Level 1: Descriptive — What happened?
Historical reporting and dashboards.
.// How It Works Architecture
Architecture built for governed enterprise execution
assistents.ai combines four layers so data ingestion, reasoning, action, and governance remain explicit, testable, and auditable.
- Data Layer
- Intelligence Layer
- Execution Layer
- Governance Layer
.// Architecture Overview
From ingestion to governed action
The stack is designed so each layer can be evaluated independently while participating in one deterministic execution pipeline.
| Layer | Primary responsibility |
|---|---|
| Data Layer | Ingest, normalize, and correlate structured and unstructured context |
| Intelligence Layer | Apply deterministic grounding and rule-aware reasoning |
| Execution Layer | Route and execute actions with autonomy and approval controls |
| Governance Layer | Audit, monitor, authorize, and report compliance evidence |
.// Layer Explorer
Inspect each architecture layer interactively
Switch across layers to review responsibilities, controls, and execution impact.
Layer 1 — Data Layer
Ingests and correlates structured systems and unstructured content into a unified context foundation.
- Connectors (Structured Sources): Schema discovery, incremental sync, and secured ingestion from ERP, CRM, HRIS, service, and infra systems.
- Document Processors (Unstructured Content): Format normalization, structural decomposition, semantic extraction, and embedding generation for documents.
- Entity Resolution: Deterministic and probabilistic matching unify entity references across systems and content types.
- Semantic Correlation Engine: Maintains relationships between entities, events, documents, and transactions for cross-source reasoning.
.// Intelligence Maturity Model
From descriptive reporting to agentic execution
assistents.ai is designed to move organizations beyond static insight toward governed operational action.
Level 2: Diagnostic — Why did it happen?
Root-cause analysis requiring manual investigation.
Level 3: Predictive — What is likely to happen?
Forecasting likely outcomes from signal patterns.
Level 4: Prescriptive — What should we do?
Recommended next actions still requiring manual execution.
Level 5: Agentic — Consider it done.
Detection, decision, execution, and audit chain completed with governance controls.
.// Detailed Workflow Example
Invoice discrepancy resolution
Representative workflow that activates all four layers in a governed loop.
Detect discrepancy in ERP
Pull original purchase order context
Retrieve and parse relevant contract clauses
Classify variance against policy and thresholds
Create tracking ticket in service system
Notify stakeholders and route approvals
Hold payment pending authorized decision
.// Deployment Options
Infrastructure flexibility
Choose deployment topology based on data residency, network, and governance requirements.
SaaS cloudVPC deploymentPrivate cloudOn-premiseHybrid topology
.// What to Evaluate Next
Evaluation checklist for technical and governance teams
Use this list during architecture review and pilot scoping.
- Context coverage across structured and unstructured sources
- Rule encoding quality for business and compliance logic
- Action safety model (approval thresholds and autonomy boundaries)
- Auditability and evidence export readiness
- Operational reliability under error and retry conditions
.// Get Started
Run a technical architecture session on your workflow
We will map data sources, rule logic, execution boundaries, and evidence requirements to a concrete pilot design.
See the architecture in action
Walk through a live session mapping your data sources, rules, and execution boundaries.
Book a sessionExplore the platform
Review how Context Engine, Semantic Layer, and Action Engine work together.
Platform overview