Turning uncertainty into certainty, transforming despair into hope
Company Philosophy
We are a company that directly addresses uncertain or incomplete problems.
The method we choose to solve the problems we select may be a product, a technology, or something else—the form is always determined by what best solves the problem.
Convia exists to analyze uncertainty, structure incompleteness, and create a world where more certain choices are possible.
Team
Seunghwa Lee
CEO
Co-founder
"Most people avoid risk. I face it head-on—and give it structure. If it can't be removed, I turn it into a calculated advantage."
Hakjun Kim
CTO
Co-founder
"I am an entrepreneur who, to turn imagination into reality, uses the brush of action to paint visions of imagination on the canvas of reality."
R&D Projects
What appears as abstract theory, we recast as tangible proof. Where others see uncertainty, we deliver certainty—through the union of math, physics, and engineering.
Topology-based Distributed AI Optimization Framework Research
Format: Research paper and PoC software
Approached structural collapse problems in distributed environments using topological mathematical models. Completed as a framework implementable at a practical level.
Mathematical Model Research for Solving Parallel Problems and Fixed Context Problems
Format: Research paper
Solved semantic loss and context fixation problems occurring in parallel processing. Currently being utilized as the theoretical foundation for productization and structural analysis technology.
MistSeeker Coder
Code structure stability analysis tool. Executable in local environments. Available for immediate download.
MistSeeker B2B
Current Status: MVP level complete, ongoing internal and external testing
Expected Launch: First Half of 2026
Please feel free to contact us if you are interested in early access or technical review.
MistSeeker Vision — Image & Video Understanding Engine
A structural perception engine (MVP) designed to help AI understand images and video in a more physically grounded and interpretable way. It explicitly separates color, curvature, shadow, and text into independent analytical modules, mathematically defines each, and supports 2D, 3D, and time-dependent inputs. The system converts visual information into structured feature vectors suitable for multimodal LLM integration, improving learning stability and accuracy. At its current stage, MistSeeker Vision provides a well-defined framework, core MVP implementation, and a solid educational and research baseline, while being architected for future expansion into high-precision, production-level perception systems.
Network Cocoon — 6G Wireless Simulation & Optimization Tool
A Python-based wireless network simulation and optimization tool designed for real 6G engineering scenarios. It supports preset-based simulations for urban and rural environments, advanced RF modeling including shadow fading, LOS/NLOS analysis, genetic-algorithm site optimization, and extended KPI evaluation such as latency, throughput, coverage, and TCO. With interactive dashboards, real-time parameter tuning, JSON export, and strong extensibility, Network Cocoon is already a usable engineering tool — suitable for researchers, telecom engineers, and organizations needing practical 6G planning insights. It stands as a robust product-level PoC ready to evolve into a commercial or advanced research platform.
Convia Mathematical Program Series
Our foundational research papers exploring advanced mathematical frameworks
Convia Mathematical Program I: Ricci–Schrödinger Correspondence (RSC) and the Hodge Problem
Abstract
This note inaugurates the Convia Mathematical Program series. It presents a programmatic, conditional research framework named the Ricci–Schrödinger Correspondence (RSC), which couples the Ricci flow with a Hodge heat evolution on differential forms. The system is designed to share a heuristic monotonic energy structure that performs both curvature smoothing and Hodge harmonic regularization, placing the regularization of the Hodge structure on complex 3-dimensional projective manifolds within a dynamic analytic setting.
Scope statement: All statements in this note are programmatic and conditional; no claim of a complete proof of the Hodge Conjecture is made. We establish the conditional implication: if the coupled flow forces the cohomology class of the residual current R in the Siu decomposition to vanish, i.e. [R] = 0, then the Hodge Conjecture holds in complex dimension three. The core unsolved question remains why the coupled flow should guarantee [R] = 0.
Convia Mathematical Program II: High-Probability Region Restriction for Cost Reduction in Probabilistic Systems
Abstract
Modern probabilistic computational systems, including large language models (LLMs), fundamentally rely on sampling and exploration. In practice, however, a substantial portion of computational cost arises from exploratory regions of low probability that are not essential for correct decision-making. This note proposes a programmatic research framework for reducing computational cost by identifying and normalizing high-probability regions of a probability distribution and restricting the decision space accordingly. The proposed principle is not limited to LLMs and applies broadly to probabilistic inference, Bayesian decision-making, and stochastic optimization. All statements in this paper are programmatic and conditional; no claim of universal optimality or complete theory is made.
Research & Technology Collaboration
Convia is not simply a company that sells products, but an R&D-focused organization that conducts continuous technological research.
If you are interested in:
Please contact us using the methods below.