Vela turns large volumes of unstructured information into governed, traceable, decision-ready intelligence. It is designed for organisations that need AI to improve judgment, speed, and consistency without losing control, security, or auditability.
The platform is designed around five executive concerns: business usability, architectural separation, security by design, operational resilience, and explainable AI outputs. The goal is not simply to retrieve content, but to produce reliable, reusable intelligence that can stand inside enterprise workflows.
Documents, spreadsheets, decks, policies, and research are converted into structured assessments and reports aligned to a business process.
User applications, orchestration, storage, AI services, and cross-cutting controls are separated so each layer can evolve without compromising governance.
Vela evaluates evidence, applies rubrics, and verifies outputs, reducing the operational risk of unsupported AI responses.
Usage, actions, and evidence lineage are captured so outputs can be reviewed, defended, and improved over time.
Vela Intelligence is a governed intelligence platform. For a CIO, the result is a platform that is easier to reason about: clearer control boundaries, stronger auditability, lower privilege ambiguity, and a better fit for high-consequence enterprise decisions.
Business applications, decision workflows, intelligence services, enterprise controls, and the secure data foundation operate as distinct layers, not a monolith.
Runtime identity separation, row-level enforcement, and narrowly scoped privileged operations are not application-layer assumptions — they are database-layer facts.
Every intelligence output passes through evaluation and verification steps before reaching users. This is a structural property of the platform, not an optional feature.
Vela improves the quality of evidence available to decision-makers. It does not replace human judgment in high-consequence processes.
At a business level, Vela can be understood as five layers. This is the view that matters for governance, integration, and scale.
The platform's security model is deliberately stricter than a conventional application stack. Access is enforced by identity separation and collection-level controls, not by application-side convenience logic.
When McKinsey was compromised in a widely reported breach, the most damaging consequence was not the initial intrusion — it was what happened next. Without architectural separation between control operations and data access, attackers were able to move laterally across the data estate with minimal friction. A single point of compromise granted broad authority across client data because no structural boundary existed to contain it. For any platform handling sensitive business intelligence, the lesson is direct: if the control plane and the data plane share identity and authority, a breach of one is effectively a breach of both. Vela's architecture is built around the opposite assumption.
Vela uses one bootstrap-only owner identity and four distinct runtime identities. Each operational context connects with its own authenticated identity, rather than relying on broad privileges plus role switching. Control-plane operations, document administration, scoped user access, and system-only access are separated by design.
Each identity can do only the work it was created for. That reduces accidental privilege creep, makes access review easier, and strengthens auditability for CIO, CISO, and platform governance teams.
Manages collections, indexes, settings, and platform control operations. Does not directly access business documents.
Handles unrestricted document work for approved non-system data sets, including ingestion and maintenance tasks, segregated from control-plane administration.
Used for least-privilege, collection-scoped document access. Permissions narrow access to exactly what is needed; they do not widen it.
Reserved for platform-controlled system records only. Intentionally isolated from ordinary business collections.
The McKinsey breach also demonstrated a second failure mode: once attackers held valid credentials, they could impersonate legitimate users and access any data those users could reach. Without collection-level boundaries, a compromised identity becomes a master key. Vela's model addresses this directly — permissions are stored in VelaAuth, an isolated and hardened permission store, and access is scoped to collections rather than granted broadly. A compromised credential can only see what that identity was explicitly permitted to see.
The application does not carry its own durable permission state. Access checks are enforced through VelaAuth — a secure, isolated permission store — reducing the risk of drift between business logic and actual authority.
Access boundaries are enforced at the collection level across documents, keys, history, and vector chunks. The system collection remains separate and cannot be exposed through ordinary scoped access.
Privileged database actions are exposed through tightly scoped, owner-controlled functions. This keeps sensitive operations narrow, inspectable, and operationally safer.
Resilience, traceability, provider independence, and deployment flexibility — designed for organisations where uptime, observability, and infrastructure choice all matter.
Every request is authenticated and authorised before any data access occurs. An unverified or insufficient identity is rejected immediately — not degraded gracefully. Attackers cannot get deep enough to probe collections, test boundaries, or exploit partial access states.
Audit and usage controls sit beside the core platform so organisations can monitor who did what, when, and at what operational cost. Every intelligence output, evidence retrieval, and user action is logged — giving CIO and CISO teams a complete, inspectable record for governance, review, and incident response.
Vela's AI layer is provider-agnostic. The platform works with major AI providers interchangeably, so organisations are not committed to a single vendor's roadmap, pricing, or data terms. Model choice can be adjusted over time without redesigning the intelligence layer above it.
Vela runs where your data governance requires. On-premises in your own infrastructure, in a private cloud, as a fully isolated managed instance, or as a shared environment for less sensitive workloads. The deployment model is a configuration choice, not an architectural constraint.
We work with enterprise technology and governance teams who need a detailed view of the platform architecture, security model, and deployment options.