Build intelligent software faster with AI-native development tools that understand your vision and write production-ready code.
How Luna WorksFrom solo creators to enterprise teams, Luna adapts to how you work and scales with your ambitions.
Use Cases OverviewLuna's Context Engineering engine orchestrates perfect knowledge transfer across agents, sessions, and teams—eliminating hallucinations and handoff gaps forever.
A continuous loop that transforms raw interaction into executable specifications. Luna's architecture ensures knowledge is never lost, only refined.
Requirements, decisions, constraints, and domain logic enter into system.
Knowledge is organized into specifications and optimized context windows.
Context is delivered to all copilots, teams, and future AI models.
Solve the root causes of software failure—miscommunication, memory loss, and vendor lock-in.
By grounding every AI response in verified Context Engineering protocols, Luna reduces model hallucinations by over 90%.
Watch the Architect Agent pass a perfect spec to the QA Agent. No meetings, no misunderstandings—just pure data flow.
Your context is your IP. Don't lock it into a specific model provider. Luna's Context Engineering layer sits above the LLM, making your knowledge portable.
Each Luna Base copilot leverages Context Engineering to deliver precise, coordinated software delivery across the entire SDLC.
Captures and structures business intent into formal technical specifications using semantic parsing and domain logic extraction.
Inherits requirements context to design system blueprints with zero ambiguity, ensuring architectural patterns align with business goals.
Generates production-ready code aligned with specs and architectural patterns, with full traceability back to original requirements.
Creates comprehensive test suites directly from the same specification context, ensuring validation matches intended behavior.
Provisions infrastructure and CI/CD pipelines matching architectural requirements, with automated deployment workflows.
Coordinates and manages the entire workflow across all copilots, ensuring seamless collaboration and optimal resource allocation throughout the development lifecycle.
Deep dive into how Context Engineering powers Luna platform.
While RAG focuses on finding information, Context Engineering focuses on structuring it. Luna doesn't just retrieve documents; it engineers data into a format that AI agents can specifically use to write code, run tests, and validate logic without confusion.
Luna injects structured requirements context directly into the Test-Driven Development agent, ensuring tests are written against approved specs, not assumptions.
Yes. A core principle of Context Engineering is data portability. You can export your project's entire knowledge graph—requirements, specs, and documentation—into standard formats like Markdown or JSON, ensuring you never lose your project's "brain."
Prompt Engineering focuses on crafting right input to get better outputs from a single LLM interaction. Context Engineering, on the other hand, is a systematic approach that structures, preserves, and orchestrates knowledge across multiple agents, sessions, and even different AI models. While Prompt Engineering is tactical (optimizing one question), Context Engineering is strategic (building a persistent, queryable knowledge system that ensures consistency and accuracy across your entire development lifecycle).
Join the developers using Luna's Context Engineering to build software 10x faster with zero knowledge loss.