Arctir
Arctir provides an AI-native knowledge graph platform, Devgraph, that unifies scattered codebases, disconnected tools, and relationship data into a single context layer for enterprises. The platform extracts and enriches context from existing systems to make development workflows AI-ready, with additional services including custom MCP server integrations and AI adoption planning.
- Artificial Intelligence
- Developer Tools
- Enterprise Software
- Software Only
- AI Agents
Funding
Founders
Founder details are not available yet.
Product
Problem
Enterprises struggle with scattered codebases, disconnected tools, and unclear ownership across their development environments. This fragmentation slows down software delivery and creates a significant barrier to adopting AI, since AI systems lack the unified business and technical context needed to produce reliable, useful outcomes.
Solution
Arctir solves this by unifying data, code, relationships, and tools into a single AI-native knowledge graph, providing the business context that AI systems need to operate effectively. The company's flagship platform, Devgraph, turns development workflows into intelligent, context-aware systems by extracting and enriching context from existing codebases, systems, and people relationships. Arctir also offers enterprise knowledge graph construction services, including data model customization, data ingestion, and annotation, alongside AI adoption planning and custom MCP server integrations to ensure seamless deployment within existing toolchains.
Target Audience
Primary customers are enterprise engineering and IT leaders who need to streamline development processes, reduce tool fragmentation, and prepare their organizations for effective AI adoption.
Features
- AI-native knowledge graph platform that connects business data, code, systems, and people relationships into a unified context layer
- Devgraph platform with context extraction, enrichment, and workflow optimization capabilities to make development processes AI-ready
- Custom data model planning and ingestion pipelines for enterprise-scale knowledge graph construction
- Custom MCP (Model Context Protocol) server development and integration for connecting AI tools to proprietary systems
- Annotation and enrichment services to improve the quality and relevance of data used by AI models
- AI adoption planning and governance guidance to align technical implementation with strategic business outcomes