Your team's floor moves. And stays.
grāmatr℠ is the real-time intelligent context engineering layer that sits between your team and every AI tool they use. Every request gets pre-classified. Every output runs against typed quality gates. The conventions your team has hard-earned persist across every member, every session, and every supported AI surface. Sustained throughput lifts and holds.
Everything an individual gets, plus a shared substrate the team builds together — governed by your admins, with the floor multiplier you can budget against.
The problem every team lead knows.
You have seen it happen. Someone leaves, and suddenly nobody knows why the deployment pipeline works the way it does.
The conventions they established, the patterns they discovered, the hard-won lessons from six months of building — gone. And you feel it immediately.
New hires spend their first weeks re-learning what everyone else already knows. They ask the same questions. They make the same mistakes their predecessors already solved. Their AI tools start from zero on every session — no understanding of the team's codebase, the team's naming conventions, the team's deployment workflow.
And every team member's AI behaves differently. One person's Claude follows your style guide. Another's ignores it. A third uses its own interpretation. No consistency. No compounding. Every Monday, the team pays the context-rebuilding tax again.
That is one practitioner's daily reality. Multiply it by the whole team.
What changes when the Loop runs across the team.
The honest pitch is not 10×. The honest pitch is that the floor rises and stays up. A disciplined team running the full Loop sees a 1.5× to 3× sustained throughput multiplier. Reviews compound. Conventions persist. New hires onboard at week one with the same context the team built over months.
At blended professional-services rates, a 2× sustained throughput floor across an eight-person AI-augmented team is on the order of low single-digit millions per year in recovered capacity. Not a one-time productivity win — an ongoing operating-cost shift.
The multiplier is derived from operator-vs-team comparison data on /proof.
Conventions persist. Reviews compound.
grāmatr sits between your team and every AI tool they use. Team conventions, architectural decisions, code-review standards, and workflow preferences become typed behavioral directives the platform delivers on every relevant request. Not static documentation that nobody reads — active context that shapes how every team member's AI responds.
When a new engineer joins your team, their AI does not start from zero. It already understands your branching strategy, your review standards, your testing conventions, and the architectural patterns the team has been refining for months. Context that took the team weeks to develop is available the moment they connect their tools.
Active, not static
Conventions shape outputs in real time at the point of need — not documentation someone bookmarks and forgets.
Day-one onboarding
New team members get the team's accumulated context the moment they connect their AI tools.
Compounds with the Loop
Every gated output makes the next interaction sharper. The team's floor gets measurably more solid each week.
This is the difference between shared documents and shared intelligence engineering. Documents inform. The Loop performs — and the context-rebuilding tax stops getting paid on every turn.
One person figures it out. The whole team can use it.
The flywheel makes individual wins into team capabilities.
Here is how it works in practice. A team member has a productive session — they figure out a complex workflow, say writing and publishing high-quality web content, or running a regulated deployment with the right approvals. The Loop captures the pattern. Team admins decide whether it becomes a shared capability. If it does, every team member can invoke it on demand, with the same quality gates and the same governance.
Capture a productive content session — research, voice, citations, quality gates — once. Make it a team capability that produces the same standard every time it runs.
What used to require a deployment runbook and tribal knowledge becomes a repeatable, auditable team capability — every step gated, every output recorded.
Every productive session is a potential new shared capability. The team gets sharper every week, with the audit trail to show how.
Consistent AI, consistent work.
When every team member's AI follows the same behavioral directives, the same conventions, and the same quality gates, the output is consistent — regardless of who did the work or which AI tool they used.
- "My Claude does it differently than yours."
- Debugging inconsistencies from per-person system prompts
- Different context, different instructions, person-by-person
- No shared understanding across AI surfaces
- One shared substrate across every AI tool the team uses
- Naming patterns applied automatically
- Testing standards enforced consistently
- Same conventions across every supported surface
The conventions your team establishes — naming patterns, testing standards, documentation requirements, code-review expectations — are not suggestions a per-person AI might follow. They are directives that travel with every team member's AI, across every supported surface.
Build specialized agents. Govern them across the team.
Your team's expertise, encoded into reusable agents that anyone can invoke under your governance.
grāmatr ships with general-purpose agents — engineer, architect, researcher, reviewer, and more. The power comes when your team builds composable agents from its own substrate: an agent that knows your API conventions, one that understands your deployment workflow, one trained on your compliance requirements.
These are not prompt templates. Each composable agent pulls live context through the Loop, adapts to the current task, and improves as the team's substrate grows. One person composes it. The whole team uses it under your admin controls.
Context-aware
Each agent pulls from the team's live substrate — not static prompts. The conventions, the decisions, the patterns travel with every invocation.
Curated capability library
The team tier includes the curated capability library — research, analysis, extraction, summarization, review, and more. Quality, not quantity.
Shared and governed
Team admins control which agents are available and which capabilities are deployed. One person creates. Everyone deploys. Full governance.
You control what is shared.
The first question every team lead asks: "What if personal data leaks into the team?" Fair question. Here is the answer: nothing gets shared unless you explicitly allow it.
grāmatr uses tiered governance that maintains clear boundaries between personal, team, and organizational intelligence:
Everything a team member does with their personal AI stays isolated. Their interactions, preferences, and personal patterns stay theirs. Cross-tier access requires explicit authorization.
Team admins decide which patterns, conventions, and capabilities get promoted to the shared team layer. You see what is being proposed for sharing. You approve or deny. Nothing flows to the team automatically.
For organizations with multiple teams, enterprise admins control what gets promoted from team intelligence to organizational intelligence. Full audit trail. Full governance. Every change is logged and reversible.
Tiered governance
The boundaries between tiers are enforced at the storage layer, not the application layer. Cross-tier access requires both authorization and the right scope; controls live below the application, not above it.
Promotion of a pattern, convention, or capability across tiers happens only with explicit authorization. Every promotion event is logged, auditable, and reversible.
You stay in control. Your team members keep their privacy. The shared substrate only contains what you have explicitly approved.
Team-lead questions, direct answers.
What does rollout actually look like — do all our developers need to install something?
For developer tools (Claude Code, Cursor, VS Code), each team member adds the grāmatr MCP server to their existing tool config — minutes per developer. For browser-based tools, no install at all; the team member signs in via the web interface. The team admin sets up the shared knowledge graph and governance rules once; individual onboarding is independent.
What happens to our existing AI tool subscriptions?
They stay. grāmatr is the intelligence layer that sits on top of whatever AI tools your team already pays for — Claude, Cursor, ChatGPT, Gemini, Copilot. The intelligence travels across tools; your existing subscriptions handle the model inference. If you ever switch vendors, the intelligence comes with you.
Who owns the team intelligence if someone leaves?
The team. Each member's interactions feed both their personal intelligence (private to them by default) and — with their authorization — patterns that get promoted to team scope. Team-scoped intelligence stays with the team regardless of individual departures. Admin controls govern what gets promoted; nothing crosses tier boundaries automatically.
Can we control what gets shared across the team?
Yes — that is the core of the admin model. Individual preferences and corrections stay private by default. Promotion to team scope requires explicit admin authorization. Every governance event is logged. Coding conventions, project terminology, composable agents — all governed at the team boundary with full audit history.
How long until we see team-level results?
Individual lift starts day one (your developers stop losing 10–30 minutes per session rebuilding context). Team-level compounding starts as soon as the first patterns get promoted — typically within the first two weeks. The full multiplier (a budgeted 1.5–3× sustained throughput) compounds over the first quarter as conventions persist, reviews accumulate, and onboarding new hires becomes a one-day exercise instead of a one-month one.
How does this scale up to enterprise?
The team tier is the same intelligence engine the enterprise tier runs on. Upgrading adds organization-wide governance (multiple teams, cross-team policy enforcement, hierarchical promotion, formal audit trails) and roadmap items like SSO and on-prem deployment. Your team data, conventions, and skills carry forward unchanged. See /for-enterprise for the full procurement story.
Move your team's floor.
The floor your team ships from today is lower than it needs to be. Move it. Request Early Access
See the public timeline, how the Loop works, what changes for individuals, or the enterprise governance story.