Description

Kore.ai is one of the leaders of the enterprise conversational AI and agentic AI market, operating since 2014. Founded by Raj Koneru with headquarters in Orlando (Florida, USA) and a large R&D centre in Hyderabad (India), the company develops a full stack for building and deploying conversational assistants and autonomous AI agents for large enterprises. The flagship offering is the XO Platform (Experience Optimization) — a low-code environment for designing, training, and deploying virtual assistants handling 100+ languages and 35+ channels (web, mobile, IVR/voice, WhatsApp, Microsoft Teams, Slack, SMS, email, Facebook Messenger).

In 2024 Kore.ai launched GALE — a GenAI orchestration platform for the enterprise: LLM-agnostic (OpenAI, Anthropic, Google, Cohere, Llama, Mistral, NVIDIA NIM), with native RAG, function calling, guardrails, multi-agent orchestration, and full production telemetry. Dedicated industry variants include BankAssist, HealthAssist, RetailAssist, ITAssist, and AgentAssist (assistant for contact-centre agents). The platform is LLM-agnostic with an emphasis on governance, compliance, and deployment in regulated environments.

Scale: 500+ enterprise customers (including PNC Bank, Cigna, Coca-Cola, Roche, AT&T, Airtel), 200M+ monthly users, more than 1.5B interactions per year. Compliance: SOC 2 Type II, HIPAA, GDPR, PCI DSS, ISO 27001, FedRAMP. Kore.ai is consistently named a Leader in the Gartner Magic Quadrant for Enterprise Conversational AI Platforms (2022–2024). Funding: ~USD 240M (Series E 2024 led by FTV Capital, with participation from NVIDIA NVentures, NEA, Nicola Wealth, and Vistara Capital).

MLOps Lifecycle

12/17 supported

Model Registry

Versioning — model artifact versioning

Approval workflows — approval workflow before production

Immutable artifacts — immutability of stored versions

Lineage tracking — tracking data and model relationships

4 / 4 supported · none unsupported

Feature Store

Online serving — real-time feature serving

Offline storage — feature storage for training

Streaming ingestion — streaming ingestion (Kafka, Flink)

0 / 3 supported · 3 unsupported hidden

Prompt Management

Prompt registry — central prompt repository

Versioning — prompt versioning and history

Testing frameworks — A/B testing and prompt evaluation

3 / 3 supported · none unsupported

Monitoring

Data drift detection — input data drift detection

Concept drift detection — concept drift detection

Hallucination monitoring — LLM hallucination monitoring

Bias evaluation tools — bias evaluation tooling

3 / 4 supported · 1 unsupported hidden

Human-in-the-Loop

Labeling services — data labeling tools

RLHF workflows — reinforcement learning from human feedback

Manual override — manual override of model decisions

2 / 3 supported · 1 unsupported hidden

Data & Knowledge

Applications

12

Architecture & Mechanisms

25

Security

Developer Ecosystem

SDK Languages

PyPythonJSJavaScriptTSTypeScript

API Type

RESTGraphQL

Community & resources

Templates library

Quickstarts

API Reference

Tutorials

Pricing & Business Model

See full pricing

Pricing models

Usage-based

Tiered subscription

Resource quotas

Per project

Per user

Cost alerting

SLA & Support

99.9%uptime SLA

StandardEnterprise 24/7

Supported AI Models

23

Supported AI Systems

3

Description

MLOps LifecycleiMLOps LifecycleFull model lifecycle: registry, feature store, prompt management, monitoring and human-in-the-loop.

Model Registry

Feature Store

Prompt Management

Monitoring

Human-in-the-Loop

Data & KnowledgeiData & Knowledge ManagementData connectors, vector database integration, native vector search and data management (PII, provenance, synthetic data).

ApplicationsiAI ApplicationsDomains and use cases this platform is best suited for — from RAG and fine-tuning to scientific research.

Architecture & MechanismsiArchitecture & MechanismsArchitectural foundations and modern AI processing methods that are natively supported or used by this platform.

SecurityiEnterprise SecurityCertifications, access controls and data-protection features essential for corporate deployments and cloud privacy compliance.

Developer EcosystemiDeveloper EcosystemDeveloper resources: available SDKs, supported programming languages, and infrastructure features and model-deployment methods.

Pricing & Business ModeliPricing & Business ModelBilling models (usage-based, provisioned throughput), resource limits and SLA parameters (uptime, support tiers).