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Adople builds enterprise AI solutions and AI agents that automate critical workflows, connect fragmented data, and transform information into intelligent action.
AI Ontology: Give Your AI a Living Model of Your Business.
Adople’s AI ontology connects your data, business logic, and workflows into one governed model of how your organization works. AI agents can understand context, support decisions, and take approved action, all from the same foundation.
Target Audience Profile
Built for operations, finance, and data leaders who need AI they can trust in real workflows.
Ontology sys metricsLive telemetry
Graph entities1,428,940
Active MCP profiles48 protocols
Relation link cohesion99.98%
Hallucination rate0.00% (bounded)
Fallback routeDeterministic SQL
Pillar // 01hub
Understand
One shared model of your objects, relationships, and rules. Break down departmental silos into a single enterprise-wide cognitive schema.
Pillar // 02fact_check
Decide
Analytics and AI agents work from the same context, with sources shown. Every deduction is linked to verified source documents and primary tables.
Pillar // 03verified_user
Act
Approved actions run through governed workflows, with people in control. No black-box writes; strict dual-signature validation triggers.
Sec-02 // Core cognitive layer
What Is an Enterprise Ontology?
Most enterprises run on dozens of systems—CRMs, ERPs, unstructured PDF repositories, legacy mainframes, and banking portals. Without an ontology, AI guesses from keywords. With it, AI follows a question across your systems the way your best analyst would.
Interactive runtime schematicManhattan Bus Topology: Systems to Action
01 / Enterprise sources
SAP / NetSuite (ERP)
General ledger, PO ledgers, vendor catalogs
Salesforce / HubSpot
Customer accounts, deal pipelines, SLAs
Doc store / S3 / SharePoint
Master service agreements, vendor PDFs, receipts
Bank APIs & SWIFT
Cash positions, clearing houses, settled wire feeds
Core abstraction fabric
Stateful graphAdople Living Ontology Engine
1. Object identifiers[Entities]
Customer, Invoice, PO, Vendor, Ledger Line, Asset, Approval Limit.
2. Relationship mesh[Links]
Invoice → BelongsTo → PO → Under → Master Contract → SignedBy.
3. Business constraints[Rules]
3% pricing tolerance limit, Net-45 term lock, dual approval ceiling above $50k.
4. Execution primitives[Actions]
Flag discrepancy, route for authorization, stage ERP write, notify auditor.
Selected: All subsystems runningSync rate: 120ms
03 / Consumers & actors
Agentic data analystsmart_toy
Queries ontology without writing blind SQL queries
Workflow execution agentsrobot_2
Performs multistep cross-system orchestrations
Human auditors & CFOperson_check
Inspects provenance chain and clicks approve
Core BI & reportingtable_chart
Always consistent with operational AI views
Sec-03 // System ontology schema
One Model, Two Halves
Enterprise operations break down cleanly into state representation and governed execution. An ontology cannot be purely a data graph; it must be an operational nervous system.
Half A // State definition
Static graphWhat your business is
Objects
The concrete nouns of enterprise operations: customers, accounts, orders, invoices, physical shipments, vendor entities, legal contracts, and banking institutions.
Properties
The precise attributes attached to every object: lifecycle status, settlement amount, signing authority, due date, geographic jurisdiction, and validation flags.
Links
The relationship mesh weaving objects together. Invoices reference POs; POs reference master agreements; customer entities own billing entities.
Half B // Dynamic execution
Action busWhat your business does
Rules & Logic
The governing constraints and operational thresholds. Tolerance margins, contractual late-payment penalties, compliance jurisdictions, and validation protocols.
Actions
Defined, governed computational and operational steps: drafting a wire remittance, routing an invoice dispute, updating a customer tier, generating an audit bundle.
Permissions
Role-based access boundaries down to property-level resolution. AI agents cannot bypass user-level permissions, and actions inherit strict organizational controls.
Sec-04 // Traceable workflow simulation
Illustrative Example: An Invoice Exception
See how the Adople AI Ontology resolves a multi-system dispute across accounts payable, contract documents, and authorization hierarchies in real-time.
Case file: INV-89241 // AP discrepancy flagSimulation status: Ready for human signoff
Layer 01
Objects
Invoice #89241 ($64,250.00)Supplier: Apex LogisticsPurchase Order: PO-2024-991Contract: MSA-AL-2023.pdfApprover: VP of Supply Chain
The entities extracted from heterogeneous enterprise sources and mapped to standardized ontological schemas.
Layer 02
Links
[Invoice #89241] → matches → [Purchase Order PO-2024-991]
[Purchase Order PO-2024-991] → sits under → [Master Contract MSA-AL-2023]
[Master Contract MSA-AL-2023] → designates authority → [Approver: VP Supply Chain]
The AI does not search across loose files; it traverses persistent architectural graph pointers.
Layer 03
Rules
Tolerance threshold
Price ±2%, Quantity 0%
Status: Breach (+4.1%)
Payment terms
Net-45 Standard
Status: Compliant
Approval limit
>$50k: Requires VP Level
Status: Escalation required
Layer 04
Actions
Route the exception to the right approver with a clear summary. Lock the invoice in the ERP to avoid automated batch payments until cleared.
Layer 05
The AI's Role
Synthesized explanation (agent generated)
"Invoice #89241 exceeds PO-2024-991 by $2,550.00 (+4.1%). Clause 14.3 of MSA-AL-2023 permits a fuel surcharge only if regional diesel exceeds $4.20/gal, but published DOT rate for the billable period was $3.89/gal. Routing for variance sign-off."
Provenance: SAP_PO_line#3MSA_PDF_page_14EIA_gov_fuel_index
Layer 06
The Human Step
The approver decides.
The VP verifies the audit trail, reviews fuel pricing delta, and approves or overrides with one click.
Capability set // 01
How AI Works With the Ontology
Our agent architecture interacts with business logic through standard protocols, eliminating probabilistic guesswork.
01 // Grounding
Grounded answers
Responses are bound to specific ontology entities and relationships. No hallucinated vendors or invented balances.
02 // Embedded UX
Models where people work
Integrates directly inside ERP interfaces, Slack channels, email clients, and customized enterprise portals.
03 // Tool protocols
Connected tools via MCP
Utilizes Model Context Protocol to execute database lookups, PDF extractions, and API mutations safely.
04 // Simulation
Scenario analysis
Simulate cash distributions, supply shifts, or currency movements across graph entities before committal.
05 // Human governance
People in control
Autonomous execution halts at predetermined sensitivity thresholds, awaiting verifiable human digital authorization.
06 // Zero drift
Self-auditing telemetry
Continuous vector alignment tracking ensures models do not drift from underlying entity definitions over time.
Foundational architecture // 02
Three Parts, One Foundation
Adople unifies the full lifecycle of enterprise knowledge from declaration to agentic action.
01 // Defineschema
Define the Ontology
Catalog enterprise entities, declare state machines, establish relationship topologies, and define explicit mathematical constraints.
02 // Runsync_alt
Run the Graph Bus
Synchronize live streams from transactional databases, document warehouses, ERPs, and banking endpoints with automated conflict resolution.
03 // Buildterminal
Build Governed Agents
Deploy autonomous workers capable of cross-system workflow execution, conversational querying, and auditable exception remediation.
Pipeline // 03
From Workflow to Working System
A battle-tested deployment methodology to take an operational bottleneck into a governed live agent in weeks.
01 / Connect
Connect Sources
Tap ERP tables, APIs, and document vaults without migration.
02 / Model
Model Reality
Map business nouns, linking verbs, and deterministic validation rules.
03 / Ground
Ground Agents
Expose ontology nodes as bound tools through MCP interfaces.
04 / Act
Govern Action
Establish dual-approval policies and execution sandboxes.
05 / Review
Audit Traces
Review every agent recommendation against source provenance logs.
06 / Evolve
Evolve Schema
Expand nodes as organizational complexity and data feeds scale.
Enterprise governance // 04
Built for Production. Designed for Control.
Six layers of institutional defense designed to satisfy Chief Risk Officers and enterprise compliance audits.
Control 01
Guardrails & Governance
Granular access control policies inherited directly from existing Active Directory / Okta entitlements down to field-level resolution.
Control 02
Telemetry & Observability
Real-time query tracing, latency breakdown, node traversal logging, and token cost tracking across every single execution.
Control 03
Decision Audits
Immutable provenance records capturing exact model versions, source document revisions, and timestamps for every generated step.
Control 04
Privacy Protection
Zero training on customer data. On-premise and VPC isolation options with client-side cryptographic hashing of PII fields.
Control 05
Deterministic Fallback Logic
When probabilistic confidence scores fall below verified thresholds, queries automatically step down to hardcoded rulesets.
Control 06
Change Control
Git-like branching and version control for ontology schemas, permitting migration dry-runs before merging to production systems.
Sec-06 // System comparison
How an Ontology Differs From What You Already Have
Most organizations have invested heavily in chatbots, vector search, or semantic layers. Here is how Adople’s AI ontology bridges the gap to reliable autonomous execution.
| Approach | What it does | What it leaves out |
|---|---|---|
| Chatbot | Answers from general knowledge | Your data, rules, and approvals |
| Search and RAG | Finds relevant passages | How things relate, allowed actions |
| Knowledge graph | Stores entities & relationships | Rules, approvals, actions |
| Semantic layer | Consistent metrics for reporting | Agents that act on learning |
Adople AI ontology | Objects, links, rules, actions, approvals in one layer | Nothing to rebuild — starts with one workflow |
Deployed verticals // 01
Enterprise Applications
Production ontology models deployed across mission-critical institutional operations.
App // 01
Healthcare AI Solutions
Ontologies connecting patient histories, billing codes, clinical protocols, and authorization guidelines without HIPAA compromise.
View Healthcare Architecture
App // 02
Financial AI Solutions
Connecting general ledgers, subsidiary structures, liquidity pools, and compliance mandates for automated closing.
View Financial ArchitectureApp // 03
Enterprise Operations
End-to-end mapping of supply chain nodes, procurement thresholds, inventory replenishment, and ERP change events.
View Operations ArchitectureApp // 04
Document Extraction
Extract structured, schema-compliant entities from messy contracts, trade receipts, and regulatory declarations.
View Extraction EngineApp // 05
Agentic Data Analyst
Conversational intelligence for complex enterprise datasets. Query business nouns without hallucinations.
View Data AnalystApp // 06
Treasury Management
Unify bank, ERP, entity, and currency data into one real-time liquidity graph with strict approval gates.
View Treasury EngineTechnical specification // FAQ
Frequently Asked Questions
Clear answers to common questions about deployment, integration, data security, and governance.
No. Adople operates as an intelligent virtualization layer over your existing architecture. It connects directly to your databases, ERP systems, and document stores via read-only connectors and APIs without requiring centralized data migrations.
Get started // Production fast-track
Start With One Workflow. Bring us one bottleneck.
Don't boil the enterprise ocean. Select one high-friction, multi-system operational workflow—invoice exceptions, contract compliance, or liquidity forecasting. We will map the ontology and show you reliable, autonomous execution in action.