Architecture / AIGIS resources
Enterprise work is moving out of single-vendor application screens and into chat, workflows, internal tools, and model interfaces. AIGIS gives that headless enterprise one governed layer before requests touch systems or models.
Executive read
Headless enterprise AI means users can work from Slack, Teams, Claude, OpenAI, Salesforce LWC, or other approved surfaces.
AIGIS sits between those surfaces and systems of record, recreating identity and permission models before execution.
Not every request needs an LLM call; governed cache, APIs, SQL, and workflows can answer many requests directly.
Model choice becomes a routing decision instead of a platform lock-in decision.
Analysis
The headless enterprise separates the work surface from the system of record. A user may ask in Slack, review in Salesforce LWC, approve in Teams, or use Claude as the interface.
Without a control plane, every surface becomes a separate governance project. AIGIS gives those surfaces the same runtime enforcement path.
AIGIS maps the user into each connected system, checks object, field, and record access, strips inaccessible fields, and logs provenance before context is assembled.
The governed router can then choose the safest execution path: cache, live API, SQL, workflow, human approval, small model, frontier model, or customer-hosted model.
When every question goes straight to a premium model, cost becomes a tax on curiosity. When governance can route known, structured, or workflow-backed requests without an LLM call, model spend becomes targeted.
Because the governance layer is separate from the model layer, enterprises can change LLM vendors as quality, cost, capacity, and policy requirements change.
Comparison
Keep reading
Architecture
A practical architecture for enterprise AI governance middleware: identity resolution, live record checks, field stripping, model routing, write approvals, and audit provenance.
Use case
AIGIS routes questions across Salesforce, SAP, ServiceNow, databases, and warehouses while enforcing each system's native permissions.
Architecture
Why LLM field-level security should remove inaccessible fields before prompt construction instead of sending masked placeholders to the model.