AI Intelligence Layer // Enterprise
Every AI call your organization makes runs without an intelligence layer — no classification, no context, no audit record, no cost attribution.
grāmatr sits in the request path and runs five things before the model fires: classifies intent, delivers the right context, enforces policy, attributes every token, and compounds what it learns. 150K context-loaded tokens on Sonnet outperforms 1M+ ungoverned tokens on Opus — because grāmatr loads what the model needs before it fires. Illustrative, pending your own baseline.
THE PROOF ARTIFACT
What grāmatr produces for every governed call.
The governance receipt is the record grāmatr writes before the model responds — intent classified, context loaded, cost attributed, audit trail written. Below is an example from the CPP Financial Governance application, in production.
Production figures as of July 3, 2026.
THE MECHANISM
What happens before the model sees your prompt.
Every AI-enabled request enters the path. grāmatr receives it before the model does — classification begins immediately.
grāmatr classifies the intent and loads the relevant organizational memory — project state, operator history, conventions. The model receives context it could not have asked for.
context[]
Policy is checked. Cost center assigned. Non-compliant requests stop here — the model never sees them. The audit record is opened.
cost_attribution
The enriched request reaches the model. The audit record is written before the response returns — session closed, compounding begun.
session_closed
FRONTIER MODEL AGNOSTIC
grāmatr doesn't replace your frontier models. It governs them.
The intelligence layer sits between your AI tooling and the frontier API. Classification, context delivery, attribution, governance enforcement, and audit trail run consistently — regardless of which provider handles the request. Your governance posture doesn't change when the model does.
Governance posture does not change when the model changes.
THE FIVE FUNCTIONS IN THREE LAYERS
Five functions. One layer. Runs on every call.
grāmatr classifies every request and loads the right organizational memory before the prompt reaches the model. Sessions are stateful by default — the model receives what it needs, not what the operator remembered to paste.
Governance rules are evaluated in the request path — non-compliant requests stop before the model fires, not after. Every call is attributed to an operator, team, and cost center at the session boundary, not reconstructed at month-end.
Every governed session writes a complete audit record to the knowledge graph. Classification sharpens. Context becomes more precise. The layer compounds what it learns — your organization's AI intelligence improves every quarter it runs.
THE COMPOUNDING LOOP
Every governed call makes the next one smarter.
Most AI tooling resets every session. grāmatr doesn't. The audit record from each governed interaction feeds back into the classification and context engine — your organization's AI intelligence compounds across every call, every operator, every quarter it runs.
IN PRODUCTION
Infrastructure in deployment, not in a deck.
grāmatr's own SaaS product — Anneal — operates on the same interception layer, classification, context delivery, attribution, and audit offered to enterprise clients. SaaS sessions and enterprise routing run through the same layer, in the same deployment. Not a simulation. Not a staging environment.
Cost routing observed: 1M ungoverned tokens on Opus cannot match 150K context-loaded tokens on Sonnet with grāmatr — not because the task was simpler, but because grāmatr loaded context the human operator would not have thought to provide. Classification determined what was relevant. The model received more information than any unaided prompt would carry. Sonnet outperformed Opus because grāmatr compensates for what human operators inherently forget. Illustrative, pending your own baseline.
Type II target: April 15, 2027. Audit scope covers the interception layer, prompt handling, session storage, and the attribution chain — not just the application tier.
Start fast on multi-tenant grāmatr Cloud — available now, tenants isolated at the database row level, never commingled. Then private cloud and on-premises (committed September 1, 2026), or fully air-gapped on-premises (committed January 1, 2027) when the perimeter demands it. Same classification, context delivery, governance, attribution, and audit trail across all four. No shared tenancy in private or on-premises tiers — your session data and organizational context never leave your perimeter.
Anthropic, OpenAI, Google, and Meta model APIs. grāmatr's interception layer is model-agnostic — classification, context delivery, attribution, governance enforcement, and audit trail are consistent regardless of which provider handles the request.
THE MODEL
grāmatr is the platform. Anneal and other applications run on it.
grāmatr is the reasoning and intelligence layer. Applications are built on top of it and inherit its classification, context delivery, governance, cost attribution, and compounding intelligence — they do not re-implement them.
Anneal ships as a complete product — you license it, we deploy it, your team is using it within weeks. grāmatr is the platform Anneal is built on, and you can build your own applications on it too, but that is a separate decision. Licensing Anneal does not require a grāmatr build engagement. Building on grāmatr does not require Anneal. Most teams end up wanting both, but neither is the price of admission for the other.
grāmatr's own SaaS application, live on the platform today — running on the same intelligence layer offered to enterprise.
A financial-governance application built on grāmatr — policy and attribution enforced in the request path.
A targeted conversational application on the NEXT90 Insights & Data Engine — built on grāmatr, inheriting classification, context, governance, and attribution in the request path.
Build your application on grāmatr and inherit the full intelligence layer — classification, context delivery, governance, cost attribution, and compounding — from day one.