One intelligence layer. Every model. Full governance.
Your teams use Claude, ChatGPT, and Gemini — often all three. grāmatr℠ gives them one intelligence layer that travels across every platform — governed by your policies, encrypted at every level, and measurably more efficient with every interaction.
The spending is climbing. The ROI isn't.
That's up 44% year over year. But here is the uncomfortable truth: Forrester projects that 25% of that spending will be deferred, because fewer than one-third of enterprises can link their AI investments to tangible financial growth.
That is not a technology problem. It is a context problem.
Every AI session your organization runs starts from zero. No learning from what worked yesterday. No awareness of what another team figured out last week. No compounding intelligence layer — just isolated, expensive conversations that repeat themselves across hundreds of seats.
Enterprise buyers need numbers. Here are ours.
Verifiable, with baselines and methodology for each.
Token economics at scale
In production use, grāmatr has saved over 33.8M tokens through intelligent routing — roughly 5,800 tokens per request across 5,830 routed requests, plus the 40K-to-1,200 system prompt reduction on every session start. These numbers are live and growing.
What does that mean in dollars? In production use, grāmatr's routing has saved an estimated $710 in API costs — and that number grows with every session. Scale that across a team, and the math speaks for itself.
Velocity and discipline at the same time
The conventional tradeoff in software delivery: move fast and accumulate technical debt, or move with rigor and miss the window. grāmatr℠ makes that tradeoff obsolete.
In the breakthrough week of March 24–31, 2026, one operator using grāmatr produced 607 commits, 1,203 files touched, 354,489 lines added — through feature branches, pull requests, code review, squash merges, and automated CI/CD. 15 production releases shipped in the same period — v2.0.39 through v0.2.39 — handled through a single grāmatr skill that automates version bump through Kubernetes rollout.
The point isn't the commit count. It's that the commit count and the engineering discipline grew together, driven by patent-pending pre-classification routing that made both possible at the same time. What a conventional product team would ship in six to ten weeks, one operator + grāmatr ships in one — without sacrificing the practices that make code shippable at scale.
You control what trains.
The question every CISO asks: "Who controls what trains?" grāmatr answers it with a five-level governance hierarchy built into the architecture.
System level
System-level intelligence — global skills, standards, and agent definitions — is maintained by grāmatr and versioned independently. Row-level security enforced at the database level, encryption at rest, and full isolation between tenants by architecture, not policy.
Enterprise level
Enterprise administrators control organizational intelligence — policies, compliance rules, and cross-team standards that apply across the entire organization. Visibility into what data informed which capabilities, when, and by whose authorization.
Team level
Team administrators decide which patterns get shared across the team and which stay private. Coding conventions, project terminology, workflow patterns, and composable agents — all governed at the team boundary.
User level
Each user's interactions train only their own intelligence. Encrypted, isolated at the database level with row-level security. Your developer's coding patterns, preferences, and decision history stay theirs alone.
Project level
Intelligence scoped to specific projects — decisions, milestones, and context that belong to a codebase or initiative, not an individual. When the project moves teams, the intelligence moves with it.
Training requires explicit authorization — at the user, team, and enterprise levels. Training events are logged and auditable by design.
Security is not a feature. It's the foundation.
Single Sign-On (SSO) Roadmap
OIDC/JWT integration planned. Your identity provider, your access rules.
On-premises deployment Roadmap
Run grāmatr entirely within your infrastructure. Your data never leaves your network.
Bring Your Own Keys Roadmap
Use your own encryption keys. Full key management integration planned.
Data residency Roadmap
Regional deployment options to meet jurisdictional requirements. Architecture designed for it.
Row-level security
User isolation at every database layer. Not tenant-level — user-level. Every query is scoped to the authenticated user.
Per-user encryption
All interaction data encrypted per-user with database-level row isolation in both the vector and object databases.
The architecture is designed so that access to infrastructure does not equal access to data. Your data is yours. Your team's data is your team's. Database-level row isolation enforces this by design.
One policy. Every AI tool.
Your organization does not use just one AI tool. Neither should your governance.
grāmatr provides the same context, the same behavioral directives, and the same organizational intelligence — whether your team is working in Claude, ChatGPT, Gemini, or any AI platform that supports the Model Context Protocol. The intelligence layer is fully portable.
If Gemini releases a model tomorrow that's better for data analysis, use it — your governance, your context, and your institutional intelligence travel with it automatically.
When a team member switches from Claude Code to ChatGPT to Gemini in a single day, their AI carries the same intelligence and the same organizational context. No per-tool configuration. No context loss between platforms. No vendor lock-in on the AI model layer.
Institutional capability that compounds.
grāmatr does not just carry existing knowledge forward. It detects patterns across your organization and recommends new capabilities.
When multiple teams independently develop similar workflows, the system identifies the pattern and recommends formalizing it into a shared skill — a repeatable, deployable capability available to the entire organization. One team's hard-won efficiency becomes everyone's baseline.
The longer your organization uses grāmatr, the more it identifies, the more it recommends, and the more your AI investment returns. Not because you bought more seats — because the intelligence layer got smarter from actual usage.
Frequently asked questions.
What compliance certifications does grāmatr have?
grāmatr is architected for compliance from the ground up — user-isolated encryption, row-level security, full audit trails, and tiered training governance. SOC 2 Type II and HIPAA certifications are on the roadmap. Current security architecture was designed to meet these standards; formal certification is underway. We are transparent about where we are in the process and will share certification status as it progresses.
Can we deploy on-premises?
On-premises deployment is on our roadmap. The architecture is designed for it — the full intelligence layer (routing, learning, skill detection) is built to run within your network. If on-prem is a requirement for your organization, talk to us about timeline.
How does grāmatr handle data residency requirements?
Data residency is on the roadmap. The architecture is designed to support regional deployment — whether your data needs to stay within the EU, within a specific country, or within your own infrastructure. Talk to us about your specific requirements and timeline.
What happens to our data if we cancel?
Your data is yours. Upon cancellation, you receive a full export of your organization's data — intelligence configurations, skill definitions, and governance records. After export confirmation, all data is permanently deleted from grāmatr systems within 30 days. We can provide a certificate of deletion upon request.
How does pricing work for enterprise?
Enterprise pricing is based on your organization's size, deployment model (cloud or on-premises), and support requirements. We do not publish enterprise pricing because every organization's needs are different. Talk to our team and we will scope a plan that matches your requirements — typically within one conversation.
Talk to us.
Enterprise AI should get smarter with every interaction — governed, encrypted, and measurable. If your organization is spending on AI tools and struggling to show ROI, we should talk.
Prefer to dig deeper first? See the proof behind the claims, learn how the intelligence layer works, read our story, or review pricing tiers.