Platform technology
The engine inside the one brain
GIBE — the Gravitre Intelligent Business Engine — learns from your connected stack and routes actions through governed, human-approved execution. Memory, models, and judgment for the same brain that powers Gravitre AI, agents, and workflows.
GIBE path
From knowledge to governed outcomes
One signature TRACE — how organizational intelligence moves through relationships and evidence before anything executes.
How GIBE moves from org knowledge to a governed business outcome — counts and paths only, not a silent trained claim.
Orchestration
One intent, coordinated work, one verified outcome
An illustrative Task Decomposition Field — how a request can become a plan, specialized capabilities, tools, approval, and evidence. Not a live run.
Intent
Find at-risk customers and prepare the right follow-up.
Outcome
Resolves after verification.
Find at-risk customers and prepare the right follow-up.
System at rest.
Illustrative orchestration — not a live run. Governance pauses writes; learning is advisory.
Knowledge Fabric
Mentions converge when the match is exact
An illustrative Entity Convergence field — normalize, exact-match, and evidence. Not fuzzy person matching. Not a live org graph.
Mentions
Acme Corp
acme corp.
Sarah
Sarah Smith
Match gate
Waiting for mentions
Resolved
No entity yet.
Mentions of the same company arrive from CRM and email — they should resolve to one identity.
Acme Corp
acme corp.
Sarah
Sarah Smith
Knowledge Fabric at rest — mentions are not yet resolved.
Illustrative entity convergence — exact and normalized mention match. Not fuzzy person matching. Not a live org graph.
An MCP server with a brain
Connectors give Gravitre hands. GIBE gives it memory, ML models, and judgment — scoped to your org, verified before it acts.
Observe → Learn → Recommend → Execute (with approval)
Perception
Connectors, RAG, and event hooks ingest what your business actually does — not generic internet noise.
Memory
Vector search, promoted org memories, entity graph links, and glossary terms — ranked and auditable.
Reasoning
ML catalog models, retrieval rankers, predictive ops packs, and routing that shows why it picked an answer.
Autonomy
Workflow execution, approval gates, failure predictions, and continuous learning from measured outcomes.
Example: HubSpot is connected, but contact search permission is missing. Confidence: Medium. Complete OAuth scopes before this workflow runs.
From your stack to audited action
Connectors, runs, and queries feed the Gravitre Intelligent Business Engine — scoped to your org, never pooled across customers.
Ingest
Connected tools, RAG sources, workflow runs, and chat queries enter GIBE as org-scoped signals — not generic internet noise.
Learn
Perception normalizes events. Memory stores promoted facts and entities. Reasoning trains rankers and ML models only when data gates pass.
Recommend
Insights, failure predictions, and proactive suggestions surface with confidence scores, routing traces, and plain-language reasons.
Execute
Writes route through approval policy. You get verified links, honest summaries, or a clear failure — every step logged.
Security you can trust
Built with security-first principles including role-based access control, complete audit trails, encrypted data, and human-in-the-loop approvals for sensitive actions.
- Role-based access control
- Complete audit trails
- End-to-end encryption
- Human-in-the-loop approvals
How Gravitre thinks before it acts
Human approval, org-scoped models, and security controls built into execution — not bolted on after the fact.
Governance
Human-in-the-loop approval
Every write waits on your policy. Chat, guided tasks, Marketplace installs, and workflows use the same gate.
Audit trail
Approvals, connector writes, and run outcomes — reviewable and exportable from your audit page.
Role-based access
Who can approve, run agents, or publish to Marketplace — controlled from Settings and org roles.
LLM and intelligence stack
Org-scoped LLM routing
Gravitre AI routes execute, chat, and search with live connector checks — MCP-native, not pooled customer training.
Built-in ML catalog
Intent classifiers, anomaly detectors, rankers, and predictive ops — each with TRAINED / data-gate status per org.
Memory and retrieval
Vector search, promoted memories, entity graph, and glossary — ranked with confidence, not hidden chain-of-thought.