Give AI agents efficient, persistent codebase context without burning tokens

Give AI agents efficient, persistent codebase context without burning tokens targets a $6.0B = 100K engineering orgs × $60K ACV total addressable market with medium saturation and a year-over-year growth rate of 30% YoY growth — based on developer tools + AI tooling adoption trends (Stack Overflow developer survey and industry reports showing strong investment in dev productivity and AI assistants).

Key trends driving demand: LLM adoption in engineering teams is accelerating — this creates demand for tooling that reduces token costs and improves developer productivity.; Shift to retrieval-augmented systems — embeddings + vector search have matured, enabling practical, production-grade context retrieval for LLMs.; Security and data governance concerns are driving enterprises toward on-prem or private-cloud AI tooling, favoring vendors that offer hybrid deployment.; Growing monorepos and polyglot codebases increase cross-module reasoning complexity, creating a niche for specialized context providers that understand repo structure..