Systematic replacement of manual, repetitive data movement or decision steps with orchestrated software workflows executing deterministically or with bounded AI assistance.
Ordered sequence of triggers, transforms, decisions, and outputs that achieves a repeatable business result.
Tool (e.g., Make, Zapier, n8n, Power Automate) that chains app actions via triggers, conditions, data transforms, and error handling.
Integration Platform as a Service: cloud environment for connecting disparate SaaS/DB endpoints using low/no‑code components.
Event (webhook, schedule, poll, manual) that initiates a workflow execution.
Push callback from a source app to a destination URL delivering event data in real time.
Scheduled retrieval of data to detect changes when push events are unavailable.
Programmatic interface exposing structured operations on an application’s data or functionality.
Software development toolkit providing packaged methods for interacting with an API or platform.
Robotic Process Automation: UI‑level scripting that emulates human clicks/keystrokes for systems lacking robust APIs.
Business Process Management: discipline and tooling for modeling, optimizing, and governing end‑to‑end processes.
Extract, Transform, Load: move and reshape data before landing in a target store (often for analytics).
Extract, Load, Transform: land raw data first (warehouse) then transform in‑place for flexibility and speed.
Central analytical store (e.g., BigQuery, Snowflake) aggregating structured data for BI, reporting, and AI retrieval.
Operational sync pushing modeled analytics data back into SaaS apps for activation.
Customer Relationship Management system storing contacts, companies, deals, and related engagement history.
Enterprise Resource Planning system integrating finance, inventory, procurement, and sometimes HR modules.
Large Language Model: probabilistic model that generates or transforms text conditioned on prompts and context.
Structured input (instructions + context + constraints) provided to an LLM to elicit a desired output.
Parameterized prompt pattern with variable slots for dynamic data insertion ensuring consistent LLM behavior.
Numeric vector representations capturing semantic meaning of text for similarity search and retrieval.
Store optimized for similarity search over embeddings (e.g., Pinecone, Weaviate) supporting RAG pipelines.
Retrieval‑Augmented Generation: fetch relevant documents (via embeddings) and inject into LLM prompt to ground outputs.
Looping system that selects actions/tools iteratively toward a goal using LLM reasoning and tool feedback.
Classification (0–5) describing depth of AI usage from none to agentic multi‑tool autonomy.
Design pattern inserting human review, approval, or correction steps within an automated chain.
Central messaging backbone distributing events to multiple consumer workflows, decoupling producers and consumers.
Augmenting a core record with external or derived attributes (firmographics, sentiment, classification).
Detection and consolidation of duplicate records to maintain single source of truth.
Standardizing data formats (e.g., country codes, casing, date formats) for consistent downstream processing.
Step enforcing validation, enrichment, or cleansing before a record advances in a workflow.
Controlled storage and rotation of credentials/API keys to minimize exposure risk.
Periodic replacement of access keys/tokens to reduce window of compromise.
Tracking, diffing, and rolling back workflow definitions (exports/JSON) as managed artifacts.
Holistic insight via logs, metrics, traces enabling rapid diagnosis of workflow behavior.
Persistent structured records of events, inputs, outputs, and errors for auditing and debugging.
Continuous measurement against thresholds (latency, failures) triggering alerts on anomalies.
Automated re‑execution logic with back‑off to recover transient failures without manual intervention.
Property ensuring repeated execution of an operation yields a single consistent effect.
API‑imposed cap on request frequency to protect service resources; exceeding triggers throttling or errors.
Alternate action or data source invoked when primary step fails or confidence is low.
Policies and controls overseeing workflow lifecycle, access, compliance, and risk management.
Categorizing data by sensitivity (e.g., public, internal, confidential, regulated) to enforce handling rules.
Personally Identifiable Information: data that can uniquely identify an individual and requires regulated handling.
Key Performance Indicator: quantifiable metric tracking progress toward an operational objective.
Return on Investment: (Net Benefit − Cost) ÷ Cost, used to evaluate automation impact.
Time required for cumulative benefits to recover the initial automation investment.
Primary axis of impact: cost avoidance, revenue acceleration, cycle‑time compression, risk reduction, insight generation.
Assigning numeric priority to leads based on attributes and behavioral signals for focused follow‑up.
LLM output that is fluent but factually unsupported or incorrect relative to provided context.
Minimum model score or rule condition required before auto‑executing next step without human review.
Alternate branch invoked when data, validation, or confidence criteria fail, enabling controlled handling.
Elapsed time between trigger occurrence and workflow completion or output delivery.