|
|
|
|
Agentic approach with low-code agent framework |
No-code approach |
Functions and Chains approach to building applications |
|
Locally deployable agent SDKs |
SaaS platform |
Locally Deployable code |
|
Data remains in customer’s
cloud environment |
Data remains on Kore’s cloud |
Data remains in customer’s cloud environment |
|
Lyzr Agents integrate seamlessly
into Lyzr’s Multi-Agent Automation Platform for seamless expansion to complex workflows in the future. |
Kore is a chatbot platform without
any scope to expand into a workflow automation pipeline. |
Langchain framework helps in development of LLM applications and hence allows scope expansion. |
|
|
|
Community Support |
|
|
|
|
|
|
|
|
|
|
|
24 hours |
NIL |
Typically 1 week |
|
200+ LLMs |
NIL |
LLM integration through libraries
like LiteLLM |
|
Integrates with all leading vector databases, embedding models and runs on any cloud |
Software as a service. |
Integrates with all leading vector databases, embedding models and runs on any cloud
|
|
|
|
|
|
|
Yes. Blog post here. |
NIL |
|
|
Flat - $399 per month. No throttling. No usage-based pricing. |
Usage-based pricing. |
Open Source. But the APIs are charged per user.
|
|
Lyzr AIMS for comprehensive AI Agents management with Agent SDK analytics |
In-built analytics of chatbots |
Langchain LangSmith for LLM observability
|
|
Lyzr Chat Agent can cross-integrate with other Lyzr agents like RAG, Data Analysis, Search and Lyzr Automata (workflow automation). |
|
No native integration. New functions and programs are required to integrate new functionalities.
|
|
600+ RAG pipeline configurations to customize the chat agent as per the customer’s need. |
Nil |
All customizations are manual. No out-of-the-box pre-built pipelines are available.
|
|
|
|
|
|
|
|
|
|
|
|
Yes. Between $10,000 to $75,000. |
|
|
|
Yes. In 48 hours |
|
|
|
How do you build a klarna-style
user-aware, self-improving chatbot?
Klarna has been all over the news the past few days as they released the performance metrics of their customer support chat assistant, which managed to automate 700 full-time agent jobs, handle 2.3 million conversations per month without dropping the CSAT score, and eventually help Klarna save $40M per annum. So how did they do it? What goes behind the scenes?
At Lyzr AI, we took a crack at building the
architecture with Lyzr’s Chat Agent SDK.
And here is how it works.Â
- Klarna has been all over the news the past few days as they released the performance metrics of their customer support chat assistant, which managed to automate 700 full-time agent jobs, handle 2.3 million conversations per month without dropping the CSAT score, and eventually help Klarna save $40M per annum.
- So how did they do it? What goes behind the scenes?
- At Lyzr AI, we took a crack at building the architecture with Lyzr’s Chat Agent SDK. And here is how it works.Â
- The user-aware function helps maintain the user’s profile, updating it in real-time
- The QA example set helps the LLM with few-shot learning to generate user preferred responses
- The long-term memory ensures that the chat agent does not lose context of all previous interactions
- The RLHF function enriches the QA example set
- The in-session short-term memory enables seamless chat exchange
Three demo apps to showcase
GenAI capabilities
Need a demo?
Speak to the founding team.
Speak to the founding team.
Launch prototypes in minutes. Go production in hours.
No more chains. No more building blocks.
No more chains. No more building blocks.
