Why Polar
An in-house sLLM removes the data-leakage problem
Run it on-premise or in a private cloud, gain accuracy from training on internal data, and optimize the business with customer-specific fine-tuning and RAG.
Commerce-specialized sLLM
Trained on high-quality commerce data, so the AI capabilities are tuned to commerce work.
On-premise security
On-premise deployment supports internal data processing and access control; external integrations require separate data-transfer policies.
Tuned per customer
Apply the foundation model flexibly per customer and adapt it to their own data through RAG.
Public LLM vs Polar
Public LLM or Polar: what changes
How you adopt AI changes both the cost structure and how data is managed. Compare the traits and the trade-offs of each approach.
Public LLM
Using a public LLM through its API
- Quick to apply through API integration
- Trained on vast data and updated often
- No hardware to build; cost follows token usage
- Sensitive information such as trade secrets or customer data may be sent outside, so policies are needed
- Hallucinations are possible, so results need verification
Polar
Built on-premise or in a private cloud
- 1Runs on-premise or in a private cloud
- 2Training on internal data improves accuracy on commerce work
- 3Custom fine-tuning and RAG fit it to your business
What to plan for
- Upfront infrastructure cost (GPUs, servers)
- Engineering work to tailor it to your environment
Evolution
AI is moving from features to a platform
Commerce is shifting from adopting individual AI features to consolidating them into a single enterprise AI platform.
- As-WasBefore
AI adopted feature by feature
AI for e-commerce
Teams adopted AI one function at a time to raise operational efficiency. Summarization, content generation, and demand forecasting each existed as a separate tool.
- Business data summaries and analysis
- Product and marketing content generation
- Inventory and revenue forecasting
- As-IsToday
A commerce-specific AI platform
POLAR — an enterprise AI platform built for commerce
Built on a private LLM that understands commerce data, it delivers search, recommendation, content generation, and other AI services from a single platform.
- Chatbot
- Semantic and image search
- Commerce+
- To-Be2025 onward
AI that keeps optimizing operations
AI-Driven Commerce Operations
Customer data and operational data connect on one AI platform to deliver personalization, operational automation, and decision support.
- Personalization
- Automation
- Operational optimization
- Decision intelligence
Successful commerce AI is not finished by the model alone. It turns into business results when a platform that understands customer data, work processes, and the operating environment comes together with domain expertise.
POLAR — Plateer's Optimized LLM and Applications with Reliability
How it works
How Polar works
From receiving a question through retrieval and verification to the final answer, Polar combines a domain-optimized model with RAG to produce grounded commerce answers.
How Polar works — a user question passes through LLM Processing (query analysis, generation request, answer verification, answer generation) and the RAG Pipeline (search filter weighting, knowledge base, most similar data extraction) to produce an answer grounded in retrieved sources.
Detailed architecture
Core Technology
The core technology, tuned for commerce
Domain-optimized model
Training on e-commerce data plus custom fine-tuning produces a dedicated AI model tuned to commerce.
Technical diagram
RAG performance
Vector embedding delivers fast, precise retrieval, and a ReRANKER model picks up even fine contextual differences.
Technical diagram
Fine-tuning techniques
FFT, PEFT, SFT, and DPO among others are used to push model performance further.
Technical diagram
Korean language model
Korean performance is strengthened on a ModernBERT base, running a high-performance embedding model that captures the latent meaning of Korean text.
Technical diagram
Use Cases
Commerce-specialized agents
Across search, shopping, and support, Polar raises customer experience and operating efficiency with agents tuned to commerce.
AI Search Agent
AI search agent
An AI search engine that makes finding things precise.
- Intent-aware semantic search
- Image search without keywords
Technical diagram
AI Shop Agent
AI shopping agent
A hyper-personalized shopping assistant that differentiates the customer experience.
- AI product comparison summaries
- Personalized recommendations
Technical diagram
CS Agent
Customer service agent
Commerce AI that improves the support experience and operating efficiency.
- Messages tailored to each product
- Key points of reviews
Technical diagram
FAQ
Frequently asked questions
What is Polar?
Polar is a private, e-commerce-specialized sLLM (small LLM) developed by Plateer. Trained on commerce-domain data, it powers search, recommendation, and support capabilities while keeping enterprise data safe on-premise.
How does an sLLM differ from a large LLM?
An sLLM is a smaller language model optimized for a specific domain. Polar trains on high-quality e-commerce data and is tuned with fine-tuning and RAG, aiming for accuracy and operating efficiency suited to commerce work. Actual performance depends on the training data and configuration.
How is data security managed?
It can run on-premise or in a private cloud to manage data processing and access. External integrations require separate review of data-transfer scope and policies. Internal data improves accuracy while the data itself stays inside the organization.
How does Polar work with XGEN?
Polar is the commerce-specialized model layer used to build search, shopping, and customer-service agents on top of the XGEN Agentic AI platform. Combined with XGEN's Agent Builder and ModelOps, you design and run commerce AI services end to end.
After Deployment
We stay with you after the rollout
It doesn't end at the build. Training that lands the capability inside your team, and technical support that keeps it running steadily.
Commerce-specialized AI
Start your commerce AI with Polar
Transform search, shopping, and support with a private sLLM built for e-commerce. We design the scope and approach together, around your situation.