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We build tools for our needs. Now you can use them in production.

That's why we created XCT — an open protocol where models propose and deterministic tools execute — because we got tired of LLMs breaking things in production. Everything we ship, we use ourselves first.

What We Do

Three things we do well. Not everything — just these.

Cloud & Automation

IaC, CI/CD, Kubernetes, observability. We set up and maintain the infra so your team can focus on the product.

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Model Training

LoRA and full fine-tuning of open-source models for your domain. We handle data, training, eval, and deployment.

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LLM Workflows

Pipelines for document validation, data enrichment, and process automation using LLMs with guardrails.

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How We Work With AI

XCT — Execution Control Transfer

Born from real incidents with autonomous AI in production. The rule is simple: the model proposes, the system validates, deterministic tools execute. No exceptions.

Model Proposes

The model looks at the context and suggests one action. Just one. No multi-step plans, no autonomous chains.

System Validates

The system checks the proposal. It can approve, reject, or ask for something else. The model doesn't get to override.

Tools Execute

Deterministic tools do the actual work. If something fails, it comes back as data — not as a crash.

The Polaris Ecosystem

Tools we built for ourselves and now maintain in production.

Polaris Core

C++ bindings for llama.cpp with Python integration. Handles inference with JSON early-stop for XCT.

C++Python

Polaris v2

Institutional chatbot with RAG, voice, and multi-platform support. For companies that need a conversational assistant with guardrails.

Live Demo

Polaris v3

Deterministic agent powered by XCT, customized to each use case. For critical automation — infra, finance, compliance — where the model proposes and the system decides.

XCTFastAPI

XCT-Qwen3-4B

LoRA-trained model for XCT execution. Not a chatbot — it proposes actions within protocol constraints.

HuggingFace

Got a problem that fits?

If you need control over LLMs in production — not demos, not proofs of concept — we should talk.

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