Business ontology for enterprise AI

Check the business facts
before AI starts the work

The contract body is on revision 3, but its attachments are still on revision 2. If separate agents proceed without checking, the report may mix two document sets. A business ontology clarifies the revision, rules and approval responsibilities before work begins.

See the contract example Explore the platform

This page explains an implementation method. The scope of data connections and automated actions depends on the product release, authorization and project design.

01 · Contract review example

The body and attachments differ. Pause the review.

Clause review and risk assessment must use the same documents. Before assigning work, check the contract ID, body, attachments and revisions. If anything is missing or inconsistent, ask the case owner to confirm it.

Check the submitted documents

Contract body Revision 3

Attachments Revision 2 · check required

Revisions differ. Pause the review until the case owner confirms the attachments.

Explore the contract review model

01
Check the documents
Confirm the contract ID, body and attachment revisions, and both parties.
02
Use one source set
Give clause review, risk assessment and final review the same document list.
03
Apply clear checks
Pause if a revision or source page is missing; automate only explicit checks.
04
Keep people in charge
Authorized staff approve material obligations, payment commitments and signatures.
05
Record what happened
Keep the document revisions, clause locations, reasons, approver and result.

The revision numbers are illustrative. An actual project must check its own submitted documents.

02 · Four common uses of ontology

Start with the problem, then choose the method

An ontology is not a single piece of software. It can clarify terms, map relationships, define business actions or limit agent behavior. These four uses help scope a project; they are not a formal industry classification.

Semantic ontology

Agree on terms and rules

What if teams define a “valid contract” differently?

Define contracts, attachments and signatures, then check for conflicting rules. RDF and OWL can provide standard forms when needed.

Knowledge graph

Map how things relate

Which contracts, people and entities are tied to a risk?

Connect confirmed objects so people can trace relationships and find supporting material. The sources still need checking.

Operational ontology

Define actions and states

When may a case be approved, returned or written back?

Specify case states, action conditions and staff permissions, then connect them to the actual process.

Agent constraints

Set limits for AI agents

What may run automatically, and what needs approval?

Specify available tools, required evidence and human decisions; block actions beyond those limits.

Teams can keep using RAG to find documents. Add relationship, rule or action definitions when the task calls for them.

03 · Product support and project work

The platform provides tools. Business owners define the rules.

DesireCore can manage documents, tasks and approvals. A project still needs the business owner to confirm fields, metric definitions and permission scopes.

AgentFS

Manage documents and rules

Keep documents, rules and processes in reviewable files with revision history.

Project decision:Field meanings, sources and revision requirements

Memory / Knowledge

Keep approved terms and experience

Store confirmed terminology, corrections and lessons within defined scopes.

Project decision:Common terms, relationships and trusted sources

Skills / Decision Trees

Reuse expert judgment

Turn business rules, examples and decision steps into inspectable capabilities.

Project decision:Rule owners, exceptions and test material

SOP / Workflow

Arrange handling and approvals

Separate automatic checks, flexible tasks and human decisions; pause or hand off exceptions.

Project decision:Stop conditions, approval points and delivery requirements

Multi-Agent Runtime

Split work by responsibility

Assign extraction, judgment and review separately, passing documents and reasons along.

Project decision:Role responsibilities and handoff contents

Policy / Receipts

Control access and record results

Limit tool access and retain approvals and execution records.

Project decision:Authorized scope, risk criteria and audit ownership

Explore the platform

04 · Where to start

Pilot a task with a clear problem and checkable records

These are possible applications, not a list of industry projects DesireCore has delivered. Each depends on the organization’s data, rules and approval requirements.

Set handling authority first

Enterprise agents

Separate tasks AI may handle from those that require staff approval.

Identify records and sources first

Data integration

If a customer has different names in two systems, agree how to identify the same one.

Agree on metric definitions first

Conversational analytics

Before answering a revenue question, confirm the formula, reporting period and data scope.

Verify equipment and state first

Digital twins

Match equipment IDs and observed states to the relevant business response.

Map production steps first

Industrial AI

Link equipment, production steps and quality records to support fault analysis.

Map service dependencies first

Observability / AIOps

Match service relationships to alerts to narrow the investigation.

Verify entities and links first

Financial risk

Check ownership and transaction relationships, and retain the basis for risk decisions.

GraphRAG can find related material

It can search along object relationships. A link found in the graph is not a conclusion; check what it means and where it came from.

05 · Practice and modeling examples

See what must be checked in actual tasks

The bid and drawing articles describe work in practice; the contract document is a modeling example. Each distinguishes what the system handles from what staff must decide.

AI Proposal Writing

Prepare a bid from tender documents and company records, with a source for each response.

Documents to check: Tender requirements, qualifications, scoring points and company evidence
Decision criteria: Check requirements, qualifications, scoring points and prohibited claims
Handling steps: Organize requirements, find evidence, draft sections, review risks and produce the document

Staff confirm commitments, pricing, qualifications and the final submission.

Read the site article

Engineering Drawing Review

Identify equipment and connections across drawings, with a location for each finding.

Documents to check: Equipment, lines, instruments, sheets, locations and revisions
Decision criteria: Check connections, identifiers and the agreed design baseline
Handling steps: Read drawings, compare revisions and list issues with source locations

Confirm the design baseline first; qualified engineers review and sign off findings.

Read the site article

Contract Review & Revision Checks

Make sure the body, attachments and findings use the same document set.

Documents to check: Contract, attachments, clauses, parties, revisions and page numbers
Decision criteria: Check revisions, attachment completeness, risks and approval conditions
Handling steps: Confirm documents, split review work, fill evidence gaps, review and deliver

Authorized people retain legal judgment, material risk acceptance and signature.

Explore the modeling example

06 · Project steps

Start with one task, test it, then expand

An FDE is an engineer who works closely with operational staff. Together they check documents, rules and approval responsibilities, then test the process on real cases. This is a way of working, not a separate DesireCore product.

01

First question
Understand the current process

Who handles the task, and where do errors occur?

Review real cases with staff, including documents, systems, common exceptions and time spent.

Output
Process, responsibility and issue lists; current measures

How to check
Actual records, system logs, handling times and error examples
02

First question
Define objects and rules

What must be checked, and against which criteria?

Confirm objects, revisions, relationships, handling conditions, exceptions and approval authority with business staff.

Output
Object descriptions, rule list, access and approval requirements

How to check
Business-owner confirmation and decisions on disputed rules
03

First question
Configure and pilot

How much of the task can the system handle?

Connect required documents and tools, set up automatic checks and human review, and pilot on actual examples.

Output
Pilot workflow, role assignments and result template

How to check
Execution, tool-use and approval records; delivered results
04

First question
Accept, then expand

Are results sound, and is work reduced?

Check quality, time and risk against approved cases. Fix failures before expanding use.

Output
Acceptance result, issue list and improvement plan

How to check
Pass rate, handovers to staff, handling time and staff feedback

07 · Checks before and after use

Review the result, the process and who is responsible

Acceptance should cover whether staff can verify the result, whether the system stays within its authority and whether errors can be traced.

Documents and reasons

Check objects, revisions and sources. Every finding should point back to its original material.

Consistent terms
Complete documents
Verifiable source

Quality of work

Test real cases and exceptions, then compare system results with professional review.

Case pass rate
Reviewer differences
Exception cases

Access and approval

Check tool permissions, human approval points, blocked actions and sensitive-data handling.

Authorized scope
Human approval
Exception blocking

Operations

Track handling time, cost, errors and staff takeovers. Keep a version when rules change.

Task records
Error causes
Revision history

FAQ

Business ontology FAQ

Answers to common project questions

01What is a business ontology?

It is an explicit agreement on what exists in a business task, how things relate, which rules apply and who may decide. In contract review, it identifies the document set and revision, pause conditions and decisions staff must make.

02How is it different from a knowledge graph?

A knowledge graph focuses on relationships between things. A business ontology also specifies rules, process states and authority. A project can use a graph when complex relationship queries are needed.

03If we have RAG, do we still need an ontology?

RAG can find documents, but it does not automatically tell whether they share a revision or who may approve a decision. These requirements need explicit rules.

04Must we model the whole company at once?

No. Start with one task, define only the required objects and rules, test them on real cases, then expand if useful.

05Does DesireCore have a standalone ontology engine?

This page describes an implementation method, not a standalone engine. DesireCore uses AgentFS, knowledge, Skills, Workflows, multi-agent collaboration and access controls to manage related material and processes. Specific scope depends on the release and project design.

06What does an FDE do in the project?

An FDE checks the process, documents and rules with staff, then applies those requirements during configuration and a pilot. Expansion depends on the test results.

Documents and references

Read the examples and source material

The product docs and case articles explain DesireCore’s approach. External references explain ontology methods; they do not imply a product partnership or affiliation.

DesireCore Business Ontology Docs

Objects, links, rules, actions, permissions and acceptance checks.

Contract Review Modeling Example

A worked example of objects, clauses, links, rules and human boundaries.

Four Ontology Approaches (Chinese)

A public analytical framework from Lao Liu Says NLP, not an industry standard.

W3C OWL 2 Overview

Standards foundation for ontology languages, classes, properties, individuals and relationships.

Palantir Ontology Overview

Public explanation of connecting objects, links, actions, functions and governance to operations.

Palantir AI FDE Overview

Public workflow for using natural language across integration, ontology editing, functions, governance and validation.