From model fine-tuning to multi-agent orchestration—we bring systematic methodology and technical depth to every AI implementation.
Full-stack AI development across data, models, agents, and deployment infrastructure.
Structured data pipelines and fine-tuning workflows for domain-specific model adaptation.
Workflow analysis for AI automation opportunities. System integration with existing infrastructure.
Autonomous agents with reasoning, planning, and tool-use capabilities. Multi-agent coordination for complex task execution.
Retrieval-Augmented Generation for knowledge grounding. Model Context Protocol for structured tool integration.
Application development with embedded AI capabilities. Unified experience across mobile, web, and desktop.
A research-driven, iterative methodology focused on measurable outcomes.
Identify opportunities, quantify inefficiencies, define objectives.
Design data pipelines with privacy controls and governance frameworks.
Rapid iteration with working prototypes and user feedback loops.
Model fine-tuning, pipeline implementation, deployment with monitoring.
Ongoing monitoring, optimization, model updates, and technical support.
An engineering-first team building practical AI solutions since 2014.
ByteBridge started as an IT consulting firm in 2014 and has since specialized in AI development. With teams in San Francisco, Seoul, and Beijing, we focus on building AI agents and productivity tools grounded in real-world application.
We prioritize technical depth over surface-level integration. Every project follows a structured process—requirements analysis, architecture design, iterative development, and systematic evaluation.
A decade of IT and AI experience
Research and engineering-first culture
SF · Seoul · Beijing
from bytebridge import Agent
class EnterpriseAgent(Agent):
def __init__(self):
self.capabilities = [
"reasoning",
"planning",
"tool_use",
"memory"
]
self.rag = RAGPipeline()
self.mcp = MCPConnector()
async def execute(self, task):
context = await self.rag.retrieve(task)
plan = self.reason(task, context)
return await self.run(plan)For project inquiries, technical consultations, or partnership discussions.
[email protected]Initial consultation available
30-minute technical discussion to scope project requirements.