🚀 Ingest Smart Meter HES and SCADA DMS tables to S3 Iceberg instantly. Learn More ➔
EnergyTrust Protocol (ETP) · Verifiable Telemetry Lakehouse

Telemetry you can prove.

A utility can prove a reading was authentic at the meter, and separately that a warehouse row hasn't changed. Nothing joins the two — so substantiating a settlement figure to a regulator takes days of manual reconciliation. meldra verifies smart grid telemetry at the ingestion boundary and writes cryptographic proof into an open Apache Iceberg lakehouse. Every query returns its figure, its snapshot ID, and its verification together.

Measured: 9,254 verified telemetry blocks/sec (single thread) · ECDSA P-256 + SHA-256 + Nonce CAS

ETP Boundary Verification

Cryptographically verify smart meter interval readings at the network boundary using ECDSA P-256 keys, HMAC-SHA256 signatures, and sequence nonces before materializing to Iceberg.

Cryptographic Deception

Silently divert replayed nonces or tampered grid streams into Phantom Grid honeypots for forensic analysis while maintaining zero corruption in your primary Iceberg tables.

Siemens & Schneider ADMS Ingest

Native zero-copy ingress connectors for Siemens Spectrum Power ADMS, Schneider Electric EcoStruxure, Itron AMI HES, Landis+Gyr MDM, and SCADA telemetry.

Grid Topology Graph

Project tabular Iceberg telemetry directly into directional graph vertices and edges using openCypher traversals to map substation-to-feeder load capacity.

Architecture

Verifiable Telemetry Pipeline

Your smart grid telemetry is verified at ingestion and written directly to your S3 bucket in open Apache Iceberg format — queryable instantly with zero copy.

📥
STEP 1

Ingest Grid Telemetry

Ingest smart meter interval readings, feeder loads, and SCADA switch states from Siemens, Schneider, Itron, or Landis+Gyr.

🔒
STEP 2

ETP Cryptographic Verification

Validate ECDSA hardware keys and nonces. Tampered streams are diverted to Phantom Grid honeypots.

🗄️
STEP 3

Open Iceberg on S3

Verified rows are committed to Apache Iceberg format in your cloud S3 bucket with full ACID snapshot history.

STEP 4

Auditable Queries

Query via serverless DuckDB SQL or openCypher graph. Every query result returns its data, snapshot ID, and proof.

Grid & Telemetry Standards

⚡ Siemens Spectrum ADMS 🔌 Schneider EcoStruxure 📡 Itron AMI Head-End (HES) 🔒 Landis+Gyr MDM 🛡️ ETP Telemetry Gateway 📊 IEC 61970/61968 CIM
Why Utilities Choose meldra.ai

Built for Energy Grids & Smart Metering

Prove telemetry integrity to regulators, prevent meter tampering, and query grid performance serverlessly.

9,254
Verified Blocks/sec
Single-thread ECDSA P-256 + SHA-256 CAS verification
Zero
Cluster Compute
Serverless DuckDB query scans over S3 Iceberg Parquet
0
Data Duplication
Data lives in open Apache Iceberg format in your S3 bucket
CIM
IEC 61970/61968
Native Common Information Model schema compatibility
The Business Unlock

Your Non-Technical Teams Get Answers — Today

Finance managers, HR directors, and operations leads no longer need to raise a ticket and wait 2 weeks for a SQL report. They simply ask meldra.ai in plain English.

💬
HR Director asks:
"Which departments had the highest headcount growth in Q3?"
→ Answer returned in 0.8 seconds
💬
Grid Operations Engineer asks:
"Show me all feeders in UK_NORTH zone exceeding 90% peak transformer capacity."
→ Pulls from HES & SCADA telemetry, returns table instantly
💬
Ops Manager asks:
"Which warehouse had the most stock discrepancies last month?"
→ Graph analysis complete in 1.2 seconds
Built for Every Industry

Any Business With Data Can Use This

You don't need a data engineering team. You don't need a warehouse. You just need your data and an S3 bucket.

🏦

Finance & Banking

Ingest GL journals, AP/AR ledgers, and trade data. Run compliance checks in plain English. Audit trails built-in.

Smart Meter HES · SCADA DMS · Feeder Telemetry
🛒

Retail & E-Commerce

Sync orders, inventory, and customer data from Shopify, WooCommerce, or any ERP. Query trends instantly without a BI tool.

Orders · Inventory · Customer segments · Returns
🏥

Healthcare & Life Sciences

Ingest smart meter interval readings, feeder loads, and SCADA switch states from Siemens, Schneider, Itron, or Landis+Gyr.

Smart Grid Telemetry · SCADA DMS · Feeder Load · CIM
🚚

Logistics & Supply Chain

Track shipments, warehouse stock levels, and supplier performance across millions of rows. Detect anomalies with AI graph analysis.

Shipments · Stock levels · Supplier KPIs · IoT
👥

HR & People Analytics

Analyse headcount, salary bands, hiring funnels, and attrition without a data team. Ask HR questions in plain English.

Headcount · Salary · Attrition · Hiring pipeline
🏗️

Manufacturing & OT

Ingest sensor readings, production line metrics, and quality control data. Run root-cause analysis with AI-powered graph traversal.

Sensors · OEE metrics · Quality control · MES data

Ready to build your own lakehouse?

30-day free trial. No credit card. Your S3 bucket. Your data. Zero lock-in.

meldra Academy

Learn how the Zero-Copy Lakehouse works, watch technical demonstrations, and discover the business challenges we solve.

8:42
Introduction
What is meldra.ai & the Zero-Copy Lakehouse?
A complete walkthrough of what meldra.ai is, why we built it, and how the Zero-Copy Lakehouse architecture works without replicating your data.
12:17
Business Case
Why Zero-Copy? The Business Problems We Solve
Learn the real business problems — ERP data silos, Spark cluster costs, and audit complexity — and how meldra resolves them without rewriting your stack.
10:05
Hands-On
Step-by-step: ingest a SAP ERP financial table into S3 Iceberg, build a knowledge graph, and run AI-powered SQL queries in under 10 minutes.

⚡ Sub-second SQL Queries Over S3

meldra.ai separates storage and compute. Your raw tables are stored in open Apache Iceberg format on your own S3 bucket, while our serverless DuckDB query engine runs analytical operations at memory speeds. No cluster warm-up delays, no idle-compute tax.

Open Metadata Catalogs: Iceberg tracking maps partition lists and excludes unneeded Parquet files automatically.
Zero-Copy Clones: Duplicate massive staging environments instantly without duplicating a single byte of S3 storage.
meldra-query.sql
-- Define open Iceberg table on AWS S3
CREATE TABLE s3.finance_ledger (
  entry_id VARCHAR,
  company_code VARCHAR,
  amount DECIMAL(15,2)
) USING ICEBERG
LOCATION 's3://meldra-lakehouse-warehouse/ledger';

-- High-speed serverless aggregation
SELECT company_code, SUM(amount)
FROM s3.finance_ledger
GROUP BY company_code;

Executing query...
[100% processed, 12.4M rows scanned in 0.08 seconds]
company_code total_amount
GRID_UK_NORTH $142,395,024.12
GRID_UK_SOUTH $98,104,235.80

📰 Architectural Insights & Blog

Read about enterprise grounding, serverless query performance, and the future of open table formats.

Enterprise AI

Enterprise AI Agents Grounded in Context

Why enterprise AI agents need structured context to deliver zero-hallucination, audit-ready answers — and how meldra.ai's Iceberg-native memory layer makes this possible at scale.

Data Engineering

DuckDB vs Spark for S3 Lakehouses

A comprehensive benchmark of serverless column pushdowns against distributed cluster runtimes for queries scanning under 50GB.

Architecture

Zero-Replication Architecture Deep Dive

Why copying tables across environments is a security and financial vulnerability. Map schemas to S3 files dynamically using Iceberg's metadata catalog.

🛠️ How to Create & Configure Your Workspace

Follow this step-by-step setup guide to provision a secure, zero-copy analytical pipeline for your database.

1

Register & Connect S3

Sign up for your free workspace, then navigate to Data Lake Config in the sidebar. Input your S3 Bucket URI (e.g. s3://my-lakehouse/warehouse) and AWS Role ARN credentials.

2

Configure Ingestion Tables

Under the Ingest tab, select your table sources (CSV uploads, PostgreSQL sync, or HES telemetry). meldra.ai reads the schemas and maps partition layout files on S3.

3

Ask AI & Track Progress

Ask our natural-language AI agent in the Chat tab to run S3 queries, detect transaction paths, or evolve table columns. Completed setup milestones are tracked in the Audit trail.

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Select your preferred method to register a new Apache Iceberg table in the catalog namespace.

Ingest via CSV Upload (Recommended)

Upload your dataset file, customize headers/schema formats, and commit files directly to your S3 bucket.

Create via SQL Prompt (DDL)

Open the interactive console and execute a standard CREATE TABLE SQL script to initialize table structures.

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Generate a long-lived token to connect external tools — including the real MCP (Model Context Protocol) server in backend/mcp_server.py — to your account. Shown once; store it somewhere safe.

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Demo mode — toggle to connect your own S3 warehouse.

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