Agents Store

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ZBrain’s Requisition Validation and PO Generation Agent automates the validation of purchase requisitions and generates fully compliant Purchase Orders (POs) without human intervention. Leveraging a Large Language Model (LLM), the agent evaluates requisition inputs against completeness criteria, budget thresholds, and approval policies, and seamlessly transforms validated requests into ERP-ready POs, ensuring speed, accuracy, and policy compliance across the procurement lifecycle.

Challenges the ZBrain Requisition Validation and PO Generation Agent Addresses

Manual requisition validation and PO creation are time-consuming, error-prone, and heavily dependent on human judgment. Procurement teams often face delays due to incomplete requests, non-compliant inputs, and unclear approval routing. Additionally, verifying requisitions against budget constraints and role-based thresholds requires coordination across multiple stakeholders and systems, which slows down procurement cycles and increases the risk of policy violations or financial discrepancies.

ZBrain Requisition Validation and PO Generation Agent streamlines the procurement process by automating every critical step, from requisition intake to PO creation. It uses LLM to check requisition documents for completeness, validate inputs against department-specific budget records and approver limits, and generate clean, standardized purchase orders. All validations are guided by an enterprise knowledge base, ensuring alignment with current policies and compliance mandates. The agent reduces manual workload, accelerates procurement timelines, and ensures that every PO issued is accurate, auditable, and fully compliant, making enterprise procurement more agile, scalable, and intelligent.

How the Agent Works

ZBrain Requisition Validation and PO Generation Agent follows a structured, multi-step process to ensure that purchase requests are validated against organizational policies and transformed into compliant, ready-to-use purchase orders. Below is a detailed breakdown of how the agent streamlines the end-to-end requisition-to-PO workflow.

Requisition Validation and PO Generation Agent Workflow

Step 1: Requisition Ingestion and Pre-validation

In the first step, the agent captures and evaluates the incoming requisition for completeness and structural accuracy.

Key Tasks:

Input Acquisition: Requisitions are received through structured digital forms, uploaded documents (PDF, DOCX), or system integrations.
Template Identification: The agent references the appropriate requisition template to determine mandatory fields and formatting rules.
Field Completeness Check: The agent utilizes an LLM to verify submitted requisitions against the required fields defined in the organizational template, ensuring the presence and format consistency. Missing fields trigger a standardized message to the requester, highlighting the specific issues.
Readiness Assessment: Requisitions that pass the completeness check are prepared for contextual validation.

Outcome:

Structured Input for Downstream Validation: A well-structured and complete requisition document is established, enabling accurate validation in subsequent phases.

Step 2: Budget Validation

Once input completeness is confirmed, the agent evaluates the requisition against relevant budgetary constraints.

Key Tasks:

Knowledge Base Retrieval: Budget records are retrieved from the knowledge base using specific parameters such as department, cost center, project code, location, fiscal year, and CapEx indicator.
Contextual Mapping: The agent infers the applicable fiscal year, standardizes naming conventions, and matches the requisition details to corresponding budget entries.
Budget Compliance Evaluation: The agent uses an LLM to compare the requested amount against the available limit in the matched budget entry.

Outcome:

Budget Validation Decision: Based on the comparison between the requested amount and available budget records, the agent returns one of the following outcomes:
- Approved: The requisition aligns with the available budget allocation.
- Rejected – Budget Exceeded: The requested amount surpasses the allocated budget.
- Rejected – No Budget Record Found: No relevant budget record exists for the specified parameters.

Step 3: Role-based Authority Validation

This step verifies whether the requisition falls within the financial authority limits of the designated approver, whose role and approval threshold are sourced and validated from the knowledge base.

Key Tasks:

Policy Retrieval: The agent accesses organizational policies from the knowledge base to determine role-based approval limits specific to departments and fiscal periods.
Authority Matching: The agent uses an LLM to verify whether the approver’s role and department align with the financial approval thresholds defined in organizational policy. This ensures that only authorized individuals can approve requisitions within their assigned limits.
Approval Threshold Check: The requested amount is evaluated against the approver’s limit to determine if escalation is needed.

Outcome:

Approver Authority Validation: Based on role-based thresholds and fiscal policy rules, the agent determines whether the assigned approver is authorized to approve the requisition. In case of rejection, a clear and standardized message is returned for escalation or correction.
- Approved: The approver’s authority covers the requisition value.
- Rejected – Threshold Exceeded: The requisition requires higher-level approval.
- Rejected – No Matching Record: The approver’s credentials do not align with any existing policy record.

Step 4: Purchase Order Generation

Following successful validation, the agent transforms the approved requisition into a purchase order, ensuring all essential elements are included and ERP-ready.

Key Tasks:

Data Transformation: The agent uses an LLM to extract and format PO fields such as department, items, quantities, delivery location, and project code.
PO ID Assignment: A standardized PO identifier is generated based on the original requisition ID.
Field Normalization: The agent ensures all data is cleaned and standardized, including date formats, currency values, and textual inputs.
ERP Preparation: Fields required for ERP ingestion, such as vendor information, PO status, currency, and creation date, are populated, with placeholder values used if necessary.
Output Formatting: A clean, well-formatted Markdown version of the PO is generated by LLM for further processing.

Outcome:

ERP-ready PO Generation: A complete and validated purchase order is generated, ready for ingestion into ERP systems or dispatch to external suppliers.

Step 5: Human Feedback Loop and Continuous Optimization

To enhance performance and align with evolving business needs, the agent incorporates user feedback to refine its processes over time.

Key Tasks:

Feedback Collection: Users provide structured feedback on requisition validations, PO accuracy, and any other areas needing improvement.
Issue Identification: The agent analyzes feedback to detect recurring issues or potential enhancements in validation logic, data interpretation or PO generation.

Outcome:

Progressive Agent Optimization: The agent evolves continuously, becoming more precise and context-aware with each iteration, reducing manual interventions and strengthening compliance with internal procurement policies.

Why use Requisition Validation and PO Generation Agent?

Time Efficiency: Automates requisition validation and PO creation, reducing manual workload and accelerating procurement cycles.
Accuracy: Ensures precise field validation, budget checks, and approver matching to minimize errors.
Compliance Assurance: Enforces policy-based approvals and budget threshold for consistent, audit-ready outcomes.
Consistency: Maintains standardized formats and logic across all requisitions and purchase orders.
Cost Savings: Reduces manual intervention and error-related rework, lowering overall operational costs.
Scalability: Handles high volumes of requisition requests across departments, projects, and geographies.

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Using advanced AI technologies such as document parsing and natural language processing (NLP), the agent intelligently extracts and analyzes catalog data against predefined rules. It validates product descriptions, pricing thresholds, and classifications, flagging discrepancies for review. This allows for faster, more accurate catalog assessments while significantly reducing the likelihood of human error.

Fully integrable with existing procurement and ERP systems through APIs, the agent automates tasks like catalog approval and compliance reporting while maintaining transparent audit logs. With a human-in-the-loop feedback loop, it enables oversight and continuous learning. The result is faster procurement cycles, improved supplier onboarding, and stronger compliance—empowering organizations to focus on strategic decision-making and value-driven sourcing.

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Technically, the agent performs structured data mapping and validation on attributes like product names, SKUs, pricing, and descriptions. Using a rules-based engine, it aligns incoming entries with existing catalog standards and flags any discrepancies—such as missing or incorrectly formatted fields—for manual review. The integration leverages APIs to securely fetch and import external product data, enabling a seamless flow of information from suppliers into the internal system. While automation handles the bulk of the workload, the agent is designed to maintain transparency and control through human oversight.

By combining automation with targeted manual review, the Master Catalog Integration Agent helps enterprises accelerate product onboarding, improve data quality, and maintain catalog integrity at scale. Its built-in feedback loop ensures that anomalies are promptly addressed, reducing the risk of downstream procurement issues. Ultimately, it offers a flexible, efficient solution for managing complex catalog environments while supporting data governance and business agility.

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The agent integrates seamlessly with systems like ERPs, PIMs (Product Information Management), and commerce platforms to extract product data—such as specifications, images, and pricing. It then uses AI-powered language generation to turn that data into clear, engaging, and SEO-friendly content. Customizable templates enforce brand voice and formatting standards, while built-in logic ensures pricing accuracy. The agent supports high-volume batch processing and allows for easy scaling, making it ideal for businesses managing thousands of SKUs or frequent catalog changes.

With a built-in human feedback loop, teams can review and approve generated content through an intuitive interface before publishing. This hybrid approach ensures both speed and quality—accelerating product launches while maintaining brand integrity. By automating the heavy lifting, the Catalog Content Generation Agent frees your team to focus on strategic growth, helping you deliver better product content, faster.

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