Platform

You deployed the model.
You haven't deployed the layer.

grāmatr is the reasoning infrastructure that runs between your organization and every AI response — classifying intent, assembling context, enforcing policy, attributing cost, and accumulating organizational intelligence. In the request path. Before the model answers.

See how it works

The request path — five functions, in sequence

01 / Classification

Intent classified before anything loads.

Every request is classified before a model sees it. Patent-pending architecture. The model receives a request that has already been understood by the organization's own intelligence layer.

02 / Context delivery

The right organizational context, assembled for this request.

Not retrieved from a document store. Assembled — from what your organization knows, has decided, and has accumulated — for this session, this actor, this moment.

03 / Governance

Policy enforced in the request path, not after the response.

grāmatr evaluates policy before the model answers — enforced in the request path, not a filter bolted on after. Every call is classified and attributed there, and the output is logged and accounted for.

04 / Cost attribution

Every session attributed to its actor at the moment it happens.

Not aggregated from log exports. Not estimated at month-end. Attributed at inference time — so cost is always tied to an actor, a session, and an outcome.

05 / Compounding intelligence

Every routed request trains the classifier.

The system gets more precise with use. Organizational patterns accumulate under the same audit trail. The infrastructure earns its place — it does not just pass requests through.

930,000+

Memories assembled and delivered in production

Real applications run on grāmatr.

Anneal runs on grāmatr first, with the Covington Place Partners Financial Governance for AI application second, in active development. Every decision, session, and outcome those applications produce runs through the same architecture being sold.

  • That is not a claim about what grāmatr can do — it is a demonstration that the architecture performs under the conditions it is sold for.
  • The audit trail exists before any external customer arrived.

What does this look like across an enterprise estate?

Production record — applications running on grāmatr

Classifications 38,000+
Learnings 56,000+
Memories assembled 930,000+
Knowledge graph entities 128,000+
Knowledge graph relationships 20,000+
Deployment status Production
Classification architecture Patent-pending
Session isolation Row-level security
Latency — pre-classification Sub-100ms
Fail mode Open — no single point of failure
SOC 2 Type I Targeting October 15, 2026
Sub-100ms Pre-classification latency. No perceptible overhead added to your AI estate.
99.9% SLA Production-grade uptime commitment. Fail-open design — requests flow through if grāmatr is unavailable.
Every deployment tier
  • Multi-tenant grāmatr Cloud
  • Private cloud
  • On-premises
  • Air-gapped on-premises
One architecture. Security postures are not uniform — the same governance spans all four.
Model-agnostic Claude, GPT, Gemini, and Llama. The intelligence layer does not require commitment to a single model vendor.

Two tracks. One infrastructure.

Enterprise

Your AI estate is deployed.
The layer that runs before it is not.

  • Context assembled for no request — models answering without what your organization knows
  • Token costs attributed to no actor, no team, no outcome
  • Policy existing in documents, not in the request path
  • No audit trail of what AI did, who authorized it, or what it returned

grāmatr closes all four gaps without replacing what you have deployed. Context delivery improves model output before you touch a model. Attribution gives the CFO a number at any point — not at month-end. Policy enforces in the request path, not after.

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Partners

One deployment decision.
Two immediate wins.

Internal Client delivery
Deploy Anneal — grāmatr's first application, a full AI operating system — to your consultants on day one. The complete intelligence layer across the entire practice, immediately, without building anything. Deliver the same infrastructure to every client engagement. Client delivery acceleration from a single architecture decision.

One partner relationship scales across an entire consultant base and every engagement that follows.

Request a partner conversation →