grāmatr is a context engineering platform that solves the most expensive problem in AI: every tool forgets everything between sessions.

Developers lose hours each week rebuilding context. Teams watch institutional knowledge evaporate when someone closes a tab. Enterprises spend billions on AI tools that start from zero every single time.

grāmatr fixes that with patent-pending architecture:

Seven-dimension pre-classification. Before every request reaches an AI model, grāmatr classifies it — effort level, intent type, skill match, memory tier, relevant context, constraints, and confidence. Your AI gets a surgical briefing, not an encyclopedia.

Persistent knowledge graph. Your decisions, preferences, patterns, and project context are structured into a searchable knowledge graph that grows with you. Encrypted at rest. Isolated by row-level security at the database level.

Learning flywheel. Every interaction feeds back into the system. Classification accuracy improves. Patterns emerge. The intelligence packet that started at 40,000 tokens shrinks to 1,200 — and performs better at 1,200 than it did at 40,000.

Cross-platform. One intelligence layer across Claude, ChatGPT, Gemini, VS Code, Cursor, Codex, mobile, and web. Your AI brain travels with you.

Team and enterprise governance. Five-level scope hierarchy — system, enterprise, team, user, project. Composable agents. Shared pattern libraries. Admin-controlled sharing. Enterprise users opted out of model training by default.

grāmatr

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