AI Automation Rules: Overview and Governance
As enterprise workflows increasingly rely on automation, integrating Artificial Intelligence directly into your operational pipelines can drastically reduce manual review cycles, enrich product data, and eliminate tedious data entry.
The Encodify platform provides native AI automation rules that allow your system to securely transmit structured item data, text fields, and digital media directly to generative AI models. Whether you are auditing marketing collateral for brand compliance or extracting structured product specifications from unstructured vendor documents, the analytical outputs, risk scores, and extracted values are automatically ingested back into your workflow in real time.
Please Note: AI Automation Rules is a premium add-on module. Don't see it in your workspace? Contact your Encodify Engagement Manager to review your subscription and request access.
1. Available AI Automation Rules
The platform offers two specialized multimodal rules tailored to distinct operational tasks. Both rules can process combined payloads of text and media files (such as PDFs and images), but they differ significantly in how they process and return data.
Use the directory below to determine which rule fits your business use case and access its step-by-step configuration guide:
Rule Type | Primary Use Case | Key Capabilities |
|---|---|---|
Quality assurance, compliance auditing, risk scoring, and media evaluation. | Evaluates payloads against custom instructions to return a qualitative narrative summary and a quantitative confidence score. | |
Data parsing, content enrichment, and automated field population. | Reads unstructured text and media to extract distinct data points (e.g., product titles, prices, summaries) and maps them directly into structured system fields (text, numeric, or date). Features optional live search grounding and pre-save data validation. |
Key Operational Differences
When to Use Which?
Choose AI Assessment when you need the AI to act as a judge or auditor. For example: Submitting a product image and promotional copy to verify if it adheres to brand safety guidelines and returning a compliance score.
Choose AI Data Extraction when you need the AI to act as a data parser or copywriter. For example: Submitting raw vendor spec sheets or product images to automatically generate and populate clean product titles, formatted prices, and marketing summaries into specific system fields.
2. Core Operational Principles & AI Governance
When designing automated workflows that leverage Generative AI, administrators must balance the speed of automation with rigorous quality control. We recommend adhering to the following governance principles across all AI rule deployments:
The "Human-in-the-Loop" (HITL) Philosophy
Artificial intelligence models are probabilistic engines, meaning they generate responses based on statistical likelihood rather than rigid programming. Consequently, AI should be deployed to assist and accelerate human decision-making, not replace critical quality assurance entirely.
Transparent Labeling: Always configure UI Help Text on fields populated by AI (e.g., "This field is populated by AI; please verify for accuracy.").
Approval Gates: Use AI outputs to advance items into a human review queue rather than triggering irreversible external actions (such as publishing a campaign or approving a vendor payout).
Prompt Engineering & Preventing "Conversational Drift"
The accuracy of an AI rule depends heavily on the structure and clarity of the instructions (the prompt) provided to the model.
Be Explicit and Structured: Avoid vague directives. Clearly separate background context, the specific task, and the required output format.
Map Instructions to Target Fields: When configuring returned data point within rules, generative AI models can occasionally suffer from "Conversational Drift"—returning conversational filler (e.g., "Here is the product summary you asked for:") instead of raw data. To prevent this, explicitly list your target fields in the prompt along with strict character or formatting constraints (e.g., "Target name: must include product name, length 20-40 characters. Target summary: length 50-100 characters.").
External Sandbox Testing: Before hardcoding complex prompts into an Encodify rule, iteratively test and refine your directives using external AI sandboxes such as Google AI Studio or Gemini. Once your prompt consistently yields the desired structured output across edge cases, migrate it into the platform.
Data Validation and Live Search Grounding
Rules have advanced controls to ensure database hygiene and data enrichment:
Enforcing Field Validation: When extracting structured data (such as dates, numerical prices, or character-capped strings), always enable the Validate Target Fields toggle. When enabled, the system validates the AI's output against your system field rules before saving. If the AI returns an invalid value, the save is safely skipped, and the item status is set to "Failed".
Using Grounding Responsibly: The rules features an optional Grounding toggle that allows the AI to incorporate live external search results along with your provided payload. Enable Grounding only when your data extraction requires supplementary real-time world knowledge (such as verifying a manufacturer's public green certification, or needs to understand world events that have occurred since the model training cut off). For purely internal data parsing, leave Grounding disabled to reduce processing latency and maintain strict data privacy.
3. Technical Considerations & Payload Hygiene
Rules share an identical underlying execution architecture. To ensure seamless API execution and prevent process degradation, design your system rules within the following technical boundaries:
Loop Prevention via Status Gatekeeping: AI rules must always be configured with strict status gatekeeper conditions. Ensure your triggering logic excludes items where the Status field already equals
"In Progress,""Completed,"(or"Success"), or"Failed". Without strict status gatekeeping, an automated field update from the AI will inadvertently re-trigger the rule, resulting in an infinite execution loop.Payload Volume Constraints: Transmitting excessively large volumes of data—such as a single file exceeding 100MB or a payload containing more than 30 individual files—will significantly degrade the quality and accuracy of the AI's output. Whenever possible, configure your rules to send compressed file renditions rather than raw master files.
Asynchronous Execution: AI evaluation is an asynchronous process. When an item triggers a rule, its status temporarily shifts to
"In Progress"while the system communicates with the AI service. Ensure your broader team workflows account for this processing window. A best practice is to configure a status workspace, which gives an overview of e.g., which rule execution are in flight, processing errors, etc.Race Conditions: If the
"Copy referenced items"feature is enabled for a main module, be aware that an AI rule might trigger prematurely before all linked items have finished generating from the copy operation.File Integrity and Encryption: Ensure that uploaded media files adhere to standard naming conventions. Files containing special URL-encoded characters (such as
%0Dor%0A) in their filenames that cannot be downloaded via the UI cannot be analyzed by the AI service. Additionally, files with malformed double extensions (e.g.,.pdf.exe) or encrypted/password-protected PDF documents will immediately cause an evaluation failure.