Arctir helps enterprises accelerate AI adoption by building AI-native knowledge graphs that unify code, business data, systems, and people relationships into a single context layer. Its flagship platform, Devgraph, turns development workflows into context-aware systems through context extraction, enrichment, and workflow optimization. The company also offers custom MCP server integrations and AI adoption planning services to align technical infrastructure with strategic outcomes.
Funding
Funding not disclosed
Founders
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