SHEET 01How It WorksARCHITECTURE

Architecture built for governed enterprise execution

assistents.ai combines four layers so data ingestion, reasoning, action, and governance remain explicit, testable, and auditable.

Talk to an Architect Platform Summary

Data Layer
Intelligence Layer
Execution Layer
Governance Layer

4Architecture layers, independently evaluable

300+Source systems across structured and unstructured

L5Intelligence maturity at agentic execution

100%Actions traceable through the audit chain

SHEET 02Architecture OverviewSTACK

From ingestion to governed action

The stack is designed so each layer can be evaluated independently while participating in one deterministic execution pipeline.

Layer responsibilitiesActive

RefLayerPrimary responsibility

L1Data LayerIngest, normalize, and correlate structured and unstructured context

L2Intelligence LayerApply deterministic grounding and rule-aware reasoning

L3Execution LayerRoute and execute actions with autonomy and approval controls

L4Governance LayerAudit, monitor, authorize, and report compliance evidence

4 layers · one deterministic pipeline

SHEET 03Layer ExplorerLYR-01..04

Inspect each architecture layer in detail

Each layer lists its responsibilities, controls, and execution impact. Read the stack top to bottom: context, reasoning, action, governance.

LYR-01INGEST

Layer 1 — Data Layer

Ingests and correlates structured systems and unstructured content into a unified context foundation.

LYR-01.01

Connectors (Structured Sources)

Schema discovery, incremental sync, and secured ingestion from ERP, CRM, HRIS, service, and infra systems.

LYR-01.02

Document Processors (Unstructured Content)

Format normalization, structural decomposition, semantic extraction, and embedding generation for documents.

LYR-01.03

Entity Resolution

Deterministic and probabilistic matching unify entity references across systems and content types.

LYR-01.04

Semantic Correlation Engine

Maintains relationships between entities, events, documents, and transactions for cross-source reasoning.

LYR-02GROUND

Layer 2 — Intelligence Layer

Applies deterministic grounding and business rules to produce traceable answers and recommendations.

LYR-02.01

Unified Context Engine (Entity Graph)

Assembles relevant context subgraphs per request and tracks evolving entity state.

LYR-02.02

Semantic Governor (Business Rules)

Encodes policy, threshold, temporal, and conditional business rules in machine-evaluable form.

LYR-02.03

Query Processor (Deterministic Grounding)

Transforms natural language into deterministic data retrieval and validates source-grounded responses.

LYR-02.04

Reasoning Engine

Performs analytical, predictive, prescriptive, and agentic reasoning on assembled context plus rules.

LYR-03ACT

Layer 3 — Execution Layer

Routes and executes multi-step actions with autonomy and approval controls enforced at runtime.

LYR-03.01

Action Router

Plans sequential, parallel, and conditional execution paths based on context and policy.

LYR-03.02

System Connectors (Action Execution)

Executes idempotent writes with retries, rollbacks, and system-specific safety semantics.

LYR-03.03

Human-in-the-Loop Manager

Routes approvals, escalations, and delegation chains for high-impact decisions.

LYR-03.04

Retry and Error Handling

Classifies transient vs permanent failures and supports compensating action patterns.

LYR-04AUDIT

Layer 4 — Governance Layer

Captures audit evidence, authorizes actions, and provides continuous governance monitoring.

LYR-04.01

Audit Logger

Immutable records for access, decisions, actions, and outcomes with source traceability.

LYR-04.02

RBAC Engine

Role, group, and attribute-based authorization at data and action level.

LYR-04.03

Compliance Reporter

Framework-aligned reporting for SOC 2, ISO, GDPR, HIPAA, and internal policy controls.

LYR-04.04

Monitoring Dashboards

Operational visibility into ingest health, workflow performance, policy events, and system behavior.

SHEET 04Intelligence Maturity ModelL1..L5

From descriptive reporting to agentic execution

assistents.ai is designed to move organizations beyond static insight toward governed operational action.

L1Descriptive
What happened?
Historical reporting and dashboards.
L2Diagnostic
Why did it happen?
Root-cause analysis requiring manual investigation.
L3Predictive
What is likely to happen?
Forecasting likely outcomes from signal patterns.
L4Prescriptive
What should we do?
Recommended next actions still requiring manual execution.
L5Agentic
Consider it done.
Detection, decision, execution, and audit chain completed with governance controls.

SHEET 05Detailed Workflow ExampleSEQUENCE

Invoice discrepancy resolution

Representative workflow that activates all four layers in a governed loop.

Governed execution loopLive

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

7 steps · all four layers · payment held pending authorized decision

SHEET 06Deployment OptionsTOPOLOGY

Infrastructure flexibility

Choose deployment topology based on data residency, network, and governance requirements.

TOPO-01

SaaS cloud

Supported

TOPO-02

VPC deployment

Supported

TOPO-03

Private cloud

Supported

TOPO-04

On-premise

Supported

TOPO-05

Hybrid topology

Supported

SHEET 07What to Evaluate NextCHK-01..05

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

SHEET 08Sign-offREADY

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.

Schedule Technical Review Pilot & Pricing

Document: assistents.ai · Architecture brief
Layers: Data · Intelligence · Execution · Governance
Lead time: Architecture review to pilot · scoped session
Sheet: 08 of 08 · How It Works

See the architecture in action

Walk through a live session mapping your data sources, rules, and execution boundaries.

Book a session

Explore the platform

Review how Context Engine, Semantic Layer, and Action Engine work together.

Platform overview

Architecture built for governed enterprise execution

Layer 1 — Data Layer

Connectors (Structured Sources)

Document Processors (Unstructured Content)

Entity Resolution

Semantic Correlation Engine

Layer 2 — Intelligence Layer

Unified Context Engine (Entity Graph)

Semantic Governor (Business Rules)

Query Processor (Deterministic Grounding)

Reasoning Engine

Layer 3 — Execution Layer

Action Router

System Connectors (Action Execution)

Human-in-the-Loop Manager

Retry and Error Handling

Layer 4 — Governance Layer

Audit Logger

RBAC Engine

Compliance Reporter

Monitoring Dashboards

What happened?

Why did it happen?

What is likely to happen?

What should we do?

Consider it done.

SaaS cloud

VPC deployment

Private cloud

On-premise

Hybrid topology

Run a technical architecture session on your workflow

See the architecture in action

Explore the platform

Architecture built for governed enterprise execution

Layer 1 — Data Layer

Connectors (Structured Sources)

Document Processors (Unstructured Content)

Entity Resolution

Semantic Correlation Engine

Layer 2 — Intelligence Layer

Unified Context Engine (Entity Graph)

Semantic Governor (Business Rules)

Query Processor (Deterministic Grounding)

Reasoning Engine

Layer 3 — Execution Layer

Action Router

System Connectors (Action Execution)

Human-in-the-Loop Manager

Retry and Error Handling

Layer 4 — Governance Layer

Audit Logger

RBAC Engine

Compliance Reporter

Monitoring Dashboards

What happened?

Why did it happen?

What is likely to happen?

What should we do?

Consider it done.

SaaS cloud

VPC deployment

Private cloud

On-premise

Hybrid topology

Run a technical architecture session on your workflow

See the architecture in action

Explore the platform