Your floor moves. And stays.
grāmatr℠ is the real-time intelligent context engineering layer that sits between you and every AI tool you use. Your AI stops resetting every morning. Your preferences persist. Your corrections compound. The floor you ship from rises — and stays up — across every supported surface, every session, every week.
Every AI you use starts from zero.
You know this routine. You open a new session, and the AI that helped you build an entire feature yesterday has no idea who you are. Your codebase, your conventions, your preferences — gone. Every single time.
Bug report — anthropics/claude-code #2545, June 2025"Explicitly instructed Claude Code to use my name and email for Git commits. 30 minutes later, Claude Code attempted to push with username 'claude' and email '[email protected]'. When questioned, acted as if no previous Git configuration instructions existed."
— @SDS-Mike
That is not a minor inconvenience. That is explicit instructions being discarded within minutes of a single session. And it compounds across sessions.
Industry survey — Stack Overflow 2025 Developer SurveyDevelopers report that AI giving 'almost right, but not quite' solutions is their biggest frustration. Positive sentiment toward AI tools fell from 70% in 2024 to 60% in 2025 — a single-year drop the survey has not recorded before.
— Stack Overflow 2025 Developer Survey (~49,000 respondents)
The reality is that this frustration has a measurable cost. Not just annoyance — actual lost productivity, every day.
Research — IEEE Spectrum, January 2026AI coding tools have been measured to slow developers down on some workflows. Tasks that once saved hours now cost extra time when context has to be rebuilt every session.
— IEEE Spectrum, January 2026
Several hours a week, every week, spent re-paying the context tax. That is not a workflow problem. That is a missing context engineering layer.
What changes on Monday.
You start your morning by re-explaining your project structure. You paste in your coding conventions — again. By the third interaction, the AI has already forgotten the architectural decisions you made an hour ago. By the fifth, it is ignoring your CLAUDE.md rules entirely.
Corrections do not stick. You fix the same mistake on Tuesday that you fixed on Monday. The AI is not learning from your interactions — it is processing them and moving on. Every session pays the context tax again.
Your AI carries the relevant context forward. It knows your project structure, your naming conventions, and the architectural decision you made last Thursday about separating test agents from coding agents. When you corrected its approach to error handling on Monday, that correction became a directive that travels with every following turn.
Every request gets pre-classified in milliseconds. Only the context this exact turn needs is delivered, just-in-time, before the model runs. Your preferences persist across sessions. Your corrections compound into lasting improvement. The Loop runs the same way on every interaction, across every supported surface.
How to get in.
grāmatr is invitation-based today. Access is by application or referral.
Apply
Request early access. Every application is reviewed individually. We are looking for practitioners who use AI seriously and want it to get measurably better over time.
Or get a referral
Know someone already with access? A referral from an existing user gets you in faster. Every member has a limited number of invites.
Set up
Once approved, you get setup instructions for the AI tools you actually use — a single idempotent command across the major LLM client surfaces. The Loop starts running on your first request.
Works with everything you use.
grāmatr is not tied to any one model or any one client. Your real-time intelligent context engineering travels with you across every AI tool in your workflow.
Start a turn in one surface, pick it up in another, finish it in a third — the Loop runs the same way on every supported surface. If a better model launches tomorrow, your conventions, preferences, and prior decisions travel with you to it.
Your data is yours.
Your interactions become your intelligence. Your preferences, your patterns, your corrections — scoped to you, isolated at the storage layer, encrypted at rest.
Cross-user access requires both authorization and the right scope. The isolation lives below the application layer, not above it — controls enforced architecturally, not by convention.
Your intelligence belongs to you. Private by default. Exportable. Deletable.
What your first Monday looks like.
Single command. The Loop installs across the AI tools you already use — your code-editor client, your chat AI, your terminal assistant. Setup takes minutes, not afternoons.
You open your first turn of the day. The Loop runs in milliseconds before the model does. The model receives the context this turn actually needs — your preferences from last week, the conventions you have validated, the decisions you made about this project — and starts the work. You don't paste anything. You don't re-explain.
By Friday, the system has captured a handful of patterns from how you actually work. By the second Monday, the model knows things about your style it didn't know on the first one. The floor that was your old normal isn't your floor anymore.
Request early access for tier details and setup instructions for the AI tools you use.
The floor moves. Permanently.
Twelve months of public GitHub data show what happens when the Loop runs end-to-end on every request. The prior-floor average was ~140 contributions per week; the trailing-eight-week average since the Loop went live is ~1,170 — held steady. Same operator, same hours, only the mechanism changed.
Individual questions, direct answers.
Will this work with the AI tools I already use?
Yes. grāmatr is model-agnostic and connects through the Model Context Protocol (MCP). It works with Claude Code, Cursor, ChatGPT, Gemini, VS Code Copilot, and browser-based AI tools through the web interface. You keep using whatever tool you prefer; grāmatr is the intelligence layer behind all of them.
How quickly will I notice a difference?
From day one your AI stops resetting between sessions — preferences and project context carry over. Within the first week, routing accuracy improves as the classifier learns what kinds of requests you make. By month three, the intelligence packet typically compresses from ~40,000 tokens to ~1,200 — your AI gets sharper, not by remembering more, but by knowing what matters for each request.
How is this different from ChatGPT's or Claude's built-in memory?
Built-in memory stores recent conversations and replays them. That is retrieval. grāmatr classifies every request before the model runs, decides what context (if any) is needed, and delivers a surgical packet — then learns from the result. It works across every AI tool you use, not just one. Your intelligence travels with you to whichever model is best for the task.
Do I need to be a developer to use this?
No. For browser-based AI tools (ChatGPT, Gemini), grāmatr connects through its web interface — no command line. For developer tools (Claude Code, Cursor), it is an MCP server config that takes minutes to set up. Either way, once connected, you interact with your AI tools exactly as you do today.
What happens to my data, and is anything shared with other users?
Your interactions stay yours. All data is encrypted at rest with row-level security enforced at the database layer — the integrity check sits below application code, so a bug cannot leak your data to anyone else. Nothing about your work is visible to other users without explicit authorization. If you cancel your account, your raw data is deleted; data export can be arranged on request.
Is there a free tier or trial?
grāmatr is in private beta with founding-member pricing on approval. Every active user costs real compute, so there is not a free tier — but founding members lock in pricing that reflects the value of shaping the product early. Apply via the early-access form and most applications are reviewed within 48 hours.
Move your floor.
Your AI should stop resetting every morning. Request Early Access
See the public timeline, how the Loop works, explore what changes at team scale, or view pricing.