Enterprise automation platform
Grounded automation for the regulated enterprise.
AI-native automation, grounded in enterprise architecture, international standards, regulations, and policies.
Platform
Design it from your architecture. Govern it by your policies.
ContextEA is an AI-native intelligent automation platform grounded in the enterprise architecture, metamodel, and organizational artifacts that define an organization's business, processes, capabilities, information, applications, technology, policies, controls, and governance.
It uses this enterprise context to intelligently design and generate automation, AI agents, and solutions aligned with the organization's intended architecture and operating model — while providing guaranteed verification against specifications, policies, governance requirements, and other constraints at both design time and runtime.
Design studio
Design intelligent, agentic automation from the architecture you already have.
Your capability maps, process models, application portfolio, information models and reference architectures are not documentation. They are the design inputs. ContextEA reads them and generates automations, applications and AI agents that realize the enterprise as it is intended to be.
Import from the EA tools you already run. Every generated object traces back to the capability, process and policy it realizes, so the architecture stays the source and the automation stays aligned.
Start from your EA artifacts
Capability maps, process models, application and information architecture — imported from the EA repositories and modelling tools you already use, keeping their source identity.
Compose agentic automations
Describe the intent; the studio proposes workflows, generated applications and AI agents as design objects — each with a role, the data it may touch, the tools it may call and the contracts that bound it.
Reuse reference architectures
Industry reference models and standards packs supply ready patterns and vocabulary. Design objects are reusable across the enterprise, not rebuilt per project.
Generate, promote, govern
What is designed is generated and promoted to runtime with its contracts attached. Governance is not a later step. It is part of the design object from the first draft.
How it works
Enterprise intent, intelligently executed.
The organization's own architecture and knowledge determine what should be built. What is built — and what executes — is continuously verified against enterprise intent.
Ground in the enterprise's own artifacts
Architecture models, standards packs, regulations and internal policies are ingested as context. Each clause is captured with its provenance (the document, the location, the text), so nothing enters the system without a source.
Turn intent into verifiable contracts
Policies and requirements become explicit contracts: what must hold, what should hold, what may hold, and what happens when it doesn't. Automation, applications and AI agents are generated against those contracts, not around them.
Prove at design time, gate at runtime
Contracts are proven at generation, tested after, and gated at runtime. An agent that would act outside policy is stopped by the policy's own clause, and every outcome carries the evidence of which check, from which artifact, made the decision.
Grounded in
Four sources of truth. One line of evidence.
Enterprise architecture
The metamodel and architecture artifacts that define business, processes, capabilities, information, applications and technology: the intended operating model, not an approximation of it.
International standards
Industry reference models and message standards, adopted as packs so generated solutions align with the vocabulary and structure the sector already runs on.
Regulations
Regulatory obligations expressed as enforceable contracts with provenance to the clause, so compliance is demonstrated by evidence rather than declared by assertion.
Policies
The organization's own policies, controls and governance requirements, captured verbatim, promoted deliberately, and enforced consistently wherever they apply.
The promise
Verify, don't assert.
ContextEA connects enterprise intent with intelligent execution. It uses the organization's own architecture and knowledge to determine what should be built, and continuously verifies that what is built and what executes remains compliant, authorized, and aligned with enterprise intent.
Enterprise-ready by design
The gates a regulated enterprise has to clear are built in, not bolted on.
Four properties, each enforced in code and proven live on every release, independent of any AI model's behaviour.
01 · Architecture
One door for every side effect.
Policy is enforced by the verification gate and the tool gateway, platform components with defined rules, never by a model's judgement. A blocked action is blocked with a model-call count of zero.
- Nothing pre-loaded: content enters only through governed doors, with lineage on every instance
- Replaceable at the seams: parsing and models sit behind versioned contracts
- Backward compatibility by design: the metamodel only ever grows, and older instances stay valid
02 · Security
Security first. Proven red, then green.
Every control starts as a failing test. It passes only once the control exists, and its refusal case joins the regression that runs on every release. The platform expects to be attacked, and can show a tester exactly where the doors are.
- Attack surface enumerated: a version-controlled list of public routes, default-deny inside the cluster
- Nothing to find in the artefact: no secrets in images, charts or environment; keys generated in the vault and never exported
- Findings fixed at source: every image scanned on every build, zero open, no waivers
03 · Deployment
A dedicated instance inside your boundary.
One infrastructure-as-code source, one chart, per-environment parameters. Nothing hand-built, and configuration drift fails the build. The application layer is cloud-agnostic on Kubernetes: the reference deployment runs in the UAE region, and the same adapters are designed to carry it to other clouds or on-premises clusters.
- Model endpoint, identity provider, key custody and residency are each a configuration of one component
- Documents held in memory only for the life of a job, then purged
- Evidence carries digests and references, never payload values
04 · Quality & scale
Tested the way it will run, from day one.
Real databases and brokers, real network policy, real identities. Tests prove what must be refused, not only what must pass. Every image is scanned and pinned by digest, and nothing is mocked in any environment.
- Refusal tests are acceptance criteria: six identity refusals and four perimeter refusals run on every release
- Bounded by design: rules and governed reads run under hard budgets for time, rows and bytes
- Stateless services scale by replica and roll without downtime; stateful tiers run as managed services with high availability and geo-redundancy as parameters
Who it's for
Built for enterprises where every action has to be defensible.
Regulated organizations that need AI-driven automation to respect their architecture, their standards and their regulators, with evidence to show for it.
Designed for sovereign deployment: on-premises and air-gapped environments where data, models and decisions stay inside the organization's own boundary.
Contact
See enterprise context become enforced contracts.
A 30-minute briefing on how ContextEA grounds automation and AI agents in your architecture, standards and policies.