Company

AI moved from prototype
to production.
The infrastructure did not.

ElectriPy AI was created to solve the operational gap between AI demos and production AI systems.

01 — The Pattern

Every project starts the same way.

Every AI project starts with prompts, models, agents, workflows, and retrieval. The prototype works. The demo looks impressive. Then production introduces a different class of problems.

Reliability
Observability
Governance
Evaluation
Auditability
Runtime control
Operational visibility
Policy enforcement
Cost attribution

Most teams do not discover these needs until after they have already shipped.

02 — The Runtime Gap

Where frameworks stop.

Frameworks help teams build AI capabilities. They do not always provide the infrastructure required to operate those capabilities safely, reliably, and repeatedly in production.

That gap is where ElectriPy AI lives.

Not a replacement for frameworks. Not another orchestration layer. The runtime infrastructure that production AI systems need but frameworks don't provide.

03 — The LSAS Insight

Architecture before implementation.

LSAS — the Layered Safety & Abstraction Stack — defines the layers production AI systems eventually need: application, orchestration, memory, knowledge, tools, models, reliability, observability, and governance.

LSAS

The architecture. Nine layers of responsibility for production AI systems with clear contracts and boundaries.

ElectriPy AI

The runtime implementation. Open source Python packages that implement LSAS as composable, installable primitives.

Explore the LSAS Architecture

04 — Open Source First

The runtime belongs close to the application.

That is why ElectriPy AI starts as open source. Developers should be able to compose reliability, observability, governance, evaluation, routing, MCP integration, and runtime primitives directly into their systems.

No vendor lock-in. No mandatory cloud dependency. No feature gating. The full runtime stack is MIT licensed and always will be.

View on GitHub

Founder Note

“Every serious AI system eventually needs runtime infrastructure. Most teams rebuild it from scratch. ElectriPy AI exists so they don't have to.”

Matt Vegas

Software architect and AI engineer with three decades of experience building enterprise systems, healthcare platforms, distributed applications, and production AI products. ElectriPy AI emerged from repeated exposure to the same operational gap across every serious AI project.

Ready to explore the platform?

Start with the layer your system needs most. The architecture scales with you.