Capture the "why" behind code to reduce AI context costs

Capture the "why" behind code to reduce AI context costs targets a $6.0B = 2M engineering teams × $3K ACV total addressable market with medium saturation and a year-over-year growth rate of 15% YoY — developer tools and AI-assistant adoption growth (industry analyst estimates, 2023-2025).

Key trends driving demand: LLM adoption in developer workflows — as teams adopt AI assistants, demand for curated, high-value context that reduces token costs increases.; Shift from static docs to living knowledge — teams prefer automated capture of decisions at commit/PR time rather than separate wikis, creating demand for embedded capture workflows.; Cost sensitivity of token-based APIs — rising usage of LLMs creates a tangible ROI for tools that can reduce tokens and latency.; Rise of semantic retrieval and embeddings — vector search enables small, relevant context bundles which this product can provide as pre-prompts to LLMs..