CortexThe governed
AI control plane.
Agents that build, run, and support your software, under your policies, on your infrastructure. The substrate for the systems you simulate before you build them.
Alpha Omega Labs · Enterprise briefing · 2026
Code is a substrate.
Like electricity, it changes
everything the moment we
modulate it.
Software that shapes itself at runtime, and simulates reality before we build it
Stand up a world. Ask it a question.
Intelligence got cheap. Finding out whether a proposal is true stayed expensive, and it is the only part anyone pays for. So the compute moves: from generating an answer to running the experiment that settles it.
Nearly free
A design, a molecule, a control policy, an entire service. Generation is commoditizing and the price keeps falling.
The expensive half
One good hypothesis is ten thousand runs: the sweep, the ensemble, the adversarial suite, the rerun six months later when someone challenges the result.
It gets cheaper
Each validated result becomes something the organization keeps and reuses, so the next question costs less than the last one.
→ Demand is not saturating. It is moving, from generation to verification, and verification is the larger half.
A substrate you can reshape can burn the building down.
Electricity waited on breakers, meters, and grounding before anyone ran it through a building where people live. Software that rewrites itself needs the same grid, and not having one is why capable AI still stalls in pilots.
Isolation
A run that can reach anything proves nothing and risks everything. Every experiment needs its own bounded world.
Authority
Something has to bound what an agent may do, what it may touch, and what it may spend, independently of who is driving it.
Evidence
A result nobody can reconstruct is not a result. Peer review and the AI Act ask for the same artifact.
→ Enterprises are not blocked on capability. They are blocked on permission.
The model was never the problem.
Enterprise AI doesn't fail the technical evaluation. It fails the review that comes after, because a chat window can't answer the three questions that decide whether anything ships.
Where is our data?
Which systems does the AI touch, where does context go, and who else can see it? Shadow AI is already inside the building.
Who approved that?
When an agent deploys, installs, or deletes: on whose authority? Most platforms cannot produce a single accountable record.
Why did it act?
Auditors and regulators ask for reconstruction: inputs, policies, approvals, outcomes. Retrofitting evidence is where projects die.
→ Cortex was built so these three answers exist by construction.
Agentic AI is the fastest-scaling enterprise software wave.
Enterprise agentic AI market, USD · Grand View Research 2025 (46.2% CAGR; intermediate years interpolated at stated CAGR). Corroborating: MarketsandMarkets 2025, AI agents $7.8B (2025) → $52.6B (2030).
→ Near term, the wedge is governed delivery. The survivors of that shakeout are the governed ones. Long term, the same substrate is where the experiments run.
The EU AI Act has moved from policy debate to operating requirements.
Inventory and classification · data governance (Art. 10) · automatic event logging (Art. 12) · transparency (Art. 13/50) · human oversight in operation (Art. 14) · technical documentation.
Penalties remain material: up to €35M or 7% for prohibited practices and up to €15M or 3% for most operator violations. Yet over half of organizations still lack a basic AI inventory, the first artifact every program needs.
Sources: European Commission AI Act page + AI Act Service Desk timeline · EUR-Lex Regulation (EU) 2024/1689 Art. 99 · Cloud Security Alliance readiness note, 2026. Full notes: pitch/research/eu-ai-act.md
European enterprises are pulling AI back inside the perimeter.
Cortex is one of the few platforms where cloud and fully on-premise are the same product, identity, memory, observability, even voice transcription run locally. Model calls go only to the providers you configure, under your policies.
Sources: IDC Worldwide Digital Sovereignty Survey 2025 · Broadcom Private Cloud Outlook 2026 · Grand View Research 2025 (Europe sovereign-cloud market ~$56B in 2025). Full notes: pitch/research/sovereignty.md
Simulating reality is a decade of work. Here is the order it has to happen in.
A substrate nobody is allowed to run is worth nothing, so permission comes first. Everything after this slide is one of these four layers.
Make it allowed
Capability policy, approval gates, audit by construction, sovereign deployment, and the records an AI Act review asks for. Nothing else gets used until someone can say yes.
Make it real
One control plane running agents, deploys, memory, channels, and observability on your infrastructure. A substrate has to be operated, not just described.
Make it checkable
An open benchmark, plus compliance evidence produced by normal operation rather than retrofitted for an audit.
Make it pay off
The research loop: question, governed run, evidence, reusable capability. This is where simulation actually lands.
→ Layers 01 to 03 are the shipped control plane. Layer 04 is where our prototypes and current R&D sit, and it is the reason the first three exist.
One governed control plane between your people, your agents, and your infrastructure.
→ Every action, on every surface, passes the same policies and lands in the same audit trail and memory.
Agents that do real jobs, each under policy.
Governed Claude / Codex harnesses in the terminal, the IDE, and CI, plan, edit, test, and ship under capability policy.
Prompt-to-production or dock what you already run. Build → test → release with rollback armed, approval-gated.
Tickets and events land in one inbox; agents run root-cause analysis, propose remediation, and fix, humans approve.
OpenTelemetry logs, traces, metrics, and session replay from every app and runtime, no vendor wiring.
Operate VMs, containers, and clusters from one surface, restarts and host actions gated and audited.
Training runs, GPU provisioning, and experiment tracking, the same platform runs our own R&D stack.
Authority comes from policy, never from whoever is driving.
Electricity only became infrastructure once it shipped with breakers, meters, and grounding. Code that reshapes itself needs the same grid. This is it, not paperwork.
- +Two planes. Human identity (SSO, roles) is separate from agent capability (policies). No one quietly escalates an agent's authority.
- +Capability ladder. observer → agent → developer → remediator → admin. Risky actions stop at approval gates on every surface.
- +Software governance. What agents install is checked against allowed registries, publishers, and licensing policy.
- +Audit by construction. Every capability check, approval, and denial recorded with actor, target, scope, timestamp.
The corporate full stack for R&D and operations.
→ Live console of a production stack, not mockups. A full walkthrough is part of the pilot.
A platform that remembers how your company works.
- +Capture. Every governed session, threads, coding runs, tickets, distills into a private knowledge graph on your stack.
- +Consolidate. A gardener agent merges duplicates, links decisions to incidents, and mines repeated work into reusable skills.
- +Recall. Hybrid lexical + semantic + graph search from Nova, the CLI, or Slack, every answer carries its sources.
Institutional knowledge stops leaving when people do. It compounds.
A thread is a governed session.
- +Slack, Telegram, Discord, wired up at signup; name your agent whatever you like
- +Grounded in your repos and memory, answers carry sources
- +Real deliverables in-thread: documents, diagrams, pull requests
- +Approvals and audit inside the conversation, same policy as every surface
The same model, run inside Cortex, is safer and more capable.
Our open Gauntlet benchmark runs an identical frontier model twice, raw, then inside Cortex's governance, across five kinds of real software work. Every run is reproducible.
Paper: “Cortex: A Fixed-Point Theory of Governed Coding Agents”. Dinu & Zeba, Alpha Omega Labs, 2026 · results + reruns public
The evidence your auditors ask for, as a by-product of operation.
Honest note: no platform makes you compliant by itself. Cortex makes the evidence fall out of normal operation, we work alongside your compliance team.
Everyone sells agents. Almost no one owns the operation.
✓ native · ~ partial / add-on / do-it-yourself · × absent ¹ ChatGPT Enterprise, Microsoft Copilot, Gemini Enterprise (¹ᵃ Google offers on-prem via Distributed Cloud) ² Devin, LangGraph, CrewAI ³ Aleph Alpha, Mistral
Assessment from public vendor documentation, July 2026 · full per-vendor notes and URLs: pitch/research/competitors.md
Deep governance and full delivery ownership, the quadrant is empty up there.
- +Assistants & copilots stop at suggestions, you still own integration, deployment, and evidence.
- +Frameworks hand you parts, governance, audit, and operations become your engineering project.
- +Sovereign model vendors solve residency for the model, not the agents, memory, or delivery around it.
- +Cortex ships the whole governed operation: agents, approvals, audit, memory, deploys, observability, cloud or on-premise.
Governed delivery is the wedge. The research loop is the reason.
Same control plane, same policies, same audit trail, extended until a question becomes a run, a run becomes evidence, and evidence becomes a capability you keep. omegaXiv and Quarks are current prototypes.
Problem intake, private or public work, papers, sources, code, artifacts, and reruns.
Contract-validated phases, experiments, simulations, traces, and publication packages.
Equations, contracts, effects, assurance, and replay in one AI-native language.
Governed agents, memory, code, deployment, observability, approvals, and audit.
omegaXiv makes research legible. Quarks makes it executable.
Structured problem intake; autonomous, semi-autonomous, or manual work; private workspaces and public discovery; papers, review, artifacts, and reruns.
A manifest-driven, contract-validated phase pipeline for experiments and simulations, with provenance, structured traces, and Git-ready result packages.
alphaomega-docs/quarks-docs/deepwiki.md · alphaomega-docs/quarks-docs/service-api.md21
When “it ran” is not enough, make the model and its evidence executable.
equation raw_additive_hybrid_value(symbolic_value: f64, learned_correction: f64, gate: f64) -> f64: return symbolic_value + gate * learned_correction def additive_hybrid_value(symbolic_value: f64, learned_correction: f64, gate: f64) !{}: require gate >= 0.0 and gate <= 1.0 return raw_additive_hybrid_value(symbolic_value, learned_correction, gate)
Symbolic and numerical model surfaces are language constructs, not prompt conventions.
Bounds, invariants, and evidence gates execute with the program.
Capabilities are typed, making the Cortex policy boundary explicit when the systems are integrated.
Tests, traces, and deterministic inspection stay attached to the run.
Python-shaped syntax, backed by a Rust reference runtime. Other languages remain supported; Sema is for workflows where the proof boundary matters.
Prototype: one program carrying identity and evidence across scientific scales.
Source-bound structures, simulator adapters, state transitions, and replay stay linked as the view changes scale.
Derived geometry and unvalidated science are labelled explicitly. Technical execution is not clinical or scientific admission.
The same evidence discipline can move from simulation into development.
Demonstrated technical prototypes: reduced/surrogate simulation, assumption-labelled reconstruction, and an illustrative state-space proxy, not OEM CAD, governing CFD, robot or plant physics, homologation, clinical, or safety authority.
One platform license. Clear ownership. Specialist work when needed.
Validate with a fixed outcome, operate Cortex under an annual license, then extend into full R&D without replacing the control plane.
- Dedicated governed pilot environment
- One bounded priority use case
- Acceptance, security & KPI baseline
- Cortex + dedicated deployment
- Updates, operations & standard support
- Cloud, model and compute usage separate
- Full Cortex + omegaXiv and Quarks prototypes + Sema toolchain
- Joint roadmap + priority capacity
- Deliverables, SLA and usage set in SOW
Pre-approved cap or fixed-scope work package for integrations, models, simulations and custom SLAs.
Contractual IP split
One Cortex product. Three hosting models.
Choose where Cortex runs, inside your perimeter, in a dedicated private cloud, or Alpha Omega-hosted. Independently, use local models, configured public APIs, or both; hosting, support, and usage terms vary.
Fully on-premises
- Existing VMs, Docker hosts or Kubernetes
- Identity, memory, observability and transcription stay local
- Outbound model calls only when configured
Private cloud
- Isolated deployment and data boundary
- Customer-owned cloud tenancy or Alpha Omega hosting
- Managed updates and operations available
Hosted / public API services
- We host and operate the Cortex control plane
- Connect configured public APIs, private models or both
- Requests sent to public providers leave the Cortex boundary and follow their residency, processing and usage terms
You supply the engines. We supply the layer that lets enterprises run them.
- +We ship no engine and no model. Cortex is engine-agnostic by construction. Simulators, solvers, provers, and frontier models attach as governed connectors.
- +The missing piece is the operating layer. Who may launch a run, against which data, under what budget, and what record survives it. Today that is bespoke glue rebuilt per customer, and inadequate risk controls are among the reasons Gartner expects >40% of agentic AI projects to be cancelled by 2027 (slide 04).
- +Every environment is demand. Each governed workspace provisioned is a new, auditable reason to buy compute: capex converted into utilization with a hypothesis attached.
- +We reach where platforms structurally cannot. Inside the customer perimeter, under EU AI Act obligations, fully on-premise as the same product, not a downgraded tier.
Physics, robotics, molecular, quantum, and frontier-model stacks, from the partners who already own them.
Isolation, authority, budget, provenance, residency. Rebuilt by hand at every enterprise, or the project stalls.
Provision the environment, broker its reach under policy, audit the run, keep the result. One substrate, many engines.
→ The world will be simulated before it is built. We are building where those simulations run.
Value from week one.
- +Fast path to a live stack, provisioned with Alpha Omega support, on our cloud or your perimeter.
- +No replatforming, connect the VMs, Docker hosts, and clusters you already run.
- +One governed surface, coding, deploys, support, monitoring, DevOps under one policy and one audit trail.
- +Evidence by construction. AI-Act-aligned records produced by normal operation.
- +Compounding memory, your organization's knowledge stays, links, and grows.
Validate, then commit.
Pick one priority workflow (e.g. support RCA or governed coding). Security & compliance walkthrough on a live stack, bring your compliance team.
Complete the 6–12 week engagement on a dedicated stack. Real workflows, deploys, and audit trails, measured against your KPIs.
Extend policies, channels, and memory across teams. Enterprise plan with multi-node topology, delegation, and SLAs.
● a2olabs.com · dashboard.cortex.a2olabs.com/cloud