How do you handle the OT security boundary?

Edge connectors run inside the OT DMZ on read-only interfaces. All upstream communication is outbound-only from the plant. We follow ISA/IEC 62443 and Purdue Model patterns.

Can the platform run on-prem?

Yes. Everything ships as Helm charts on Kubernetes with object storage backends. Fully on-prem, hybrid, and cloud deployments are all supported.

How long does it take to build an industrial IoT data platform?

It depends on how many plants and protocols the first release covers, the state of your historians and asset data, the OT network rules at each site, and how many analytics workloads go live first. We set the plan after a scoping call.

Can we start with a fixed-scope MVP?

Yes. The first release is scoped to one workflow, such as one line's machine data feeding one maintenance or quality dashboard, and later releases add plants and workloads.

How do you keep cloud costs predictable?

Through tiered storage, intelligent downsampling, partition strategies tuned to time-series patterns, and per-team cost observability.

How do you use AI in delivery without risking quality?

AI tools help our engineers draft code and tests, but a person reviews every change, and it passes the same automated tests and security checks as any other code. Which tools may see your code or data is agreed with you at the start.

Manufacturing / Industrial IoT Data Platform

Industrial IoT Data Platform

Plant data sits in PLCs, SCADA systems and historians that analytics teams can't easily reach. Agnotic Technologies builds the industrial IoT data platform that brings it into one governed store, so every factory project builds on the same data.

15-Minute Scoping Call

OPC UA + MQTT

Read-only connectors at the plant edge

Lakehouse

Open table formats on object storage

1 platform

For operations, quality, maintenance, and engineering

Trusted by global innovators

Industry Overview

What is an industrial IoT data platform?

An industrial IoT data platform is the shared system that collects machine and sensor data from PLCs, SCADA, historians and MES, adds asset and production context, and stores it where analytics, dashboards and machine learning can use it. Agnotic Technologies builds these platforms for manufacturers, alongside the OT systems they already run.

Industry Challenges

Why does factory data stay in silos?

Common problems we hear from manufacturing teams scoping a data platform.

OT/IT Divide: PLC and SCADA data live in historians that IT and data teams can't reach without specialized skills.
Data Silos per Initiative: Each smart factory project builds its own data pipeline, duplicating effort and creating inconsistencies.
Context Poverty: Raw tag values without asset context, production state, and quality metadata are nearly useless for analytics.
Scaling Cost: Naive lifts to cloud warehouses balloon cost when industrial time-series volumes hit production scale.

Our Approach

How do we build an industrial IoT data platform?

Engineering choices that respect the OT network and still give analysts usable data.

OT-Native Edge Connectors

Direct, performant integration with PLCs, SCADA, historians, and MES, without disrupting the OT network.

Contextualized Data Model

Raw tags enriched with asset hierarchy, production context, and quality metadata for immediate analytical value.

Multi-Workload Lakehouse

One platform that serves real-time dashboards, ML training, quality analytics, and long-term archival.

Capabilities

What goes into the platform?

Capabilities we build into industrial data platforms.

OT Connectors

OPC-UA, Modbus, EtherNet/IP, S7, and historian (OSIsoft PI, Wonderware) integrations out of the box.

Asset Data Model

ISA-95-inspired hierarchy linking tags to assets, lines, cells, and products.

Edge Processing

Filtering, downsampling, and feature extraction at the plant edge to control upstream bandwidth.

Time-Series Optimization

Columnar storage layouts and indexes tuned for industrial time-series query patterns.

Self-Service Analytics

Connect Tableau, Power BI, Grafana, or Jupyter to the same governed data layer.

Streaming & Batch

Real-time event streams and batch ETL coexist with consistent semantics.

Data Catalog & Lineage

Searchable catalog of every tag, asset, and derived metric with column-level lineage.

Access Controls

Row, column, and tag-based access aligned to your Active Directory or IAM.

Cost Observability

Per-team and per-workload cost attribution with proactive optimization alerts.

How It Works

How does plant data reach analytics?

From the plant edge to governed, business-ready tables.

OT Edge

Plant-side connectors pull from PLCs, SCADA, historians, and MES via secure, read-only interfaces.

Edge Gateway

Normalization, filtering, and buffering at the plant: resilient to WAN outages.

Streaming Backbone

Kafka pipelines carry contextualized events to the lakehouse with schema enforcement.

Lakehouse Layers

Bronze (raw), Silver (cleaned/joined), Gold (business-ready) tables on open formats.

Semantic Layer

dbt models and metric definitions for consistent KPIs across BI tools.

Governance

Catalog, lineage, access controls, and cost observability across the stack.

Engineering Stack

Technology stack

A pragmatic stack chosen for reliability, speed, and ease of operation.

OT Integration

OPC-UAModbusEtherNet/IPOSIsoft PIMQTT

Lakehouse

Apache IcebergDelta LakeTrinoSparkDuckDB

Streaming

KafkaKafka ConnectFlinkMaterialize

Modeling

dbtCubeSQLMesh

BI & Notebooks

GrafanaTableauPower BIJupyter

Infra

KubernetesTerraformVaultOpenTelemetry

Measured Impact

What should the platform measure?

Measures we build reporting around, agreed with you before launch.

Coverage

Plants, lines and assets connected

Tracked per site as connectors go live.

Freshness

Time from sensor to query

Measured per source and workload.

Reuse

Projects built on shared data

Instead of a new pipeline per initiative.

Cost

Storage and compute per plant

Reported per team and workload.

Use Cases

Which workloads run on it?

Common uses of a shared factory data platform.

Predictive Maintenance Data

Centralized, clean sensor data for equipment health models across plants.

Quality Analytics

Cross-plant quality comparison with contextualized process and inspection data.

OEE & Production Analytics

Consistent OEE calculation methodology across lines, plants, and regions.

Energy Analytics

Machine-level energy consumption correlated with production state.

ML Feature Store

Governed feature pipelines for any manufacturing ML workload.

Regulatory & Customer Reporting

Traceability and compliance reports from one auditable data source.

Integrations

Which systems does it connect to?

We connect to the systems your teams already know.

Historian

OSIsoft PIWonderware / AVEVA

SCADA

IgnitionRockwell FactoryTalk

ERP

SAP S/4HANA

Warehouse

Snowflake

Analytics

Databricks

BI

Tableau / Power BI

Compliance-First Development Services Backed by Global Standards

We build secure, scalable products designed for privacy, interoperability, and regulatory readiness from day one across every sector we serve.

Service Organization Control 2

Verified controls for security, availability, and confidentiality of enterprise data systems.

Information Security Management

Adhering to the international gold standard for managing information security risks.

Quality Management Systems

Ensuring consistent quality in software delivery and operational process optimization.

Our Edge

Why Global Leaders Choose Us

We combine deep technical expertise with industry-specific knowledge to deliver solutions that aren't just functional, but transformational.

Enterprise-Grade Security

We implement rigorous security protocols and compliance standards (HIPAA, GDPR, SOC2) across all industrial solutions to protect sensitive data.

High-Performance Scaling

Our architectures are built to handle massive data loads and user bases, keeping performance steady whether you're serving ten or ten million.

Accelerated Time-to-Market

Using our suite of internal tools and proven frameworks, we cut setup work so your first release reaches users sooner.

Embedded AI Integration

Beyond simple wrappers, we build deep-learning integrations and predictive analytics directly into the core of your industry-specific workflows.

Engagement Model

How does an engagement work?

A scoping call, a fixed-scope first release, then scale and support.

Scoping call

We map your plants, PLCs, historians, MES and the OT network rules at each site.

Fixed-scope first release

One line's data, from edge connector to one dashboard, working end to end.

More plants and workloads

Further sites and analytics workloads added on the same platform.

Scale and support

Our team supports the connectors, pipelines and data model as plants change.

Facts and sources

Which standards shape an industrial data platform?

Short facts we design around, each linked to its primary source.

NIST SP 800-82 Rev. 3, Guide to Operational Technology (OT) Security, published in September 2023, gives guidance on securing OT such as industrial control systems while addressing their performance, reliability and safety requirements.
Source: NIST SP 800-82 Rev. 3 (NIST CSRC)
The ISA/IEC 62443 series of standards defines requirements and processes for implementing and maintaining electronically secure industrial automation and control systems (IACS).
Source: ISA, ISA/IEC 62443 Series of Standards
MQTT 5.0, an OASIS Standard, is a lightweight client server publish/subscribe messaging protocol suited to constrained machine to machine and Internet of Things environments.
Source: OASIS, MQTT Version 5.0

Voice of success

We don't just build products; we forge lasting partnerships. See how we've helped industry leaders transform their vision into technical reality.

“I can clearly see how Agnotic has a unique way of handling end-to-end development. They are always active on quick chat and provide support quickly.”

Aaron Phelan

Founder, Benchmark

“Agnotic combines deep technical expertise with strong domain knowledge. They understand the business context, anticipate challenges, and make collaboration smooth and effective.”

John Pasmore

Founder, Latimer

“Agnotic is the best technical team we evaluated. Their engineering excellence made our work dramatically easier and allowed us to stay focused on what matters most for maternal care outcomes. They took full ownership of the technical execution, and we are always happy to continue working together.”

Kim Smith

Founder, My Lauren

The Same Engineering Patterns, Across Other Industries

The technical patterns behind this platform translate naturally into adjacent verticals.

Energy

Apply the same predictive analytics and IoT patterns to grid and renewable assets.

Transportation

Extend supply chain visibility into fleet, route, and last-mile orchestration.

Robotics

Pair smart factories with autonomous mobile vehicles and cobot fleets.

Frequently Asked Questions

No. PI and Wonderware remain excellent OT historians. We mirror their data into the lakehouse read-only for analytics, leaving the OT estate untouched.