Executive Summary

Engineering teams embedding LLMs into code review, generation, and automation struggle with repeated API token spend, truncated context windows, and the absence of persistent "why" metadata that forces expensive prompt re-creation. This pain is especially acute for distributed orgs and platform teams that face rising AI bills—often thousands to tens of thousands of dollars per month—and slower onboarding when intent isn't explicit. You could build a developer-first platform that captures the "why" behind code as structured intent attached to commits, PRs, functions, and test failures, semantically compresses that intent into compact signatures, and replays or caches it to shrink LLM context and redundant prompts by up to ~90% in common workflows. The product would provide IDE and Git integrations, a proxy SDK for LLM calls, enterprise on‑prem options for sensitive code, and analytics that translate compressed context into measurable token and dollar savings. The timing is favorable: LLM adoption in engineering is accelerating, remote and asynchronous work raises the value of explicit rationale, and cost pressure on AI APIs makes a $6.0B addressable market (2,000,000 developer teams × $3,000 ACV) attractive—Market Score 86/100 and Revenue Potential 88/100 reflect that opportunity. Early customers can demonstrate fast ROI by cutting repeated prompt volume and API spend, which helps justify procurement to engineering leaders. To stand out you must marry high-quality human rationale capture with robust semantic compression, tight IDE/CI integrations, and enterprise controls (audit trails, encryption, on‑prem deployment), because simple snippet caching won't win in a medium-competition field. The challenges are real—changing developer workflows, ensuring compressed "why" fidelity for downstream LLMs, and proving savings across diverse stacks—but if you deliver measurable token reduction and a frictionless UX the business can be defensible and lucrative.

Market Opportunity

Capture the "why" behind code to shrink AI context use by 90% targets a $6.0B = 2,000,000 developer teams × $3,000 ACV total addressable market with medium saturation and a year-over-year growth rate of 18% YoY — estimated growth for developer tooling and AI-assisted developer productivity platforms (industry reports, 2023-2025 estimates).

Key trends driving demand: LLM adoption — engineering teams increasingly embed LLMs into workflows, creating demand for tools that optimize token usage and accuracy.; Remote and asynchronous engineering — distributed teams raise the value of explicit, persistent rationale to reduce context-switching and ramp new engineers faster.; Cost pressure on AI APIs — rising token costs make tools that compress and cache intent valuable to engineering leaders looking to control cloud spend..

Key competitors include Sourcegraph, GitHub Copilot / GitHub Copilot for Business, CodeSee.

Sign in to access

Capture the "why" behind code to shrink AI context use by 90%

Executive Summary

Market Validation

Market Opportunity

More in Developer Tools

Manage dozens of websites with centralized automation and governance

Reduce latency & cost with AI-driven backend optimization for mobile games

Missed sales from phone leads fixed by an API phone system that captures and qualifies

AI coding tools lose context, provide persistent cross-tool memory

Open-ended scientific tasks lack rigorous, domain-expert benchmarks

Fix fragile delivery-app checkout flows with AI-driven test & observability