Manufacturing Context Layer

The plant-specific context that AI agents need
to solve manufacturing’s toughest problems

Sight Machine’s Manufacturing Context Layer grounds agents in both a plant’s rules and process experts’
knowledge, so agents can deliver accurate, trustworthy analysis and recommendations.

See the Manufacturing Context Layer in action

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Plant data, AI-ready

General-purpose AI can’t reliably interpret raw industrial data
because it lacks the context to understand how plants really run.

Sight Machine ingests data from every source in the production process — controls, historians, MES, ERP, and document repositories — and then layers in SOPs, P&IDs, and line diagrams, so agents understand the plant’s structure.

Production data arrives with inconsistent names, units, structures, and meanings. Sight Machine’s data dictionary resolves that, identifying each signal and mapping it to an asset.

Drawing on 15 years of on-the-ground work in factories, Sight Machine then transforms all of that data into four core schemas: cycles, parts, downtime, and defects. The result is a semantic layer that ties every signal to an asset, every asset to a process, and every process to the KPIs a plant is measured by.

From raw data to a
record of production

Sight Machine doesn’t just group or sort data.
It transforms it into four core data schemas.

These schemas make it possible to describe what is happening in real time, and to derive new features. For example, whether a machine is down or in changeover, or how many parts it has run since the last changeover.

Cycles and parts capture what produced good output. Downtime and defects capture what didn’t.
Together they connect what happened on the line to what it cost.

A digital twin of how
the plant really runs

Sight Machine builds an operational digital twin by characterizing every asset and material flow and assembling them into lines and plants: an end-to-end model of how each step affects the next.

Plant operators name their own levels. For example, Line, Process Area, and Oven. Every event is stamped with its shift and production date, so “last shift” and “quarter to date” just work. Reference tables translate the codes machines report into the terms operators use. Each connection records how long material takes to travel between steps, so analysis can connect upstream conditions to downstream results.

KPIs defined once
and
applied consistently

KPIs are a plant’s scorecard, and a scorecard needs consistent definitions.

Running, idle, and stopped follow the same rules everywhere. Ideal throughput is calculated the same way for every shift. Part quality is determined by a fixed set of criteria. Because every KPI is linked to the same cycles, parts, downtime, and defects, every plant shares one foundation for improvement.

Built by agents,
approved by experts

Every transformation, derived feature, signal, and KPI is informed by plant operations, created by an agent, and approved by an expert.

Agents build a knowledge base from the written know-how they read: SOPs, known issues, troubleshooting steps, and lessons from past investigations. When they learn something new — like an engineer’s explanation for why a sensor reads high — they propose adding it. And after each successful analysis, agents offer to save the work as a reusable tool, so each investigation builds on the last.

Along the way, users can add context by uploading files like SOPs, line diagrams, maintenance manuals, and shift schedules. Once the context layer is built, any compatible, authorized agent can use it through MCP, with every action permissioned and logged.

A stable foundation
for every decision

Every manufacturer defines its operations differently, and rarely the way
a general-purpose AI agent was trained. Sight Machine settles those rules
once per plant, so agents start from the same definitions every time.

Accurate

Without guidance, agents fill gaps with assumptions that may not match how a plant works. The Manufacturing Context Layer replaces assumptions with the plant’s own definitions and procedures.

Consistent

Left to themselves, agents can answer the same question differently each run. With shared definitions, the same question gets the same answer, no matter who asks or when.

Efficient

Guided agents don’t re-infer the plant’s rules for every question. They take fewer steps, make fewer exploratory calls, and spend less time and money getting to the answer.