65%
automation rate on contact centre workflows
NOS
80%
reduction in document processing costs
Sonae Sierra
6–12
weeks from kickoff to production deployment
GenOS delivery model
Why GenOS
What makes GenOS different from Kore.ai
01
LLM-native. No intent training burden.
Kore.ai's NLP core requires intent annotation before it can handle your language. GenOS uses frontier LLMs natively. Describe what the agent should do and it does it. No annotation sprints. No entity training. No retraining cycles when your language or workflows evolve.
02
In production in 6–12 weeks
The average Kore.ai implementation takes two months (G2, 470 reviews). That is configuration only. The team required to maintain it comes on top. GenOS's Forward Deployed Engineer delivers a scoped, integrated deployment and stays accountable for quality and improvement after go-live.
03
One platform for all three surfaces
Kore.ai's AI for Work, AI for Service, and AI for Process are separate products with separate governance. GenOS runs employee assistant, customer service agents, and document automation on a single control plane: same RBAC, same audit trail, same improvement loop across everything.
Why switch to GenOS
Teams choose GenOS over Kore.ai when
- You need to be in production in weeks. You do not have a team to build and maintain the platform.
- Your use case involves unstructured language. Kore.ai's NLP requires intent training. GenOS is LLM-native from day one.
- You want one governed platform across employee assistant, customer service, and document automation. Kore.ai splits these across three separate products.
- Post-go-live improvement matters: Kore.ai leaves the customer responsible for tuning; GenOS's FDE owns the quality loop
- Your process has high exception rates requiring judgment. GenOS's human-in-the-loop is production-grade. Kore.ai's is configurable but heavyweight.
Feature by feature
| GenOS | Kore.ai | |
|---|---|---|
| Architecture | LLM-native: frontier models handle language without intent annotation | NLP-based core extended with LLMs: requires intent training and entity annotation per use case |
| Time to production | 6–12 weeks with a Forward Deployed Engineer; go-live is in the contract scope | 2 months average implementation time (G2, 470 reviews); varies by SI and configuration complexity |
| Deployment model | Named FDE builds and runs the deployment; customer does not need to configure anything | Customer or SI builds and maintains; Kore professional services is a separate engagement, not included |
| Contact centre integrations | Available via integration; not native to Genesys, Avaya, or Nice | Deep native connectors for Genesys, Avaya, Nice, Salesforce Service Cloud, ServiceNow |
| Platform breadth | Three surfaces (Assistant, Service, Supervisor) on one platform with shared governance | AI for Work, AI for Service, AI for Process: broad coverage but each product has its own admin |
| Governance layer | Unified RBAC, audit trail, and policy guardrails across all surfaces in one control plane | Governance exists per product; no single control plane across employee and customer-facing surfaces |
| Post-go-live improvement | FDE-led improvement loop: usage analysis, knowledge audits, quality evaluation, model routing | Customer owns maintenance: bot flows break when knowledge changes; no built-in improvement framework |
| Analyst recognition | Growing; European market focus | Strong: Gartner Magic Quadrant, G2 Enterprise Leader, Forrester coverage |
| Human-in-the-loop | Structured exception queues with full context, approval workflows, and correction history | Human handoff configurable; context to agents is limited without custom build |
| Multilanguage support | Inherits from underlying LLMs; strong for European languages | 50+ languages with dedicated NLP training; mature for large-scale multilingual deployments |
Frequently asked
GenOS vs Kore.ai: what is the difference?
Kore.ai is a platform you staff a team to build, configure, and maintain, with an NLP core that needs intent training. GenOS is LLM-native, delivered by a named engineer, in production in 6 to 12 weeks, and improved continuously after go-live.
Does GenOS require intent training like Kore.ai?
No. Kore.ai's NLP core needs intent annotation and entity training per use case. GenOS uses frontier LLMs natively: describe what the agent should do and it does it, with no annotation sprints or retraining when your language changes.
How long does a GenOS deployment take compared to Kore.ai?
GenOS reaches production in 6 to 12 weeks with a Forward Deployed Engineer, with go-live in the contract. Kore.ai implementations average around two months for configuration alone (G2, 470 reviews), plus the team needed to maintain it.
Bottom line
Kore.ai gives you a broad platform and leaves the building, intent training, and maintenance to you. GenOS is LLM-native from day one, live in 6–12 weeks, and continuously improved by the engineer who owns it. You need AI in production. You do not need a team to run the platform. GenOS is built for that.
See in practice
Next step
Book a working session. We run it in your environment, on your data.