The context layer that makes AI agents actually work.
Gravas builds the context layer that makes AI agents actually work inside a business. We also build the products, transformation, and training to put it to work.
Scanned 14 systems · 832 entities mapped
Resolved 47 dependencies · confidence 96%
Drafted credit memo · policy match
No anomalies · awaiting Gate approval
Agents are powerful. Your enterprise is invisible to them.
Models got smart. The new constraint is what they can see of your business at the moment they act.
Most enterprise AI stitches together fragments. A vector search here. A document chunk there. It works for demos. It breaks in production.
The fix is not a better prompt. It is a better substrate.
More context, worse answers
That rule applies to LLM-heavy systems. Gravas is built differently.
Dumping every document, thread, and database row into a prompt pushes cost and latency up while accuracy collapses. Past a point, each extra token makes the model worse.
Gravas feeds the right context at the right grain. The substrate stays in the optimal zone — accurate, fast, and cheap — even as enterprise complexity grows.
Illustrative — directional, not benchmarked.
Directional context supported by Anthropic, McKinsey / QuantumBlack, and MuleSoft / Salesforce reports.
Three ideas. One platform.
Context Graph
A living model of how information relates across your enterprise: customers, contracts, workflows, decisions, dependencies. Continuously enriched and queryable.
Context Engineering
The discipline of assembling the right context at inference time: retrieval, ranking, compression, memory. The graph is the source. Engineering is the delivery.
AI-Native Enterprise
The operating model. Software, workflows, and teams designed around agents that understand the business. Not bolted-on chatbots that guess at it.
Four pillars. Every cycle makes the next one stronger.
Platform-first
Every engagement strengthens the Context Graph.
Ship, then refine
Deployed beats specified.
AI-native by default
We use our own tools before recommending them.
Outcome over activity
Revenue, cost, cycle time. Not hours worked.
Own the full stack
If we recommend the rebuild, we do the rebuild.
Hover a node to see how the system reinforces itself.
Operating software where the agents do the connecting.
Gravas ERP collapses CRM, ERP, field service, and recurring contracts into one platform. The same Context Graph that understands your customers also drives the agents that handle service orders, billing, and renewals.
- ✓Ask the system, work inline, or use traditional forms.
- ✓Agents reconcile invoices against orders, contracts, and history.
- ✓Humans review exceptions; agents handle the rest.
“Metroline is disputing the March invoice. Should we approve the credit memo?”
Credit memo is justified. Terms match contract, history shows similar pattern, and amount is within delegated authority.
Map. Build. Operate. Compound.
Strategy and execution live in the same team. We do not hand off to an implementation partner.
Map
AI agents interview your team and reconstruct the operating reality in the Context Graph.
Build
Engineers and agents ship production workflows on the platform, not slide decks.
Operate
Agents run the work; humans set direction. Memory persists across sessions and teams.
Compound
Every deployment teaches the platform. The next engagement starts smarter.
Built for operators, not tourists.
Mid-market service businesses with real operating complexity and no appetite for a year-long integration theater.
Real workflows
Invoicing, AR, inventory, HR, operations. Not FAQ bots.
Real outcomes
Days to cash, error rates, cost per transaction.
Real ownership
We build and operate what we recommend.
Real speed
First agents deployed in weeks.