Technology | Automation Anywhere

The AI infrastructure for enterprise work.

Integration and Extensibility

Native connectors

SAP, Salesforce, ServiceNow, Workday

GenAI packages

Pre-built integrations for OpenAI, Microsoft, Google, Amazon + support for custom models

API support

REST/SOAP with open standards

iPaaS integrations

MuleSoft, SnapLogic, Workato

Connector Builder

Instantly generates custom connectors

Emerging standards support

A2A protocol, MCP

Autonomy and Orchestration

Agentic Orchestration

Manages decisions and exceptions across AI agents, systems, data, and humans

Human-in-the-loop

Collaborates with humans for validation, decision-making, and exception handling

Monitoring and ROI reporting

Real-time visibility into automation performance and business impact

AI Guardrails & Data Masking

Protects sensitive data and ensures traceability, enforcing responsible AI usage across agents and models

Model and agent evaluations

Validates performance, accuracy, and reliability of deployed AI agents

Identity and access management

Secures authentication, authorization, and role-based access

Customization

AI Agent Studio

Low-code workspace for building custom AI agents

Citizen Developer tools

With free training

Automator AI suite

Auto-creation and rapid deployment

Automation Co-Pilot

Natural language automation

No-code/low-code development

AI-assisted for both technical and business users

Security

Certifications

SOC 1/2 Type 2, ISO 27001, HITRUST, ISO 22301

Encryption

FIPS140 certified AES256 + SSL/TLS

Credential vaults

CyberArk, HashiCorp, Azure, AWS

Access controls

Role-based, SAML, MFA, AD, SSO

Compliance

GDPR with immutable audit logs, PII masking

Infrastructure

16 global datacenters, >99% SLA, 4-hour RTO/RPO

Others respond. We resolve.

Discover Automation Co-Pilot

Hybrid by design.

Probabilistic-only agents hallucinate. Deterministic-only agents stall. Our hybrid model delivers precision with adaptability.

Dimension	Probabilistic-Only	Deterministic-Only	Hybrid Agentic Process Automation
Execution predictability	Low (stochastic outputs)	High (fixed paths only)	Enterprise-High (bounded AI within proven frameworks)
Development complexity	Low (prompt engineering)	High (extensive rule authoring)	Medium (template-based with business context)
Runtime performance	Variable (model-dependent)	Consistent (rule-based)	Optimized (enterprise-tuned with process caching)
Maintenance overhead	High (prompt drift management)	High (rule maintenance)	Low (self-optimizing with business context learning)
Integration flexibility	High (natural language)	Low (structured APIs only)	Enterprise-High (semantic understanding + API orchestration)
Error recovery	Retry with variation	Explicit exception handling	Intelligent escalation (process-aware recovery)
Scalability pattern	Model inference bottlenecks	Linear rule evaluation	Process-optimized (enterprise workload patterns)
Observability	Black box (prompt/response)	White box (rule traces)	Enterprise transparency (full process + decision audit)
Business context awareness	None (generic model)	Static (hardcoded rules)	Dynamic (enterprise-trained on private processes)
Security and compliance	External API dependency	Local but inflexible	Enterprise-grade (private deployment + compliance frameworks)
Process learning	No learning retention	No adaptation	Continuous optimization (business-specific intelligence)
Multi-process coordination	Limited (single-task focus)	Manual orchestration required	Native (enterprise process orchestration)
Stakeholder integration	Developer-dependent	IT-dependent	Business-user accessible (natural language + guardrails)
Cost predictability	Variable (token-based pricing)	Fixed (infrastructure costs)	Optimized (enterprise licensing + efficiency gains)

AI made for mission-critical work.

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