Enterprise AI Workforce & Automation

Put AI to Work.
Across Your Real Operations.

AI workflow automation helps operational teams handle recurring work across logistics, supply chain, customer operations, and finance. Opryna designs, implements, and manages agents connected to your existing systems, under company rules.

Find Automation Opportunities →Explore Use Cases
Works with your
existing systems
Human oversight
at every step
Measurable
operational impact
Illustrative workflowAI workflow automation connecting shipment documents and logistics operations at a container port
From documents
to done!⤵
Document ReceivedProof of delivery, invoice, etc.
AI Verifies & MatchesExtracts data, validates, checks rules
Review ExceptionsHuman approval when needed
Prepare for ERPApproved data, traceable records
ERP
TMS
WMS
Finance
Real workflow.
Real results.
Works with the systems you already use
SAPORACLE▦ Microsoftodoo♧ Mekarisalesforce▰ Jira✣ slack◉ WhatsAppAnd more
Examples of business systems. Compatibility depends on available interfaces, permissions, and the defined workflow.

MEET OPRYNA

AI workforce for company operations

Agents read operational evidence, compare it with business rules, and prepare or carry out authorized actions. Your team defines the rules, reviews exceptions, and approves changes in authority.

Understand

Read orders, POs, delivery notes, invoices, email, and system records.

Assess

Compare records with business rules and flag exceptions.

Act

Prepare or execute actions within agreed authority.

Improve

Use team corrections to refine documented rules.

OPRYNA SERVICES

Four services, from assessment to ongoing operations

Services can be taken in stages. You may begin with assessment alone.

01

Assessment & Advisory

Map processes, rank opportunities, and assess data readiness.

Output: Opportunity map and pilot recommendation

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02

Agent Implementation

Design roles and business rules, test historical records, and run a controlled pilot.

Output: An agent operating within the agreed pilot scope

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03

System Integration

Connect approved records and workflows to existing ERP, WMS, TMS, email, and applications.

Output: A secure, traceable data flow

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04

Optimization & Support

Monitor performance, refine rules with the team, and expand only after evaluation.

Output: Regular performance reports and ongoing support

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OPERATIONAL OPPORTUNITIES

Where manual work accumulates

A single order crosses sales, production, warehouses, logistics, and finance. Handoffs often create repeated checking and follow-up work.

  • Validate orders from customers and distributors.
  • Follow up supplier POs and material delivery schedules.
  • Track shipments, proof of delivery, returns, and claims.
  • Compare freight and supplier invoices with agreed records.
Explore candidate workflows →
AI workflow automation for freight and operational review
Begin with one process and its supporting evidence.

COMPANY CONTROL

Authority is agreed for each process

Pilots start at L0–L1. The company approves any increase in authority and can lower it at any time. Business policies and customer commitments stay with your team.

See the governance model →
  1. L0

    Observe

    Record results without taking action.

  2. L1

    Recommend

    Provide suggestions; the team decides and acts.

  3. L2

    Prepare

    Prepare transactions or messages for approval.

  4. L3

    Act within limits

    Execute only within company-approved limits.

  5. L4

    Operate autonomously

    Complete a fixed process scope while the team audits.

IMPLEMENTATION JOURNEY

Six stages with a decision at every gate

Indicative duration: about 15 weeks for one process in one location, adjusted for scope and data readiness.

  1. Weeks 1–2

    Discovery

    Interview process owners and review 20–30 sample documents. Map handoffs and rank candidate workflows.

  2. Weeks 3

    Baseline

    Choose one process in one location. Agree volume, processing time, error measures, and acceptance criteria.

  3. Weeks 4–5

    Design

    Approve business rules, exception scenarios, and initial authority in writing. Configure the role in OPRYNA Studio.

  4. Weeks 6–8

    Integration & validation

    Connect approved sources through OPRYNA Connect. Set access and audit trails in OPRYNA Control, then test historical data.

  5. Weeks 9–14

    Controlled pilot

    Operate at L0–L1, compare recommendations with the team, and review performance and exceptions weekly.

  6. Weeks 15

    Evaluation

    Compare with the baseline and decide in writing whether to expand, improve, or stop.

See stages and responsibilities →

SUCCESS MEASURES

Measure a baseline before the pilot

Agree two or three metrics from one area. These are measurement examples, not promised outcomes.

Customer service

Order validation time and response time for order status.

Order fulfillment

Stock recommendation accuracy and time to handle constraints.

Supplier coordination

Follow-up time and delivery delays identified early.

Documents and costs

Review time and validated invoice discrepancies.

EXPLORE THE CATALOG

58 agent roles across eight departments

Explore documented functions and example tasks, then use the assessment to shortlist roles for your company. Pilot scope and integration are validated together.

Browse agent roles →

Which process takes the most team time?

Start with an assessment. Prepare anonymized samples and agree an NDA before sharing company data.

Find your starting team →

AI Workflow Automation: frequently asked questions

What is ai workflow automation?

AI workflow automation connects documents, business rules, and human approvals so teams can move operational work forward across existing systems.

Where should a team start?

Choose one repeatable workflow, gather representative documents, and define what a reviewer must approve before running a pilot.

Can AI replace every approval?

No. Ambiguous records, policy exceptions, and sensitive decisions need a defined human review path. Set thresholds and ownership before deployment.

For a broader framework on managing AI risk, consult the NIST AI Risk Management Framework.