ThecontextlayerfortheAI-nativeenterprise.
A living map of how your business works, the engineering to feed it to AI, and the operating model to build software on top.
Models got smart. Enterprises stayed opaque.
The hard part is no longer how smart the AI is. It's how much it can see of your business at the moment it acts.
Most AI reaches for information in fragments. So agents act on half the picture, contradict themselves, and need babysitting exactly where they were meant to save you time.
The fix isn't a cleverer prompt. It's a better foundation underneath.
A living representation of your enterprise.
The Gravas Context Graph captures how everything in your business connects — and keeps itself up to date as things change.
It is organized into four parts: Rules, Concepts, Schema, and Dependencies.
Rules
Your policies and limits, written down so agents work within them — not around them.
Concepts
The real things your business runs on — 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.
The map is the source. The AI needs the right slice of it at the right moment — that's the engineering in between.
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.
A team of agents, not one big chatbot
Specialized agents with clear roles, limited access, and lasting memory, working together.
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.