the agent on top. Select a route to trace it.

ALL ROUTES select a route

Three routes, one decision

Route 1 always runs first. The knowledge layer then recommends 2a, 2b, or 2c for the question at hand, and the agent follows that recommendation.

The routing decision

Step 1 is the decision itself. The knowledge layer reasons over its metadata and returns a route recommendation at runtime, with the reasoning attached. The AI agent consumes the recommendation; it does not choose the route.

INPUTS · KNOWLEDGE-LAYER METADATA

Criticality of the need

Documents needed to answer

Token-economics model

Match to certified metrics and business logic

Persona, classification, inherited permissions

STEP 1 · NEO4J + COLLIBRA

Knowledge layer weighs the question

Routing is a prioritization, not a cost minimizer. Cost is one input among several; the goal is the best fit for the need.

RETURNS Recommended route + reasoning, explainable and auditable

RECOMMENDS ONE OF

2aBrute force$$$$$

Critical need and a known set of documents holds the answer. Accuracy outweighs cost.

2bSemantic search$$

Very large corpus and an approximate semantic match is good enough. RAG stays first-class.

2cCertified context and data$

Question matches a certified metric harvested from the BI reports. Skips documents entirely.

Both 2a and 2b are improved versions compared to traditional approach guided by context metadata available in Knowledge Layer; AI Agents know which chunks, files, slices (selected chunks across files) to consume.

Path 2c is superior offering based on when certified context and data is available; becoming the more common path as the organization advances to a higher volume of Certified AI Ready assets.

The three TAKL components

Each component maps to a part of the footprint. Together they make the routing recommendation possible.

T·A

Access Intel

Before any route is taken, the graph resolves who is asking, what the data is, and what they inherit, so the agent only sees what that persona is entitled to.

persona category classification permissions inheritance

ENABLED BY Collibra + Neo4j; Automated Persona Permissions Framework

K · ROUTE 1

AI Economics Routing Prioritization

The knowledge layer recommends 2a, 2b, or 2c at runtime by weighing criticality, document count, token economics, and certified-metric matches.

2a 2b 2c

ENABLED BY Knowledge-Layer Metadata + Token-Economics Model

L · ROUTE 2C

Certified Knowledge Blocks

Business logic harvested from BI reports and document slices from enterprise content are certified through the stewardship framework. They become reusable blocks: Certified Context Packages and Certified Data Products, ready to plug into any AI consumer. Each block is built once and reused everywhere, which raises the speed and accuracy of AI responses.

report scanning certification KPI serving

ENABLED BY Starburst, Deasy Labs + Collibra; Governance Certification Framework

Technology-neutral: Neo4j or similar (Graph DB) · Starburst or similar ("query-anything-where-it-lives") · Collibra or similar (Data Governance & Metadata Catalog) · Deasy Labs or similar (Docs chunking, cross-file slices) · BigID or similar (auto data classification).