Frequently Asked Questions

Engini is an enterprise AI worker platform that deploys autonomous agents to automate complex business workflows. Unlike traditional automation tools, Engini's AI workers reason through tasks, handle exceptions, and execute multi-step processes across your entire tech stack, covering finance, HR, sales, IT, revenue operations, and customer support.

Engini connects to 1,000+ enterprise systems including SAP, Salesforce, HubSpot, Workday, NetSuite, and Zendesk, giving AI workers read and write access to execute real work autonomously.

AI Workers are autonomous software agents that reason, make decisions, and execute multi-step tasks across connected enterprise systems without human intervention. Unlike traditional bots or scripts, AI workers understand context, resolve ambiguity, and adapt to changing data in real time. Each Engini AI worker is purpose-built for a specific business function and can coordinate with other workers for complex cross-functional workflows.

Traditional RPA tools rely on brittle, rule-based scripts that break whenever a system changes or an unexpected exception occurs. They require constant maintenance and fail on anything outside their predefined rules.

Engini uses LLM reasoning layered over business rules to dynamically handle exceptions, adapt to changing data, and resolve ambiguity without human escalation. Where RPA halts, Engini reasons through the problem and continues, making agentic AI a true upgrade path beyond legacy RPA.

Zapier and Make are trigger-action tools for simple, linear workflows between a few apps. They cannot handle multi-step reasoning, exception handling, or stateful orchestration across complex enterprise systems. Engini orchestrates multi-agent workflows with full reasoning capability, system state awareness, and exception resolution, handling the messy realities of enterprise data that Zapier-style tools cannot address.

Engini is built for mid-market and enterprise organizations with complex, high-volume operational workflows. It's used by operations leaders, finance teams, HR departments, IT teams, sales operations, and revenue operations professionals who want to eliminate manual work at scale.

Engini solves operational bottlenecks caused by manual work and fragmented systems. Core use cases include Order-to-Cash (reducing DSO, resolving invoice exceptions), Procure-to-Pay (touchless invoice processing), Employee Lifecycle (automated onboarding and offboarding), Sales Intelligence (continuous prospecting), and Customer Support (autonomous ticket resolution across Zendesk, Intercom, and Freshdesk).

Agentic automation is a paradigm where AI agents perceive context, reason about goals, and autonomously execute multi-step workflows, adapting in real time based on the data they encounter. Unlike deterministic RPA scripts, agentic automation uses large language models (LLMs) to interpret ambiguous situations and make decisions. Engini workers observe system state, reason through exceptions, coordinate with other agents, and self-heal errors without human supervision.

Engini AI workers combine LLM reasoning with live system data and configurable business rules. When encountering a task, a worker pulls real-time data from connected systems, applies business logic and compliance rules, uses LLM reasoning to interpret ambiguous information, executes actions across connected systems, or escalates to a human when confidence thresholds are not met. Every decision is fully auditable.

Yes. Exception handling is a core Engini capability. Traditional automation tools halt on data anomalies or missing fields. Engini workers apply heuristic reasoning to classify and resolve exceptions in real time, for example, cross-referencing order history to resolve a payment mismatch and either correcting it automatically or escalating with a fully documented recommendation. Exceptions are never left unresolved.

The Agentic Workflow builder is a no-code visual designer for configuring multi-step AI worker logic. Teams define trigger conditions, system connections, branching decision trees, and escalation rules without writing code. It supports parallel agent coordination, loop logic, conditional branching, and human-in-the-loop checkpoints.

Engini Tables is a relational data layer providing shared persistent memory for AI agents. Multiple workers across different workflows can maintain context, share state, and access structured data, the connective tissue that enables multi-agent coordination at enterprise scale without relying on external databases.

Engini Chat is a conversational interface that lets users trigger workflows, query agents, and receive structured responses through natural language. A single command can trigger a workflow spanning Salesforce, SAP, Slack, and email simultaneously, connecting conversation directly to your enterprise stack.

Yes. Engini is built for multi-agent workflows where complex tasks are decomposed into subtasks handled by specialized agents coordinating through shared state. An Order-to-Cash workflow might involve a cash application agent, a dispute resolution agent, and a reconciliation agent, all operating in parallel and sharing results through Engini Tables.

Engini pricing is customized based on deployment scope, usage volume, and the number of active AI workers. It is not a one-size-fits-all subscription. Contact the sales team for a tailored quote, or use the ROI Calculator to estimate the value Engini delivers for your operations.

Engini offers a guided pilot program, not a self-serve trial. You deploy a real AI worker against a live workflow in your environment, giving you a true measure of ROI before full commitment. The pilot is scoped with your team to target a specific high-value workflow. Contact sales to start.

All Engini plans include: the Agentic Workflow builder, AI Workers library with pre-built workers, connectors to 1,000+ enterprise apps, Engini Tables, Engini Chat, Engini Apps, and onboarding support. Enterprise plans add custom SLAs, dedicated Customer Success Management, SSO/SCIM provisioning, advanced audit logging, and priority support.

No. Engini pricing is based on usage tiers and the number of active AI workers deployed, not on transaction volume or API calls. This keeps costs predictable and aligned with the operational value delivered, regardless of workflow throughput.

Engini's SOC 2 Type II audit is currently in progress. The platform fully operates under SOC 2 controls: AES-256 encryption at rest, TLS 1.2+ in transit, role-based access control, full audit logging, and continuous security monitoring. Enterprise customers can request security posture documentation. Visit the Trust Center for the full security overview.

Yes. Engini supports full GDPR compliance: data residency controls, Data Processing Agreements (DPAs), right-to-erasure workflows, audit trails for all data access, and data minimization controls. Engini does not use customer data to train AI models. Visit the Trust Center or read the Privacy Policy.

Engini supports HIPAA-compliant configurations for healthcare customers: Business Associate Agreements (BAAs), end-to-end PHI audit logging, role-based access controls limiting PHI exposure to authorized agents only, and data isolation by customer. Contact us to discuss your healthcare compliance requirements.

Engini uses AES-256 encryption for all data at rest and TLS 1.2+ for all data in transit. Integration credentials (API keys, OAuth tokens, passwords) are stored in an encrypted vault and never exposed in plaintext. No customer data is stored beyond workflow execution requirements, and none is used to train AI models.

Yes. Enterprise plans include SAML 2.0 SSO, compatible with Okta, Azure AD, Google Workspace, and other major identity providers, plus SCIM 2.0 for automated user provisioning and deprovisioning. Employee access is automatically revoked when they leave your organization.

The customer retains full data ownership. Engini acts as a data processor, not a data controller, processing data on your behalf with no rights to use it for any other purpose. Engini does not sell customer data, share it with third parties outside service delivery, or use it for AI model training.

Engini connects to 1,000+ enterprise systems via AI-native connectors: ERP (SAP, Oracle, NetSuite, Dynamics 365, Infor), CRM (Salesforce, HubSpot, Pipedrive), HRIS (Workday, BambooHR, SuccessFactors, ADP), ITSM (ServiceNow, Jira), Support (Zendesk, Intercom, Freshdesk), Identity (Okta, Azure AD), Messaging (Slack, Teams, Email). View the full catalog.

Native connectors (Salesforce, SAP, HubSpot, Workday) configure in minutes. Custom API integrations typically take 1–3 days using Engini's connector framework. Enterprise legacy system integrations may require 1–2 weeks. Engini's onboarding team supports all integration setup as part of your deployment.

Yes. Engini has deep, bidirectional ERP integrations: SAP S/4HANA, Oracle ERP Cloud, NetSuite, Microsoft Dynamics 365, and Infor. AI workers can read and write ERP data (invoices, purchase orders, vendor records, payment postings, GL codes) in real time, essential for Order-to-Cash and Procure-to-Pay automation.

Yes. Engini's connector framework supports REST, GraphQL, webhook, and SOAP/XML APIs, enabling integration with any custom internal or third-party system. Custom connectors are built once and reused across all workflows with centralized security and credential management.

Yes. Engini can orchestrate existing RPA bots as subagents within agentic workflows, handle exceptions those bots escalate autonomously, and gradually replace brittle scripts with reasoning-capable AI workers — preserving your RPA investment while systematically upgrading your automation stack. Learn more in the Beyond RPA chapter.

Pre-built workers deploy in hours to 2–3 days. Customized workers based on templates take 1–2 weeks. Fully custom workers for complex enterprise workflows take 2–4 weeks — including workflow design, system integration, exception mapping, and go-live validation. Engini's onboarding team accelerates every deployment with structured templates and guided configuration sessions.

No deep technical skills are required. Operations teams can configure worker logic, define business rules, and monitor performance through a visual interface without writing code. IT and engineering teams can extend workers using APIs and build custom connectors for advanced use cases.

Engini onboarding follows five steps: (1) Discovery — define the workflow, identify systems, map exceptions; (2) Integration setup — connect systems via native connectors or custom APIs; (3) Worker configuration — set logic, business rules, and escalation conditions; (4) Validation — run against live data to verify accuracy; (5) Go-live — deploy to production with monitoring and support in place.

All customers get onboarding support, documentation, and a knowledge base. Growth customers get standard technical support with defined SLAs. Enterprise customers get a dedicated Customer Success Manager, priority technical support, SLA-backed response times, and quarterly business reviews.

Yes. The Build Your Worker feature lets teams design fully custom AI workers: define triggers and inputs, configure multi-step workflow logic with branching and parallel paths, connect enterprise systems, set exception handling rules and escalation conditions, and test against live data before deploying to production.

Yes. Engini offers professional implementation services: workflow analysis, system integration, custom worker development, testing, and go-live support. A network of certified implementation partners is available for regional or industry-specific deployments. Contact us to discuss your needs.

Browse the Workers Library for the full catalog of pre-built workers. Read deployment playbooks for Customer Support, Order-to-Cash, Sales Intelligence, HR, and P2P. Or book a live demo with the Engini team.