ThredCloud is transforming the way leading manufactures leverage OT data.

As production targets get more challenging and margins get thinner, understanding out how to extract every last ounce of value out of your production facility becomes increasingly important. Learn how different manufacturers are using ThredCloud to solve different problems.

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User asking Claude what happened last night and report

Turn messy shift handovers into a one‑question briefing.

Mid‑sized plants with small teams and no data department can use ThredCloud to turn messy, verbal shift handovers into a single, reliable source of truth for “What happened on the last shift.” By connecting Ignition, pulling in existing P&IDs, and defining key equipment relationships, you create a knowledge graph that understands how your plant fits together, without a long IT project or new infrastructure.

Once that graph is live, supervisors can ask natural‑language questions such as “What happened overnight?” or “What should I worry about at start‑up?” and get a concise summary: a timeline of key events, the top issues, likely root causes, and practical recommendations. Over time, you should expect investigation time per incident to drop from “half an hour on the phone and in the logs” to a few minutes, shift reports to become consistently available and searchable, and more troubleshooting to be handled by your own team rather than external consultants. The effect is calmer mornings, fewer surprises, and a path to paying for the system within the first few weeks of real use.

Add an industrial knowledge layer to the analytics stack you already own.

Large manufacturers with established analytics stacks, data warehouses, BI dashboards, and data science teams can adopt ThredCloud as an industrial knowledge layer rather than a replacement for what they already have. ThredCloud sits between OT data sources (including Ignition) and your existing tools, building a knowledge graph that encodes assets, tags, process hierarchies, and relationships in a way machines and humans can both understand.

Your teams then access this structure through APIs and connectors they already know how to use - from Python notebooks to BI tools and custom applications. Instead of manually hunting for “the right” tags, reverse‑engineering relationships, or rebuilding plant context in every project, data scientists and engineers can query the graph directly and feed richer, better‑labeled features into their models and dashboards. As this layer becomes part of your standard stack, you should expect shorter analytics and modelling projects (because context work is done once, centrally), a noticeable lift in data science productivity, and improved model performance driven by better understanding of how the plant actually behaves. You keep your existing investments and gain a structured, reusable map of the factory that makes every downstream tool more valuable.

Laptop screen showing OT data sources to ThredCloud to existing tools

De-risk Migrations - User on laptop in oil field

Use ThredCloud to de‑risk migrations and win more projects as an integrator.

Industrial systems integrators and migration specialists can use  ThredCloud to turn SCADA and control system upgrades from risky, knowledge‑draining projects into a repeatable, low‑drama service offer. Instead of starting with half‑outdated documentation and nervous operators, you begin every migration by building a living knowledge graph of the existing plant: importing legacy P&IDs, linking tags to equipment, and capturing operator know‑how as relationships and annotations in one place.

That graph then travels with the project. As you cut over from old platforms to modern systems, ThredCloud lets you map old tags to new tags, preserve dependencies, and keep a record of “how it used to behave” right alongside the new configuration. Operators can ask questions in their existing language—“Why is Tank 3 behaving differently than before?”—and see what changed: parameter differences, historical behaviour vs current behaviour, and suggested next steps grounded in both old and new context.