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The platform

Context graph infrastructure. Built for accountability, not just capability.

Most AI infrastructure treats accountability as an output: citations bolted on, audit logs appended. Andromeda treats it as an input. The context layer is encoded before the model operates.

Five layers. One architecture.

01

Data connectivity

Your data stays where it lives. Connect to existing sources like SharePoint, S3, SAP, Jira, databases, and REST APIs without migration or duplication.

02

This is where other platforms stop.

Domain intelligence

Andromeda extracts entities, relationships, and domain rules specific to your operation and encodes them as a structured ontology. Not a general schema. A precise map of how your domain actually works.

03

Core platform

The context graph engine. Ingestion, entity extraction, relationship modeling, graph construction, retrieval, and audit. Open, swappable infrastructure throughout. No vendor lock-in.

Graph store · Vector store · Search index · Message broker · Workflow engine

04

AI / LLM services

LLMs in Andromeda don't get general knowledge. They get your knowledge: structured, verified, and scoped to your domain. Model-agnostic. Swap LLMs without re-engineering the context layer.

05

Workflow surfaces

Chat, structured query, workflow-embedded, API, and agentic orchestration. Human users in production today. Agent-ready as that capability matures, grounded in the same verified context graph.

Deployment posture and certificationsSecurity

RAG retrieves. Andromeda reasons.

Generic RAG
Andromeda
Mechanism
Generic RAGVector similarity
AndromedaGraph traversal across typed relationships
Answer quality
Generic RAGStatistically likely
AndromedaConstrained by the structure it traverses
Auditability
Generic RAGSource citation
AndromedaThe full chain that produced the answer
Domain knowledge
Generic RAGGeneral inference
AndromedaAn ontology verified against your domain
Failure mode
Generic RAGConfident and wrong
AndromedaExplicit about what it does not know
Stakes tolerance
Generic RAGBrowse and discover
AndromedaOutputs that have to be defended

A context graph built for capability gets you impressive demos. One built for accountability gets you production deployments in regulated environments.

Proof is built in, not bolted on.

Andromeda ships with a built-in evaluation framework that runs at every stage of the build, not as a check at the end.

You know the context layer is working before it touches production. And you can prove it to the people who need to sign off.

Before the build

SME validation

Expert-verified ground truth, established before anything runs

During the build

Accuracy scoring

A multi-dimensional rubric, not pass/fail

Before deployment

Adversarial testing

Red-team your own domain before a user can

After deployment

Regression gating

Rebuilt graphs re-answer a frozen question set before shipping

Read the architecture. Then test it.

Bring the question your current tooling gets wrong. We'll run the session against your documentation and show the full chain from question to source.