The productivity floor is about to move. Permanently.
grāmatr℠ is the real-time context-engineering layer that sits between you and every AI tool you use. It classifies every request, delivers only the context the request needs, and runs typed quality gates on every output. The floor your team ships from rises — and stays up — because every interaction runs through the same disciplined loop.
One dated inflection. A new sustained floor.
The trailing twelve months of public GitHub contributions from the first user of the loop show a single dated inflection — March 24, 2026 — and a new weekly baseline roughly an order of magnitude higher that has held for eight straight weeks. Every weekly bucket is independently verifiable.
See the full timeline and methodology →
The chart on /proof is built from the public GitHub contribution calendar. Anyone can verify it. The mechanism change behind the inflection is available for review under enterprise due diligence.
Every AI tool ships from a low floor.
Most teams pay the context-rebuilding tax on every interaction — and that tax is the productivity drag, not the model.
Each new session starts cold. The AI has no idea who the team is, what conventions matter, what was decided yesterday, what was already tried and discarded. So practitioners spend the first ten to thirty minutes of every session re-explaining the same context. They re-paste the same conventions every Monday. They re-fix the same mistakes their AI made last Tuesday. Multiply that by every practitioner and every session, every week — that is the floor most teams ship from, and it is much lower than it has to be.
This is not a niche complaint. The data confirms it is an industry-wide productivity problem getting worse, not better.
"Context engineering is the delicate art and science of filling the context window with just the right information for the next step."
"I really like the term 'context engineering' over prompt engineering. It describes the core skill better."
"Building with language models is becoming less about finding the right words for your prompts, and more about answering the broader question of 'what configuration of context is most likely to generate our model's desired behavior?'"
The people building AI agree: the problem is not the models. It is context engineering — getting the right context to the model at the right time, every time. grāmatr runs that in real time, on every request.
Five stages. One Loop. Runs on every request.
Real-time intelligent context engineering is not a feature. It is a disciplined five-stage Loop that runs before, during, and after every interaction with every AI tool you use — and that levels you up every time it cycles.
Classify
Every request is classified in milliseconds before the model runs. The output is not a label — it is a contract describing how much effort the request deserves, what intent is being expressed, which capabilities apply, what hard constraints, and what quality criteria the answer must satisfy. The frontier model receives that contract and starts working immediately, instead of burning thousands of tokens figuring out what was wanted.
Deliver
The pre-classifier decides on every turn whether context is needed at all — and if it is, exactly what. Only that gets delivered, assembled in real time from a typed, multi-tier knowledge graph and routed to the model before the model runs. Without the Loop, every turn pays four taxes: the full system prompt rides along, the model spends reasoning tokens figuring out what context it needs, it spends tool tokens searching for and fetching that context, and it pays again when the first fetch was wrong. With the Loop, the model goes straight to the work. Those four taxes are paid once per turn — and the savings compound across every turn of every session.
Execute
The agent does not free-run. It executes through a typed phase template with a mandatory plan gate, so the work proceeds in disciplined steps rather than a single uncontrolled pass. The model produces output the rest of the Loop can verify and learn from.
Shape
Every output runs against typed quality gate criteria set before the work began. The output either meets the gate or it does not ship. Every PASS or FAIL is recorded with evidence — the audit trail your procurement team and your AI-skeptics both ask for. This is where AI behavior gets shaped to your standard, not the model's default.
Learn
Every gated outcome becomes a signal that feeds the classifier. The next request starts smarter than the last. The cycle doesn't just repeat — it levels up. Learn is what makes the Loop a flywheel instead of a one-shot.
Every cycle of the Loop levels you up.
The five stages run end-to-end on every request. The flywheel isn't separate from the Loop — it <em>is</em> the Loop, closing on itself. Each cycle leaves the classifier sharper, the substrate richer, the floor higher. The next request starts at the new level.
What 'leveling up' actually means
The classifier levels up
Every gated outcome is labeled training signal. The classifier gets more accurate at routing your next request with every interaction.
Your substrate levels up
Patterns, decisions, and conventions you have validated stay in the substrate. They are what the next Classify step has to work with.
Your floor levels up
Speed inside disciplined gates is not a spike. It is a new level. The floor your team ships from rises and stays at the new level.
Velocity alone is a spike. Velocity inside typed gates, with audited outcomes feeding the next decision, is a permanent level-up. That is why the floor moves and stays.
See how the Loop works in detail →Claude → Codex → Gemini → Perplexity → back. Same context. Same Loop.
grāmatr improves your work regardless of which foundation model you pick or which client you happen to be in. Switch between models mid-day. The Loop runs the same way. Your preferences, your conventions, your prior decisions — all of it travels with you to whichever model you choose next.
And it doesn't matter what surface you're using — grāmatr improves every supported client. Pick whatever fits the task.
When a better foundation model launches next month, switch to it. The Loop, the substrate, and the audit trail come with you. No vendor lock-in on the model layer. No context loss between surfaces.
Request Early AccessThe floor rises wherever the loop runs.
For Individuals
Your AI stops resetting every morning. Your preferences persist. Your conventions persist. Your last week's decisions persist. The personal floor rises and stays.
See what changes for you →For Teams
Conventions persist across every team member. Reviews compound. New hires onboard at week one with the same context the team built over months. The team floor lifts and stays — defensible 1.5×–3× sustained throughput.
See what changes for your team →For Enterprise
Audited outputs against typed quality gates. Token-savings as a verifiable per-request metric. Compliance as code. The audit trail that procurement and legal both ask for, in the same loop that lifts the floor.
See the enterprise story →Move your team's floor.
The floor is what your team should expect. The ceiling is what the same loop has already done in public. The next move is yours. The private beta is open.