Aberspace Engineering

AI is being deployed. Nobody is governing it. fixes that.

Governed intelligence infrastructure for institutions that cannot afford to get AI wrong.

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— request— allowed- - cut by policy

Illustrative traffic · every thread passes through Atlas

Choose your environment

The environments that hold the most valuable data cannot safely use most AI systems.

Pick the one that looks like yours. Hover to preview, click to enter — the rest of the page becomes about you.

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Strict access controlRegulatory complianceData sovereigntyFull auditability

Traditional AI platforms assume cloud access and unrestricted data flow. That assumption breaks the moment sensitive data is involved.

Tier 1Intelligence= Metadata

Tier 2Knowledge= Compliance

Tier 3Wisdom= RAG

Tier 1Intelligence= Metadata

Know what you have. Without moving it.

Atlas maps every data source, AI tool and workflow in your environment and classifies content at ingest — on metadata, in place. Nothing leaves.

Data detection & classificationAutomatically identify PII, PHI, PCI, CUI, and compliance exposure across datasets at ingest time. Custom taxonomies and regulatory frameworks.

Risk visibilityDataset-level heatmaps and scoring across collections and systems. Understand exposure before it becomes a compliance event.

keep scrolling · the file flips to its Data Facts

Tier 2Knowledge= Compliance

Decide who may touch it. Every time.

Tags, roles and project context become declarative policy. Atlas evaluates it on every request — no cached decisions, fail-closed — and writes an immutable audit event for each one.

Policy enforcementOPA-based control over access and behavior. Policies are evaluated at query time, not configured once and forgotten.

Full observabilityTrack every query, retrieval, and response with complete provenance. Know who asked what, what data was used, and what the model returned.

Audit trail · simulated

Tier 3Wisdom= RAG

Ask the hard questions. Inside the lines.

People and agents query through the governed interface. Retrieval is scoped by tag and role before the vector search runs, redaction applies per classification, and the model runs where the data lives.

Governed retrieval & agentsControl how AI agents access and use data through governed RAG pipelines. Every retrieval filtered through policy before reaching the model.

Metadata-driven inferenceRun AI on structured and archived data without exposing raw datasets. Operate on metadata and classification outputs — not the data itself.

Engagement hierarchy

Atlas enables three tiers of AI engagement.

Building a foundation for trusted intelligence.

WISDOM= RAGanswers you can defend
KNOWLEDGE= Compliancecontrols you can prove
INTELLIGENCE= Metadataan inventory you can trust

How it works

A governed layer between your teams and your AI.

Ingest

Data enters Atlas. Sensitive content is detected and classified automatically.

Tag & Map

Data, users, and models are linked via tags. Policies define who can access what.

Enforce

Every request is evaluated against live policies before reaching the model.

Prompt

Users query AI through the governed chat interface. Access is scoped by tag and role.

Audit

Every action is logged — query, retrieval, response — with complete provenance.

Result: AI that is observable, controllable, and accountable — by design.

How Atlas fits

Atlas is the system that enforces this.

Atlas sits between your data and your models, determining what can be accessed, how it is used, and what is returned. It turns AI systems from open-ended tools into controlled, auditable infrastructure.

request lifecycleend-to-end policy enforcement

User / Agentauthenticated · OIDC / SAML

Atlas GatewaymTLS · tenant · scope

Policy EngineOPA rules · every request

Governed Retrievalapproved collections only

Model Inferencelocal · template · tool gating

Audit Logimmutable · full provenance

Policy evaluationEvery request

OPA rules checked at query time. No caching of authorization decisions across requests.

Data classificationOn ingest

PII, PHI, PCI and CUI detected automatically; classification metadata follows data through the pipeline.

Audit loggingFull provenance

User, query, retrieval, response. Append-only, tamper-evident, exportable as JSON-lines or CEF.

on-premise0%No data leaves your infrastructure

policy evaluationQuery timeEvery request, no cached decisions

request auditedEveryFull provenance from query to response

data egressZeroModels run where sensitive data lives

Products

Built by Aberspace. Starting with Atlas.

We build the infrastructure that lets organizations deploy and govern AI inside their own environment — securely, privately, and in full compliance.

Governed intelligence middleware.

The middleware layer that sits between your teams and your AI — enforcing policy, logging every action, and ensuring nothing reaches a model that shouldn't.

Full visibilityMap every AI tool, workflow, and data source across your institution in real time.

Policy enforcementOPA-based controls evaluated dynamically at query time — not set once and ignored.

Sensitive data detectionAutomatically identifies PII, PHI, PCI, and CUI at ingestion with custom taxonomy support.

Complete audit trailEvery query, retrieval, and model response logged with full provenance and context.

Explore Atlas Read the architecture

Next from Aberspace

Reserved. The next product in the governed-intelligence stack lands here.

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Users & Agents

ATLAS

Policy Engine

Sources & LLMs

Data ingestion & classification

◈ Audit trail0 events

For your role · Defense & DIB

Built for every stakeholder in the AI decision chain.

Different stakeholders face different pressures when AI meets sensitive data. Atlas gives each role the specific controls they need.

All ten roles, by industry, on the Solutions page

Who we are

We saw the gap.

The organizations with the most to gain from AI are the ones that can't safely use it. Their data is too sensitive. Their compliance obligations too strict. The cost of failure too high.

So we built the infrastructure to change that.

Design philosophyEngineering-firstWorking systems over promises.

Every claim we make is backed by running code. Our documentation is our product spec. We ship what we describe.

Design philosophySecurity by designArchitecture-time, not deployment-time.

Governance is the foundation we build on. Security decisions happen at architecture time. Not as an afterthought.

Design philosophyDepth over breadthOne hard problem, solved completely.

We go deep on making AI safe to operate inside environments that cannot afford failure. Nothing else.

Design philosophyOperator mindsetBuilt for 2am reliability.

We build for the people who run these systems under pressure. Clear failure modes. Full observability. No surprises.

These beliefs drive three engineering principles

01AI runs where the data lives

Atlas deploys inside your infrastructure so models operate on data in place — no egress, no third-party exposure.

02Governance is enforced by the system

Every query passes through access control, classification checks, and scope constraints before any data is retrieved.

03Every interaction is observable

Atlas produces immutable audit trails for every query, retrieval, and inference decision. Full provenance, always.

Where it runs

Deploy where the data lives.

Atlas integrates with modern AI infrastructure and deploys entirely within your environment. Policy enforcement applies at every integration boundary.

Topology 01On-premisesBare metal and VMware deployments with full hardware control.

Topology 02Private cloudOpenStack, private VPC, managed Kubernetes clusters.

Topology 03HPC clustersSlurm-managed GPU clusters and research compute grids.

Topology 04Air-gappedFully disconnected environments with no external network path.

LLM inferencevLLM · TGI · Triton · llama.cpp

Vector databasesQdrant · Milvus · Weaviate · pgvector

Storage systemsS3-compatible · NFS · HDFS · Ceph · Starfish

Identity providersLDAP · SAML · OIDC · Kerberos

DrawingATLAS-GOV-01

Sheet01 / 05 · DEFENSE

Rev2026.10

Full architecture: layers, request lifecycle, policy model, audit schema

Why now · Defense & DIB

The window to get ahead of this is closing.

The case study

Start small. See results fast.

One team. One workflow. One measurable outcome. That is all this pilot asks.

AI governance experiment

AI usage map across your institution
Risk assessment — data, compliance, bias
Governance framework and policy templates
30–60 day implementation roadmap
Access to Atlas for one team or workflow

Timeline

Weeks 1–2Setup & onboarding

Weeks 3–5Experiment trials

Week 6Results & readout

Effort required: one or two kickoff meetings and a small test dataset. Easy to start. Easy to stop.

Talk to us

Talk to us about your environment.

Every deployment is different. We scope Atlas to your infrastructure, your data, and your compliance requirements.

Request a demo Launch a case study