Context layer for high-stakes AI
AI your organization can actually stand behind.
In high-stakes environments, an unverifiable output isn't a UX problem. It's a liability. Andromeda is the context graph infrastructure that makes every answer traceable, defensible, and accountable.
The problem
The cost of getting it wrong.
In maintenance
A technician follows an AI-generated procedure. The part is wrong. The machine goes down. The warranty claim is filed.
In defense
An analyst acts on an AI output. The source is unverifiable. The decision can't be walked back.
In compliance
An AI system recommends a regulatory position. The auditor asks how you got there. Nobody has a clean answer.
The LLM wave generated capability. It didn't solve accountability. That's the gap Andromeda closes.
What Andromeda is
Andromeda is the context layer for high-stakes AI.
It's context graph infrastructure that makes every output traceable, every reasoning chain visible, and every answer defensible.
It sits between your organization's knowledge and your AI. Not a search engine. Not another RAG wrapper. The structured foundation that makes AI deployable in environments that can't afford to be wrong.
Capabilities
Built for accountability, not just capability.
+GROUND
Grounded in your domain
Your industry's rules, relationships, and constraints, encoded as structure rather than retrieved from general training data.
+PROVE
Every answer cites its source
Not a probability score. A reconstructable chain from question to entity to relationship to conclusion.
[ Trace this answer ]
The question
This failure is 30 days out of warranty but matches a known defect pattern. Covered defect, or misuse?
The answer
Covered. The unit falls inside the defect campaign window under the policy in force at sale.
What the answer rests on
UNIT · DEFECT-CAMPAIGN · WARRANTY-POLICY
UNIT — SOLD-UNDER → POLICY REV.4Two sources report different warranty start dates, 41 days apart.
- SRC-ADealer registrationGoverning
Delivery to end customer recorded, with the registration date as the warranty start.
- SRC-BShipping manifestSuperseded
Unit released from the distribution center, with the ship date recorded against the same unit.
- SRC-CWarranty policy, rev in force at saleControlling
Coverage begins at delivery to the end customer, not at shipment. §4.2.
What was recorded
The determination, the policy version it was made under, both sources, and the reason one was set aside.
You just reconstructed an answer. So can an auditor.
Illustrative example. Not customer data.
+DEFEND
Defensible to anyone who needs to sign off
Legal, compliance, auditors, regulators. When they ask how you got that answer, you can show them.
+DEPLOY
Deployable on your security terms
Cloud, on-prem, air-gapped, or hybrid. The same platform, configured to your posture.
Two ways to deploy
One context layer. Two ways to deploy it.
Enterprise Platform
The full context layer: custom ontology, LLM grounding, source-traced reasoning, and a full audit trail, deployed at infrastructure scale.
For organizations with complex documentation, distributed knowledge, and real accountability requirements.
Applied AI Services
Expert-led engagements that surface the accountability gaps before you invest in infrastructure. Every engagement is a qualified platform deployment in development.
For organizations that need expert delivery before infrastructure commitment.
Deployment
The same context layer. Wherever your security requirements live.
- Multi-tenant SaaS
- Dedicated Cloud
- On-Premises
- Air-gapped / Edge
In production
10,000+
users
A Fortune 10 OEM runs Andromeda across technical documentation, service records, and warranty history. Every phase expanded scope, because the context layer was already in place.
Your environment demands accountability. Your AI should too.
Start with the workflow where an indefensible answer costs the most. Measure what changes. Expand from there.