Your clients are already using AI. Make it intelligent.
Every consulting engagement, every client deployment, every SaaS integration — AI tools start from zero every session. No learning. No context. No compounding intelligence. grāmatr adds the layer that makes AI actually get smarter, and gives you a new revenue line for delivering it.
The math that gets partner attention.
Consider a professional services firm with 30,000 practitioners billing at a blended rate of $350 per hour across 2,000 hours per year. That is $21 billion in annual billable capacity. A 10% productivity gain — conservative, given what we have measured — recovers $1.8 to $2.25 billion in billable capacity annually. Not theoretical capacity. Recovered hours your practitioners can bill.
The opportunity is not selling AI tools. Your clients already have those. The opportunity is making those tools intelligent — giving them context that persists, learning that compounds, and routing that cuts costs by 97% on every session start.
grāmatr is the intelligence layer above the protocol. MCP is the pipe. Our patent-pending pre-classification routing architecture is what makes the pipe smart — deciding what context to send, when to send it, and how much each interaction needs before the AI model ever sees the request.
Three ways to partner. One intelligence layer.
Whether you are deploying for your own teams, building for clients, or integrating into your software — the economics work.
Deploy for Your Practitioners
Internal deployment — consulting firms, Big 4, professional services
Your practitioners spend 20–30% of every AI interaction rebuilding context. grāmatr's pre-classification routing eliminates that overhead — every request is triaged in milliseconds, and only the relevant context is delivered. In production, that reduced a 40,000-token context window to 1,200 tokens with better results.
What could your people do with that time back? In one verified case, the same pre-classification architecture compressed months of engineering output into a single week — 559 commits across five simultaneous projects. That is time compression, not heroics. It is what happens when AI stops rebuilding context and starts compounding intelligence.
Embed in Client Deliverables
Client-facing delivery — systems integrators, agencies, managed service providers
You build AI solutions for your clients. grāmatr adds the intelligence layer they cannot get from a chatbot wrapper — pre-classification routing that triages every request before the LLM runs, cross-session learning that compounds over weeks, and auditable context delivery for regulated industries. Revenue share on every deployment.
Here is what changes for your client: instead of AI that resets every session, their teams get AI that learns which approaches worked, routes each request to the right context at the right cost, and gets measurably more efficient over time. A 10% productivity gain across a client's AI practitioners — at average billing rates — recovers millions in annual capacity. That is the deliverable your competitors cannot replicate.
Integrate Into Your Platform
ISVs, SaaS platforms, developer tool companies
Add intelligence to your software without building it from scratch. grāmatr exposes two integration surfaces: an MCP server for AI-native tools, and a REST API for traditional platform integration. Your users get an AI that learns their preferences, carries context across sessions, and gets smarter with every interaction — inside your product.
The patent-pending routing architecture handles the complexity. Your platform sends a request; grāmatr classifies it, determines the optimal context payload, and returns the intelligence your AI needs — in 1,200 tokens instead of 40,000. Your API costs drop. Your user experience improves. Your AI actually gets smarter.
Two surfaces. One intelligence layer.
grāmatr is model-agnostic because the intelligence sits above the model layer. The pre-classification routing runs before any LLM sees the request — so it works with Claude, ChatGPT, Gemini, Codex, VS Code Copilot, and any MCP-compatible platform. Switch models tomorrow; the intelligence layer and everything it has learned travels with you.
For AI tools that support the Model Context Protocol. Claude Code, Cursor, Windsurf, and the growing ecosystem of MCP-enabled platforms connect directly. The intelligence layer becomes part of the AI's context pipeline — no middleware, no proxy, no additional latency.
For SaaS platforms and enterprise applications. POST a request, get back a classified intelligence packet — effort level, intent, relevant context, behavioral directives — in 1,200 tokens instead of 40,000. Authenticate with API keys, scope to individual users or teams. Your platform gets pre-classification intelligence without building its own routing engine.
Both surfaces connect to the same intelligence layer. A user who works in Claude Code during the day and your platform in the evening gets continuous, compounding context across both. One brain, every tool.
Built for regulated industries.
If your clients are in financial services, healthcare, legal, or government — they need more than AI productivity. They need audit trails, data isolation, and governance that stands up to regulatory scrutiny. grāmatr was architected for this from day one.
Auditable classification logs
Every AI decision routed through grāmatr generates a classification log — what was requested, how it was classified, what context was provided, and why. Your clients' compliance teams can audit exactly what their AI did and why it did it.
Per-user encryption
User data is encrypted at the individual level with row-level security. Not tenant-level — user-level. Even grāmatr staff cannot access user data. Team and enterprise tiers add additional encryption layers with administrative governance.
Tiered training governance
Nothing trains without authorization. User-level learning stays isolated by default. Team-level sharing requires admin approval. Enterprise-level intelligence requires organizational authorization. Every training event is logged and reversible.
Data residency Roadmap
Regional deployment options for jurisdictional requirements. The architecture is designed to support data residency — whether your clients need data to stay within the EU, a specific country, or their own infrastructure.
For partners deploying in regulated industries: every classification log, every training event, and every intelligence update is traceable. Your clients' compliance teams do not need to trust that the AI behaved correctly — they can verify it.
Not memory. Intelligence.
Your clients have tried "AI memory" tools. They store past conversations and retrieve them when keywords match. That is search, not learning. It is a filing cabinet, not a brain.
grāmatr's patent-pending architecture does something different. It pre-classifies every request before the AI model sees it, determines the optimal context to include, and routes the intelligence that will actually help — in 1,200 tokens instead of dumping 40,000 tokens of "everything we remember" into every prompt.
The result: AI that gets smarter with each interaction. Not because it stores more. Because it understands which context matters for this specific request, right now. That is the difference between retrieval and intelligence — and it is what your clients will pay for.
Cross-session learning compounds over time. In production, the classification pipeline processed over 5,830 requests in one week, with 1,933 learning corrections feeding back into the system — each correction making the next routing decision more accurate. A practitioner's AI on day 90 is more efficient than on day one because the intelligence layer has learned their patterns, their preferences, and their decision-making style. That compounding effect is what turns a productivity tool into an intelligence asset.
Partner questions, direct answers.
What does the revenue share model look like?
Revenue share is based on the number of active users you bring to the platform. The exact structure depends on your deployment model — whether you are embedding in client solutions, reselling as part of a managed service, or integrating into your own SaaS. We structure the economics so the partnership is profitable for both sides from day one. Start a conversation and we will scope the specifics to your model.
How does branding work in client deployments?
Partner deployments carry a "Powered by grāmatr" badge — your brand stays front and center, with grāmatr visible as the intelligence layer behind it. Full white-label (grāmatr invisible) is not available. This is intentional: every deployment builds the grāmatr brand across domains, which strengthens the platform's credibility for all partners. You get your branding, your domain, your tenant isolation — grāmatr gets attribution.
How does client data isolation work?
Each client deployment is fully isolated. Per-user encryption with row-level security means that Client A's data is invisible to Client B — and invisible to your organization unless the client explicitly grants access. This is enforced by architecture, not policy. Even grāmatr staff cannot see client data.
What AI models does grāmatr support?
grāmatr is model-agnostic. It currently works with Claude, ChatGPT, Gemini, Codex, and VS Code Copilot — any platform that supports MCP. The REST API extends this to any AI integration. If a new model launches tomorrow that is better for your use case, the intelligence layer travels with it. No vendor lock-in on the model layer.
What is the integration timeline?
For MCP-native integrations: your platform connects through the standard MCP protocol. If your tool already supports MCP, the integration is configuration — not development. For REST API integrations: standard REST endpoints with API key authentication. Typical integration timeline depends on your platform's architecture, but the API surface is intentionally small. Talk to our team about your specific integration path.
Is the patent-pending architecture defensible?
The patent application covers the pre-classification routing architecture — the method by which grāmatr determines what context to send before the AI model processes the request. This is not a wrapper or an API aggregator. It is a novel approach to context engineering that reduces token usage by 97% while improving response quality. The patent is a fact of the architecture, not a marketing claim.
Start a partner conversation.
Whether you are deploying for 30,000 practitioners or integrating into a SaaS platform, the first step is the same — a conversation about your specific model. Start a Partner Conversation
Want to dig deeper first? See the proof behind the claims, learn how the intelligence layer works, review enterprise governance, or explore pricing.