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PlateerAI Labs

Product · Polar

Polar — a private sLLM built for commerce

Polar is an e-commerce-specialized sLLM developed by Plateer. It keeps enterprise data protected while providing a model tuned to the commerce domain, so you can build and run a range of AI applications and services on it.

E-commerce specialized · on-premise sLLM

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

Core enterprise data stays under internal control, so sensitive data never leaves.

Tuned per customer

Apply the foundation model flexibly per customer and adapt it to their own data through RAG.

How it works

How Polar works

From query analysis through generation, verification, and the final answer, Polar combines a domain-optimized model with RAG to produce accurate commerce answers.

How Polar works — user question → Polar sLLM (query analysis, generation request, answer verification) → RAG retrieval (knowledge base, ReRANKER reordering) → accurate answer

Core Technology

The core technology, tuned for commerce

Training on commerce-specific data (products, categories, descriptions) → custom fine-tuning (sentiment classification, text classification, named-entity recognition, intent classification, morphological analysis, summarization)

Domain-optimized model

Training on e-commerce data plus custom fine-tuning produces a dedicated AI model tuned to commerce.

RAG pipeline — Docs → Embedding → Vector DB → Filtered Docs → ReRANKER → Reranked Docs → Response

RAG performance

Vector embedding delivers fast, precise retrieval, and a ReRANKER model picks up even fine contextual differences.

Base LLM → fine-tuning techniques (FFT, SFT, PEFT, DPO) → fine-tuned sLLM

Fine-tuning techniques

FFT, PEFT, SFT, and DPO among others are used to push model performance further.

Korean text → ModernBERT-based embedding model → text as vector (0.027, -0.011, …)

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.

Use Cases

Commerce-specialized agents

Across search, shopping, and support, Polar raises customer experience and operating efficiency with agents tuned to commerce.

AI search — interpreting a query like "I need trousers that are comfortable and slimming" through a meaning-based tag graph for precise retrieval

AI Search Agent

AI search agent

An AI search engine that makes finding things precise.

  • Semantic search
  • Image search
AI shopping — combining click data, purchase history, current trends, preferences, and weather to recommend exactly the right product

AI Shop Agent

AI shopping agent

A hyper-personalized shopping assistant that differentiates the customer experience.

  • Product comparison
  • Personalized recommendation
Customer service — analyzing one-to-one inquiries, product Q&A, and written reviews to read customer disposition and provide a response guide

CS Agent

Customer service agent

Commerce AI that lifts both customer experience and operational efficiency.

  • Tailored messaging
  • Review summarization

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, which gives it higher accuracy and better operating efficiency on commerce work than a general-purpose large model.

How is data security guaranteed?

It can run as an on-premise deployment or in a private cloud, so core enterprise data never leaves. 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.

Enablement

Training tailored to each role and project stage, so the capability lands inside your team quickly.

Operations and support

Maintenance, incident response, and monitoring — we stay responsible for operations after the build.

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.

Ask about PolarSee the XGEN platform