Tagsight reads your drawing set into the registers a project runs on. It reads every tag off the P&ID and classifies the signal type to ISA 5.1 or your own standard. An engineer confirms each read before it exports to Excel, TIA Portal, Rockwell L5X, and DEXPI.
Automated tagging
Tagsight aims to automate the process of tagging and organizing content or data, which can save significant manual effort and improve consistency across large datasets.
Improved searchability
By applying structured tags and metadata, the tool can make content easier to find and retrieve, enhancing overall data discoverability and workflow efficiency.
Time savings
Automating classification tasks that would otherwise be done manually can free up team resources and reduce the time spent on repetitive organizational work.
Scalability
Tag-based automation solutions are generally well-suited for handling growing volumes of content, allowing organizations to scale without proportionally increasing labor.
Better data organization
Consistent tagging supports cleaner data structures, which can improve downstream analytics, reporting, and integration with other systems.
Tagsight reads the engineering drawing itself. It takes a P&ID set and returns the instrument index, I/O list, equipment list, line list, and valve list a project runs on, with every tag classified to the standard the drawing already uses. The read is not a black box. An engineer confirms or corrects each tag before anything exports. Most tools draw or store documents. Tagsight turns the drawing into a structured engineering record and keeps it true as revisions come in.
Because it removes the work senior engineers least want to do: reading tags off a drawing and typing them into a register by hand, then doing it again on every revision. Tagsight does that read and hands back a reviewable list in the drawing's own tag convention, whether ISA 5.1, KKS, IEC 81346, or a house standard. It exports into the formats a team already runs, from Excel to TIA Portal to Rockwell L5X, and it compares revisions so a Management of Change review shows only what changed. The engineer stays in control of every read. The best way to judge it is to run one of your own drawings through it.
Because it removes the work senior engineers least want to do: reading tags off a drawing and typing them into a register by hand, then doing it again on every revision. Tagsight does that read and hands back a reviewable list in the drawing's own tag convention, whether ISA 5.1, KKS, IEC 81346, or a house standard. It exports into the formats a team already runs, from Excel to TIA Portal to Rockwell L5X, and it compares revisions so a Management of Change review shows only what changed. The engineer stays in control of every read. The best way to judge it is to run one of your own drawings through it.
Tagsight is a cloud-hosted web application with a public REST API and an MCP server for desktop clients. The drawing reading combines computer vision and machine learning with a human review step, so every result is checked before it exports. It reads vector, scanned, and CAD-exported PDFs.
Tagsight is used by instrumentation and controls teams at EPCs, system integrators, and operators. We do not publish individual customer names.
We have collected here some useful links to help you find out if Tagsight is good.
Check the traffic stats of Tagsight on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
Check the "Domain Rating" of Tagsight on Ahrefs. The domain rating is a measure of the strength of a website's backlink profile on a scale from 0 to 100. It shows the strength of Tagsight's backlink profile compared to the other websites. In most cases a domain rating of 60+ is considered good and 70+ is considered very good.
Check the "Domain Authority" of Tagsight on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.
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