AI-native enterprise infrastructure
Draevor Systems builds AI platforms for operational intelligence, automation, and decision support — where governance, observability, and zero trust security are part of the architecture rather than a compliance layer bolted on afterwards.
Operating principles
Any vendor can claim to be secure. What matters to a buyer is whether the claim is a property of the system or a paragraph in a brochure. These four commitments constrain how Draevor platforms are designed, not just how they are described.
Zero trust boundaries, encryption in transit and at rest, scoped short-lived credentials, and least-privilege service identity. Security decisions are made at design review, not retrofitted before a customer questionnaire.
Model usage, data lineage, and automated actions are recorded as first-class events. Policy is expressed as configuration under version control, so what the system is permitted to do is reviewable and diffable.
Designed for graceful degradation: bounded blast radius, explicit timeouts and backpressure, and health signals that distinguish a degraded dependency from a failed one. Failure modes are specified, not discovered.
Architected to support the evidence that regulated buyers ask for — immutable audit trails, data residency controls, retention policy, and access review exports. We describe the controls we build; we do not claim certifications we have not completed.
Product ecosystem
Each platform solves a distinct operational problem, but they are not four separate stacks. Identity, policy, audit, and observability are inherited from Draevor AI Core — so a control implemented once applies everywhere.
AI Content Intelligence Platform
Automated creation, processing, and intelligence workflows for digital content.
OneMinute turns content operations into a pipeline rather than a series of manual steps. Source material is ingested, analysed, transformed, and routed through repeatable workflows, with generated output tracked back to the inputs and model versions that produced it.
Built for
Creators, agencies, and content-led businesses
Capabilities
AI Operational Intelligence Platform
Real-time monitoring, data fusion, and operational awareness across distributed systems.
Draevor correlates signals that normally live in separate tools — infrastructure telemetry, sensor feeds, logistics events — into a single operational picture. It is built to answer the question an operator actually has: what changed, does it matter, and what should happen next.
Built for
Infrastructure, logistics, and operations teams
Capabilities
Strategic Risk Intelligence Platform
AI-assisted risk analysis, strategic intelligence, and decision support.
Stratova supports analysts working on questions that do not have a single correct answer. It structures evidence, surfaces the drivers behind an assessment, and keeps the reasoning legible — so the output is an argument a decision-maker can interrogate, not a score to be taken on faith.
Built for
Risk, strategy, and analysis functions
Capabilities
Enterprise AI Foundation
The orchestration, governance, and observability layer every Draevor platform runs on.
AI Core is the substrate: it routes work across models, enforces policy at the call boundary, and records what happened. Because governance sits in the foundation rather than in each application, a control added once is inherited by every product above it.
Built for
Platform, security, and engineering teams
Capabilities
Reference architecture
Four layers, one direction of flow. Governance, security, and observability are not a fifth layer at the end — they run vertically through every stage, which is the only way an audit trail survives contact with a real system.
Streaming and batch ingestion from internal systems, sensors, third-party APIs, and operational databases. Every record is tagged with origin, timestamp, and classification at the boundary.
Model orchestration, retrieval, and evaluation. Requests are routed by policy, guardrails are applied at the call boundary, and every inference is recorded with its model version and inputs.
Where isolated outputs become a picture: entity resolution, cross-source correlation, scoring, and simulation. This is the layer that distinguishes an event from a situation.
Operator-facing surfaces and programmatic outputs. Actions with real consequence are gated behind human confirmation, and every action is written back to the audit trail.
Security engineering
Zero trust is not a product you buy; it is a set of decisions you make repeatedly. These are the ones Draevor platforms are architected around.
No implicit trust from network position. Every request is authenticated and authorised on its own merits, service-to-service calls carry verifiable identity, and segmentation limits what a compromised component can reach.
TLS 1.3 for data in transit and authenticated encryption at rest. Key material is held in a managed key service with defined rotation, and application code handles key references rather than raw keys.
Designed to federate with enterprise identity providers via OIDC and SAML, with role-based access control, SCIM-style provisioning, and short-lived scoped credentials in place of long-lived static keys.
Security-relevant events — authentication, authorisation decisions, data access, model invocation, configuration change — are written to an append-only trail with a stable schema suitable for export into a customer SIEM.
Structured logs, metrics, and distributed traces with correlation IDs that survive across service and model boundaries. If an outcome cannot be reconstructed after the fact, it is treated as a defect.
Dependency and vulnerability scanning in CI, secret scanning on every commit, infrastructure defined as reviewed code, least-privilege build credentials, and threat modelling at design review.
Responsible AI
Autonomy is easy to demo and difficult to defend. Draevor platforms are designed so that consequential decisions keep a human in the loop, and so that the reasoning behind an output can be examined rather than assumed.
Actions with material consequence are gated behind explicit human confirmation. Autonomy levels are configurable per workflow, and the default is the conservative one. Escalation paths are defined before deployment, not improvised during an incident.
Outputs are delivered with the evidence and inputs that produced them, including source references and model version. An operator should be able to answer "why did it say that?" without opening a support ticket.
Model selection, prompt and policy configuration, data handling rules, and permitted actions are versioned, reviewable artefacts. Changes to what the system may do go through change control like any other production change.
Evaluation runs before release and monitoring continues after it: drift detection, output quality checks, and defined rollback paths. Known limitations are documented and surfaced to operators rather than hidden behind a confident interface.
Enterprise readiness
Enterprise-ready architecture means deploying into an existing environment without demanding that the environment change first.
Containerised, stateless services with externalised configuration and declarative infrastructure. Designed to run across major cloud providers or in a customer-controlled environment where data sovereignty requires it.
Containerised · IaC-definedEvery capability in the platform is reachable through a documented, versioned API. The interface is a client of the same API, which keeps the contract honest.
Versioned · OpenAPI-describedPackaged for orchestrated deployment with health and readiness probes, resource limits, horizontal scaling, and rolling updates with defined rollback.
Helm-packaged · Probe-instrumentedOpenTelemetry-based instrumentation for traces, metrics, and logs, exportable to the monitoring stack a customer already operates rather than a proprietary console.
OpenTelemetry · ExportableHorizontally scalable services with asynchronous processing, queue-backed workloads, and backpressure, so that load growth is a capacity decision rather than a rewrite.
Horizontal · Queue-backedEngineering credibility
Draevor Systems is an engineering-led company. The platforms described on this page are designed by someone who has spent their career operating production infrastructure — where the difference between a working demo and a reliable system is the part nobody writes a blog post about.
The through-line of that work is unglamorous and load-bearing: designing cloud architecture that survives its own growth, running deployment and reliability practice for systems people depend on, and instrumenting environments well enough that failures can be explained rather than guessed at.
That background is the reason Draevor platforms lead with governance, observability, and zero trust rather than model benchmarks. Anyone can wire an API call to a language model. The engineering problem is everything around it — identity, policy, failure handling, audit, and cost — and that is where the actual product is.
“The hard part of enterprise AI was never the model. It is proving, months later and to someone who was not in the room, exactly why the system did what it did.”
Engineering disciplines
Infrastructure & AI engineering
Production systems across their full lifecycle — design, deployment, operation, and incident response.
Distributed system design, network and identity boundaries, and infrastructure defined as reviewed code.
CI/CD pipelines, containerisation and orchestration, observability instrumentation, and reliability engineering.
Model orchestration, retrieval architectures, evaluation, and the governance layer production AI actually requires.
We are happy to go deeper than a landing page allows. Architecture walkthroughs, threat models, and honest answers about current limitations are available on request.
Contact & partnership
We would rather have one substantive engineering discussion than fifty marketing calls. If you are evaluating AI infrastructure, running a pilot, or exploring a technical partnership, tell us what you are actually trying to solve.
Assessing OneMinute, Draevor, Stratova, or AI Core against a real workload. Expect architecture diagrams and direct answers about current limitations.
Integration, joint delivery, or building on top of Draevor AI Core. We are interested in partners with domain depth we do not have.
Security questionnaires, architecture review, or responsible disclosure of a vulnerability. Reports are acknowledged and taken seriously.