- Artemis Agent Platform: Builds and operates enterprise agents with structured, programmable behavior.
- Agent Blueprint Language: Defines orchestration, memory, tools and policies explicitly.
- Enterprise Search: Connects agents to governed business content and data.
- Guardrails and Governance: Supports controlled execution, tracing, auditing and policy enforcement.
- Observability: Monitors agent behavior and performance across enterprise deployments.
- Model Independence: Allows organizations to work across model and infrastructure choices.
Kore.ai
Enterprise-grade AI agent platform for work, service, and process automation
Updated August 1, 2026
Kore.ai Overview
Kore.ai is an enterprise agent platform for building, orchestrating and governing AI agents across service, work and business processes. Its current flagship platform is Artemis, which uses Agent Blueprint Language to define structured orchestration, memory, tools and controls while remaining independent of a single model provider.
The platform combines enterprise search, channel connectors, guardrails, tracing, auditing and observability. Older XO and AI-for-Service documentation remains available, so Artemis should be read as the current platform direction rather than a company-wide rename.
Key Features
Pricing
| Plan or service | Current price | Key details |
|---|---|---|
| Essential | Contact Kore.ai | Plan exists in official billing documentation; no public dollar rate |
| Advanced | Contact Kore.ai | Plan exists in official billing documentation; usage and add-ons vary by product |
| Enterprise | Custom pricing | Enterprise scale, governance, service and deployment terms by quote |
Pricing source: https://docs.kore.ai/ai-for-service/manage-assistant/plan-and-usage/billing-and-payments/
Pros
| Comparison | Where Kore.ai may fit better |
|---|---|
| Google Dialogflow | Kore.ai focuses on governed, multi-agent enterprise orchestration rather than a single conversational interface. |
| Microsoft Copilot Studio | Model-independent architecture can suit organizations that do not want one cloud ecosystem to define every agent. |
| Amazon Lex | Artemis combines agent design, enterprise search, guardrails and observability in a broader platform. |
| IBM watsonx Assistant | Agent Blueprint Language gives technical teams an explicit way to define orchestration and controls. |
| ServiceNow AI | Kore.ai can span service, work and cross-application processes outside a single workflow platform. |
Cons
| Comparison | Tradeoffs to check |
|---|---|
| Google Dialogflow | Kore.ai does not publish a simple self-serve dollar rate and usually requires sales engagement. |
| Microsoft Copilot Studio | Teams deeply standardized on Microsoft may prefer native administration and licensing within that ecosystem. |
| Amazon Lex | The wider platform and governance surface can create a steeper learning curve than a narrower bot service. |
| IBM watsonx Assistant | Plan names exist in billing documentation, but public usage economics require a tailored quote. |
| ServiceNow AI | Organizations should validate connectors, implementation ownership and support scope for their exact systems. |
Reviews
- G2 Review (Rating: 4.6/5 from 502 reviews): The current pool highlights ease of use, low-code development and integrations. Common concerns include learning curve, usage limits and performance or loading issues; competitor-authored marketing reviews are excluded.
