ThecontextlayerfortheAI-nativeenterprise.
A proprietary context graph, the engineering discipline to deliver it, and the operating model to build software on top of it.
Models got smart. Enterprises stayed opaque.
The constraint is no longer model intelligence. It is what the model can see of your business at the moment it acts.
Most enterprise AI reaches for context in fragments. Agents act on partial information, contradict themselves across sessions, and require human babysitting exactly where automation was supposed to pay off.
The fix is not a better prompt. It is a better substrate.
A living representation of your enterprise.
The Gravas Context Graph (internally code-named Aether) captures how information relates across your business as a continuously enriched graph.
It is organized into four buckets: Rules, Concepts, Schema, and Dependencies.
Rules
Policies, constraints, and governance logic encoded so agents reason against them, not around them.
Concepts
Business vocabulary as first-class entities: customers, products, contracts, workflows, decisions.
Schema
Structural definitions kept in sync with the systems that produce them.
Dependencies
How workflows, decisions, approvals, and agents connect across the enterprise.
The discipline of delivery.
A graph is a source. Models need delivery. Context Engineering bridges them.
Retrieval strategy design
Hybrid retrieval combining vector search, graph traversal, and re-ranking, selected per task.
Context window optimization
Compressing, summarizing, and prioritizing so the model receives signal, not noise.
Prompt architecture
Prompts as software: versioned, tested, observable. Not artisan craft.
Graph-informed assembly
Using graph structure to decide what context belongs in a given inference.
Memory management
Persistent, structured memory per agent and workflow across sessions.
A new way to build software.
The Gravas platform is built on the same operating model it enables.
Agent fleet, not monolithic chat
Specialized agents with defined roles, scoped tool access, and persistent memory, coordinated through standard protocols.
Tools as first-class infrastructure
A registry of well-defined, versioned, observable tools gated by role and configuration.
Memory that persists
Per-user, per-agent memory with explicit pin, forget, and recall semantics.
Configure by description
Describe workflows, objects, dashboards, and agents. The system constructs them.
We've built this before. Now we're building it as a platform.
The patterns inside Gravas are drawn from production systems built and operated by our team, measured, observed, and refined under real load.
Hybrid retrieval, layered▼
Vector search, knowledge graph traversal, and re-ranking working together with strict tenant isolation.
Agent operating model▼
Specialized agents with defined roles, persistent memory, tool access through standard protocols, and human-in-the-loop gates.
Safety by configuration▼
Read-only review agents, bounded pre-approvals, destructive operations never pre-approved by default.
Multi-provider by design▼
Anthropic, OpenAI, regional providers, and open-source models through a single abstraction.
Everything we ship runs on it.
The platform is not a standalone product. It is the foundation beneath everything Gravas does.
Products
Products built on Gravas inherit context-aware agents the moment they ship.
AI Transformation engagements
Engagements extend the platform, leaving every client with durable infrastructure.
Training programs
Training teaches the same patterns the platform enforces.
Enterprise-grade. Mid-market practical.
Most AI infrastructure is sold to companies with 500-person platform teams. Gravas is built for businesses with real complexity but no appetite for a year-long integration project.
Multi-tenant by design
Tenant isolation enforced architecturally, not promised contractually.
Cloud-native
Built for modern cloud deployments with observability, security, and compliance in mind.
Built by operators
Shipped mid-market SaaS for two decades and know what actually deploys.