EnerGaze Private deployment

Causal forecasting, delivered inside your AWS.

Agentic pipelines that prove program impact and forecasting ability with audit-grade confidence.

EcoMetricx deploys a full-stack, AWS-native causal forecasting pipeline into your infrastructure. We combine staggered-adoption DiD, hierarchical reconciliation, scenario simulation, and LLM-assisted QA to deliver defensible forecasts, faster program insights, and operational automation without data leaving your cloud.

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End-to-end Cloud-native orchestration

Causal + Predictive DiD, baseline, and scenarios

Single-tenant Deployed in your account

92 Signal confidence

Why utilities adopt this

Quantify savings with cohort-based DiD
Forecast load with hierarchical reconciliation
Automate QA, reporting, and approvals
Retain complete audit lineage in S3

Outcome-oriented delivery

We do not provide a generic code drop. We partner to deploy, calibrate, and operate the pipeline so it aligns with your regulatory requirements, data contract, and planning cycle.

Feature-dense by design

Every component is purpose-built for energy program analytics, reliability planning, and decision automation.

Staggered-adoption DiD

Estimate ATT(g,t) with not-yet-treated controls and bootstrap uncertainty for defensible impact claims.

Chronos 2 + XGBoost fallback

Zero-shot global baseline forecasts with robust fallback when Chronos is unavailable.

Hierarchical reconciliation

Bottom-up forecasts are reconciled to system totals with proportional scaling and optional MinT extensions.

Probabilistic scenarios

Monte Carlo sampling produces quantiles and risk-aware planning distributions.

LLM QA + policy gate

LLM summaries are validated by deterministic thresholds to prevent unsafe actions.

Automated monitoring

MAPE/WAPE drift signals trigger retraining and approval workflows.

Audit-grade artifacts

Every run produces complete lineage in S3 for compliance and audit review.

Event-driven orchestration

Step Functions, EventBridge, and Lambda coordinate the pipeline at scale.

Secure by default

Runs inside your AWS account with least-privilege IAM policies.

Human approval gates

Critical findings are escalated to SNS for manual oversight.

Domain-tuned reporting

LLM narrative reports translate metrics into executive-ready insights.

Deployment-ready code

Idempotent CLI scripts stand up the entire stack in minutes.

TIme-Series and Panel Data

Platform-ready for time-series forecasting and panel data predictions

Nowcasting

Improved forecasting current conditions

Architecture snapshot

Composable AWS primitives with end-to-end orchestration and clear operational boundaries.

Ingest + QA

Validate curated panel coverage and data integrity before compute.

Estimate + Train

Run DiD effects and baseline training in parallel for speed.

Forecast + Reconcile

Apply effects, generate scenarios, and reconcile hierarchies.

Report + Monitor

Deliver narratives, metrics, and policy-governed decisions.

Compute Layer

SageMaker processing and training jobs handle all heavy model workloads.

Orchestration

Step Functions coordinates parallelism, retries, and deterministic approvals.

Storage

S3 holds the curated panel and immutable run artifacts for auditability.

Governance

LLM output is constrained by policy gates and explicit allowlists.

Designed for measurable outcomes

Bring planning, regulatory, and program teams onto one operational platform.

Lower forecast error

Blend causal impacts with baseline projections to reduce blind spots in demand planning.

Faster impact validation

Automated DiD surfaces provide continuous insight into program efficacy.

Decision-ready reporting

LLM narratives are bound to audit artifacts for consistent stakeholder communication.

Operational resilience

Policy gates and human approvals keep sensitive decisions in the right hands.

Security and governance built-in

We deploy inside your AWS account with strict boundaries and traceability.

Single-tenant deployment

Your data never leaves your AWS environment. No multi-tenant pooling.

Privacy-by-design

Guardrails enforce minimal data exposure and policy-driven access.

Least-privilege IAM

Role-scoped access for Lambda, SageMaker, Step Functions, and EventBridge.

Encrypted storage

S3 encryption defaults to SSE-S3 with optional KMS support.

Audit lineage

Every run produces immutable artifacts with timestamps and traceable inputs.

How we deliver

This is not a self-serve codebase. We partner with you to deploy, calibrate, and operate.

Discovery and data contract

Validate data availability, treatment definitions, and planning constraints.

Infrastructure deployment

Provision AWS resources, networking, and IAM policy boundaries.

Calibration and validation

Tune thresholds, hierarchies, and scenarios with your domain experts.

Operational handoff

Enable monitoring, reporting, and a shared operations playbook.

FAQ

Is this an open-source project?

No. EcoMetricx deploys and operates the pipeline as a managed engagement in your infrastructure.

Where does our data live?

All data stays in your AWS account, in buckets you control.

Can we use our own models?

Yes. The pipeline is modular and can accommodate custom forecasting or causal components.

How long does deployment take?

Initial deployment can be completed in days, with calibration and validation following.

Ready to deploy a causal forecasting pipeline in your AWS?

We will scope the data contract, deploy the infrastructure, and deliver a production-ready forecasting service.

Contact EcoMetricx