BUILD

Standardized data foundation, digital twins, and runtime AI for all your production processes

While CONNECT unifies your data, Agents in BUILD make it meaningful by modeling production in real time with four scalable core schemas.

Most plant data isn’t inherently AI-ready. Even when labeled and mapped, it often can’t be related across plants or systems. Applying a consistent, standardized structure transforms that data into a powerful foundation for Industrial AI.

BUILD makes data AI-ready by linking inputs to outcomes, showing how what went into production impacts yield, quality, and efficiency. By converting all OT data into four scalable schemas, manufacturers get a consistent foundation for improvement across every line and plant.

Core Capabilities
Automation Transforms Data into Four Core Schemas
Cycles, parts, downtime, and defects — plus lines and KPIs — are configured, not coded. Fifteen years of proven tools transform and structure data into core schemas, creating standardized digital twins for any discrete or continuous production process across plants.
Data Engineering for Operations Teams
Because data structuring is delivered through configuration, agents are applied and bring powerful data engineering capabilities directly to operations teams — enabling the people who know their processes best to shape, refine, and extend data structures without relying solely on IT.
Blends Inputs and Outcomes
Agents link process parameters, materials, and machine settings (inputs) with outcomes like downtime, defects, and yield — creating the causal foundation for AI.
Streaming and Real-Time Resilience
Data is processed continuously in real time. BUILD can handle late-arriving or out-of-order data, ensuring accuracy even in messy, high-volume industrial environments.
AI-Ready Foundation
Raw plant signals are transformed into standardized models that feed directly into AI analysis, recommendations, and digital twins.
Continuous Adaptation
Models automatically update as new fields, tags, or process changes are introduced, keeping your foundation always current.
Enterprise Scale
Because every site uses the same core models, improvements proven in one plant can be applied enterprise-wide without re-engineering.
How it works

From a Unified Namespace to meaningful, AI-ready production models

1.
Extract Events from Process Data
Agents analyze process signals to detect meaningful events — product changeovers, downtimes, and new cycles or batches — so operations can be represented in real time.
2.
Blend IT and OT Data
Agents combine OT signals with IT data (serial numbers, quality records, ERP transactions) by matching timestamps or identifiers, creating a single view of production.
3.
Join Data Across Production Steps
To track material flow and understand downstream impacts, agents link data across steps using time offsets, flow rates, and constructed identifiers — overcoming the limitations of raw timestamp joins.
4.
Empower Operators with Control Plane
A simple interface and agents let operations teams (not just IT) define joins, rules, and refinements — putting data engineering into the hands of the people who know the process best.
5.
Build KPIs and Runtime Fields
With semantic modeling, KPIs and Runtime Fields are available instantly and flexibly — without reprocessing data. This allows teams to slice performance by shift, line, or plant dynamically.
6.
Scale with Templatized Pipelines
Once a pipeline is built, it can be repeated across similar lines and plants — accelerating enterprise-wide rollout without one-off engineering.
7.
Iterate Continuously
Workspaces support safe, rapid iteration. New models or rules can be tested in isolation and merged into production seamlessly, with no downtime.
Integrations & Ecosystem

BUILD works with IT and OT platforms manufacturers already trust.

Through close collaboration and partnerships, Sight Machine is deeply integrated with ecosystems from Microsoft, Siemens, Databricks, and NVIDIA, making real-time industrial data available wherever it’s needed.  
Microsoft Fabric
Runs natively as a Fabric workload, bringing standardized industrial data directly into your Fabric environment.
Pushes data beyond bronze — all the way to the gold level — so production data is available for advanced analytics, machine learning, and enterprise dashboards.
Open by Design
Structured models feed into BI tools, AI frameworks, and enterprise systems without vendor lock-in.
Supports both cloud and edge deployments, ensuring flexibility for IT and OT teams.

From raw data points 

to meaningful production models

BUILD transforms messy plant data into standardized, AI-ready models — accessible in real time, mapped to your production process, and scalable across the enterprise. It’s the essential layer that turns connected data into continuous improvement.

BUILD IN CONTEXT

BUILD is the second layer in the Sight Machine Industrial AI stack.

After CONNECT creates the Unified Namespace, STRUCTURE blends and models data to make it meaningful. By linking inputs (materials, process parameters, machine settings) to outcomes (downtime, defects, yield), and by standardizing across four core models, STRUCTURE provides the AI-ready foundation for ANALYZE and OPERATE.
Without BUILD, there’s no way to scale AI across plants — it’s the bridge between raw data and actionable intelligence.