One Source of context
for all AI systems

Will this replace my AI content generation tools? No. Source is designed to work alongside tools like ChatGPT, Claude, and other generative systems. Those tools generate content, while Source provides the structured brand context they need to produce reliable, on-brand outputs.

Traditional RAG retrieves semantically similar documents to provide context to AI models. Source goes further by structuring brand knowledge into task-aware context, rules, and validation criteria optimized for generative workflows. This helps AI systems apply the right brand guidance for each output.

Source integrates with AI tools through APIs, SDKs, and MCP connections. It delivers structured brand context to agents and applications so they can generate and validate outputs using accurate brand knowledge. This allows teams to keep their existing AI tools while ensuring outputs remain consistent and on brand.

A Context Engineer designs the systems that determine how AI generates and evaluates content. Instead of writing individual prompts or pieces of copy, context engineers structure brand and company guidelines, messaging frameworks, evaluation criteria, prompt systems, and context retrieval logic so AI agents can produce reliable outputs across workflows that is both accurate and on-brand.

Context engineering is the creation of systems that structure and deliver the right context to AI agents across organizational workflows. It converts brand knowledge, messaging rules, and product information into structured context AI can use when generating outputs. This ensures consistent, high-quality results across agents, teams, and use cases.