The audit trail your AI investment is missing.
grāmatr℠ is the real-time intelligent context engineering layer that sits between your teams and every AI tool they use. Every request is pre-classified. Every output runs against typed quality gates. Every PASS/FAIL is recorded with evidence. The productivity floor moves and stays — and procurement, legal, and audit get the verifiable record they have been asking for.
The spending is climbing. The ROI is not.
AI spending is up 44% year over year. Forrester projects 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-engineering problem.
Every AI request your teams run starts cold. No record of what worked yesterday. No knowledge of what another team figured out last week. No verifiable trail that any output ever met the quality bar. Just isolated, expensive interactions — repeated across hundreds of seats, with nothing compounding and nothing auditable.
Every output gated. Every gate recorded. Every token saving measurable.
The thing that flips an AI vendor evaluation from skeptical to signed is not a feature list. It is whether you can answer, on a slide, three questions your CIO and your General Counsel will both ask: how do we know the AI did acceptable work, how do we know it is getting cheaper instead of more expensive, and how do we know our data did not leak. grāmatr answers all three from the same architecture.
Typed quality gates on every output
Before any request runs, grāmatr sets the quality criteria the answer must satisfy — not as suggestions, as typed gate definitions. Inside the Loop's Execute stage, the agent works through a phase template (OBSERVE → THINK → BUILD → LEARN with a mandatory PLAN GATE) — the internal phases of every agent execution, distinct from the Loop's outer five stages of Classify → Deliver → Execute → Shape → Learn. Each phase boundary either meets the gate or it does not ship. Every PASS or FAIL is recorded with evidence. That recorded ledger is the answer to 'how do we know the AI did acceptable work?' — in a format your audit team can actually consume.
Token economics as a verifiable per-request metric
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 fetching that context, and it pays again whenever the first fetch was wrong. With the Loop, the pre-classifier decides per turn whether context is needed at all, and delivers exactly what is needed. The savings are computed on every request, the same way every time, and exposed as an audited number — not a marketing claim.
Compliance treated as code, not as a binder
Every governance policy, every control mapping, every evidence link in your grāmatr deployment lives in a versioned, reviewed, audited artifact. Compliance status is a git history, not a PDF. Your SOC 2 evidence pack does not get assembled the night before the audit — it exists in the repository, updated continuously.
Defensible math your CFO will actually run.
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.
For an enterprise running ten teams of eight practitioners at a blended professional-services rate of $250/hour, a 2× sustained throughput floor across 80 AI-augmented seats is on the order of $30–60 million per year in recovered billable capacity. Not a one-time productivity win. An ongoing operating-cost shift, computed against your team's actual baseline.
The multiplier is derived from operator-vs-team comparison data on /proof (methodology section). For the operator ceiling — what the same Loop has produced in public at the top of its range — see the chart on /proof.
You control what propagates. The architecture makes it enforceable.
The question every CISO asks: "Who controls what behavior, at what level, across our organization?" grāmatr answers it with a five-level governance hierarchy enforced at the database level — not at the application layer.
System level
Global skills, standards, and agent definitions are maintained by grāmatr and versioned independently. Row-level security at the database level, encryption at rest, full isolation between tenants by architecture — not by application-layer filtering.
Enterprise level
Enterprise administrators control organizational directives — policies, compliance rules, and cross-team standards. Visibility into what was promoted, when, and by whose authorization. Every governance event is logged.
Team level
Team administrators decide what propagates across the team and what stays scoped. Coding conventions, project terminology, workflow patterns, composable agents — governed at the team boundary, with audit history.
User level
Each user's preferences, decisions, and corrections stay scoped to them by default — isolated by row-level security at the database layer, with all interaction data encrypted at rest. Promotion to team or enterprise scope requires explicit authorization.
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 under the same governance.
Cross-tier promotion of any directive, convention, or capability requires explicit authorization at the user, team, and enterprise levels. Nothing propagates automatically. Every event is logged, auditable, and reversible.
Security is not a feature. It is the architecture.
Row-level security
Every query is scoped to its authenticated user via per-transaction session variables enforced by Postgres row-level security at the database layer. Isolation is at the row granularity — a misconfigured service cannot leak another user's data because the database itself refuses to return it. The integrity check sits below your application code, not on top of it.
Encrypted at rest with row-level isolation
All interaction data is encrypted at rest at the storage layer, with row-level isolation across both the vector and object databases. Cross-user access requires explicit database authorization — enforced at the storage layer. The architecture eliminates an entire class of leak conditions: a misconfigured application can't expose what the database won't return.
Single Sign-On (SSO) Roadmap
OIDC / JWT integration planned. Your identity provider, your access rules. Targeted for the enterprise tier launch.
On-premises deployment Roadmap
Run grāmatr entirely within your infrastructure. Your data never leaves your network. Architecture is designed to support it.
Bring Your Own Keys Roadmap
Use your own KMS-managed encryption keys. Full key-management integration planned.
Data residency Roadmap
Regional deployment options for jurisdictional requirements. Architecture is designed to support EU / UK / country-scoped deployments.
Roadmap items are flagged honestly. Architecture was designed from day one to support them; formal availability ships with the enterprise tier. We will not claim what we have not delivered.
Where we stand today. Where we are headed.
Every control documented. Every evidence link version-controlled. Every policy change reviewed. The full compliance program is a working repository — available for enterprise due diligence under NDA, not a marketing PDF assembled the night before. We are transparent about where we are in the certification process, and we will not claim certifications we do not hold.
SOC 2 Type II
Program activeCompliance program runs out of the gramatr/soc2-coordination repository — every control mapping, every evidence link, every policy version-controlled and tracked through pull requests. 82 PRs landed in the first two weeks of program activity. The architecture was designed to meet SOC 2 controls; the formal audit is on schedule.
HIPAA
Architecture alignedArchitecture is designed to support BAA-covered workloads. Formal attestation on the roadmap. For healthcare prospects, current status and target dates available under NDA.
GDPR / CCPA
Aligned todaySee the Privacy Policy for our lawful bases for processing, data-subject rights, retention, and the standard contractual clauses we rely on for international transfers.
Data residency
RoadmapRegional deployment for jurisdictional requirements. Roadmap commitment; architecture is in place. Talk to us about your specific requirements.
One governance policy. Every AI tool your teams use.
Your organization does not use just one AI tool. Neither should your governance.
grāmatr delivers the same context, the same behavioral directives, and the same organizational intelligence — whether your team is working in Claude, ChatGPT, Gemini, Cursor, Codex, VS Code, or any platform that supports the Model Context Protocol. The Loop is fully portable across the model layer.
When a Frontier vendor releases a model tomorrow that is better for a particular workload, your team uses it — and the governance, the audit trail, and the compounding institutional context travel with them. No vendor lock-in on the model layer. No per-tool configuration. No context loss between platforms.
Institutional capability that gets sharper with use.
grāmatr does not just carry existing knowledge forward. The flywheel detects patterns across your organization and surfaces them for review.
When multiple teams independently develop similar workflows, the system identifies the pattern and recommends formalizing it into a shared, governed capability — available across the org under your existing approval process. One team's hard-won efficiency becomes the organization's baseline, with the audit trail showing exactly which inputs informed the new capability.
The longer your organization runs the Loop, the more it identifies, the more it recommends, and the more verifiable signal feeds back into the classifier. AI investment that gets measurably sharper with use — not because you bought more seats, because the mechanism compounds.
Procurement-grade questions, direct answers.
What compliance certifications does grāmatr have today?
SOC 2 Type II program is active, run as code out of a versioned compliance repository. HIPAA architecture is aligned; formal attestation on the roadmap. GDPR and CCPA: aligned today, see the Privacy Policy for lawful bases and rights. We will not claim certifications we do not hold; current status and target dates are available under NDA.
Can we deploy on-premises?
On-premises deployment is on the enterprise-tier roadmap. The architecture is designed for it — the full Loop runs within your network. If on-prem is a requirement, 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 — EU, UK, country-scoped, or your own infrastructure. Talk to us about your specific jurisdiction 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, governance records, and audit logs. After export confirmation, all raw data is permanently deleted from grāmatr systems within 30 days. A certificate of deletion is available on request. Aggregated model improvements derived during your active subscription are retained as grāmatr IP and are not reversed; deletion is prospective.
How does pricing work for enterprise?
Enterprise pricing is based on your organization's size, deployment model (cloud or on-premises), and support requirements. Every enterprise's needs are different. Talk to our team and we will scope a plan that matches your requirements — typically within one conversation.
Can we audit individual outputs?
Yes. Every output passes through typed quality gates set before the request runs; each gate produces a PASS or FAIL with evidence. The gate log is queryable per-user, per-team, per-project, and per-time-range. The typical use case is for compliance review or post-incident investigation; the system supports it natively.
Move your enterprise floor — with the audit trail to prove it.
If your organization is spending on AI tools and struggling to show ROI — or your procurement and audit functions cannot yet sign off on how your teams are using them — we should talk.
Want to dig deeper first? See the public timeline and methodology, how the Loop works, the release notes, or pricing tiers.