Nova Energy:
Large Load Workflow Engine

Accelerating grid interconnection approvals for AI data centers through a deterministic rules engine and automated evaluation workflows, delivering audit-grade decision memos.

LIVE DEMO
tx-large-load-v0 — bash — 80x24
[18:01:33]Initializing tx-large-load-v0 engine...
[18:01:33]Loading process graph: process_graph.yaml (v0.1)
[18:01:33]Graph loaded: 14 nodes, 22 edges
[18:01:33]Ingesting intake request: REQ-2026-0042
_

The Interconnection Complexity

For AI data centers and large flexible loads, interconnection is not a static request. Any input reconfiguration fundamentally alters the required regulatory path and compliance rules. Traditionally, developers navigate this complex web manually, risking fatal delays. Today, the Nova tx-large-load-v0 engine replaces guesswork with deterministic code. It instantly evaluates your project's request schema against the ERCOT process graph, calculating exact regulatory outcomes in seconds.

Dynamic Input Evaluation

The engine's Deterministic Traversal algorithm evaluates new parameters through the YAML process graph, immediately surfacing the exact rules and edges that are satisfied.

Audit-Grade Provenance

Every Decision Memo includes precise rule_id, doc_id, and local PDF sha256 hash validations, providing investment committees with absolute proof of compliance.

Automated Readiness

The platform automatically checks project parameters against ERCOT Planning Guide citations, generating a compact, tri-state checklist (Satisfied / Missing / Not Satisfied).

CURRENT CAPABILITIES

v0 Engine Architecture

Our deterministic rules engine transforms complex regulatory frameworks into automated, audit-grade workflows.

MEMO-2026-0042-A.htmlGENERATED

Project: CyrusOne Bosque 400MW

Status: Material Change Risk Detected

Rule ID: LL_QA_006_MATERIAL_CHANGE

Trigger: Timeline Accelerated > 15%

// Evidence Hash

sha256:e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855

Automated Decision Memos

The system ingests project parameters and automatically generates HTML memos containing full compliance evidence. Every decision is tagged with specific Rule IDs and document hashes, creating an immutable audit trail for regulatory filings.

Deterministic Traversal

Our engine evaluates edge criteria based on a strict YAML process graph. The "first satisfied edge wins" logic ensures consistent, reproducible outcomes for every interconnection request, eliminating human error in complex routing.

Node N2
E2P
Node N3P
if load_mw_total > 75 and plan == "phased":
  route_to(N3P)
else:
  check_next_edge()
Planning Guide Sec 9.2.4
SATISFIED
Reactive Power Capability
MISSING DATA
Telemetry Verification
NOT SATISFIED

Energization Readiness

The engine automatically aligns project status with the ERCOT Planning Guide. Our tri-state checklist (Satisfied / Missing / Not Satisfied) provides instant visibility into what's blocking energization, preventing costly last-minute surprises.

Citation Integrity

We maintain a strict mapping between published rule locations and local PDF anchors. Every claim in our decision memos is backed by a direct link to the source document, ensuring 100% traceability and confidence in compliance.

PDF

ERCOT_PLANNING_GUIDE.pdf

Page 42 • Section 9.2.1

"Interconnection requests for Large Loads must include a Load Commissioning Plan..."
Anchor: #pg9-2-1
TECHNOLOGY ROADMAP

Advanced Modules

Beyond the v0 engine, we are developing next-generation capabilities to optimize grid interaction and maximize available capacity for AI workloads.

IN DEVELOPMENT
Dynamic Envelopes

Future versions will support real-time responses to grid constraints via dynamic envelope technology, allowing loads to maximize consumption during non-constrained intervals.

IN DEVELOPMENT
Intelligent BESS

When grid capacity falls short, our platform will model intelligent Battery Energy Storage Systems (BESS) integration to provide physical mitigation and optimize capital efficiency.

IN DEVELOPMENT
Load Smoothing

Advanced capability to forecast and smooth the load profile for high-energy AI data centers, reducing peak demand charges and improving grid stability compliance.