The lightest model,the deepest value

sLLM optimization fine-tunes and quantizes small language models in the billions-of-parameters range so they run on your own infrastructure without external APIs. With a narrowly defined task, a small model reaches sufficient quality, and pruning, distillation, and quantization bring practical inference speed on a single GPU or even CPU servers. Data never leaves the organization and there is no per-call billing.

Cortexys starts the design from security requirements. We assume an environment where data cannot leave, select and serve models under that constraint, and integrate with existing internal systems and databases. Because quantization can degrade quality, we build a task-specific eval set first and measure quality before and after quantization on the same benchmark before deploying.

Advanced RAG is a retrieval-augmented generation system that searches internal documents and generates answers grounded in them. New documents flow into answers without retraining, and every answer carries citations that can be verified. Hybrid retrieval combining BM25 and dense search plus reranking raises retrieval precision, and a verification step suppresses content not supported by the sources.

Cortexys looks at parsing quality before retrieval. Internal documents mix tables, layouts, and scans, and when parsing fails everything downstream fails with it. We wire the existing document permission model into retrieval filters so restricted documents never leak into answers, build retrieval eval sets from real work questions to manage improvement numerically, and can deploy the entire pipeline on-premise.

Multimodal VLM solutions understand images and video and reason about them in text. Beyond recognizing objects, they interpret symbols on drawings, convert document tables into data, and summarize what is happening in footage. Real-time detection is handled by lightweight detectors in the YOLO family, while interpretation and reporting go to the VLM, with the two layers composed by role.

Cortexys designs around validation on field data. Real-world images with varying lighting, angles, and contamination behave differently from public benchmarks, so we establish evaluation criteria on field data first and fit the model to it. We validated this approach in a YOLOv11-based detection PoC for Renault Korea on an actual manufacturing floor. Since visual data often cannot leave the internal network, on-premise GPU serving is our default consideration.

An AI agent is a system in which a language model carries out multi-step work by calling tools and APIs. Given a goal, it plans on its own, takes actions such as querying or updating internal systems, checks intermediate results, and adjusts the next step. Complex work is handled by multi-agent structures where agents with divided roles collaborate.

Because an agent holds execution authority, Cortexys designs the safeguards alongside it. We embed guardrails that define and enforce the allowed range of actions, technology we validate through guardrail4agent, a model we developed and released publicly. Agents sit on top of existing system APIs to minimize new infrastructure, and human approval points for failures and uncertain judgments are placed at design time. An AI agent build for a major corporate talent development center is currently in progress.