Company Overview
About Seeq
Seeq is an industrial analytics and collaboration platform headquartered in Seattle, Washington that enables process engineers, reliability engineers, and operations analysts at manufacturing and energy companies to investigate and extract insights from the time-series data generated by process historians, SCADA systems, and lab information management systems — without requiring data science expertise or the involvement of IT and data engineering teams for routine analytical work. The company was founded in 2013 by former OSIsoft executives with deep domain knowledge of industrial process data infrastructure, and positioned Seeq as the analytic application layer on top of industrial historians like OSIsoft PI, Aspen InfoPlus.21, and GE Proficy Historian, allowing engineers to analyze, annotate, and share findings from process data through a web-based interface that does not require specialized query languages or custom scripting.
Business Model & Competitive Advantage
Seeq's core analytical capabilities include time-series search and trend visualization, signal cleansing and interpolation, anomaly detection, frequency analysis, and statistical process control charting — all accessible through a visual workflow that engineers compose by connecting analytical steps rather than writing code. The platform's collaboration layer allows engineers to publish their analytical findings as Seeq Stories — documented analyses with embedded trend charts and annotations — that can be shared with operations management, maintenance teams, and other engineering stakeholders without requiring the recipient to have Seeq expertise to view and understand the analysis. This sharing capability transforms analytical work from individual discovery into organizational knowledge that persists beyond the engineer who performed it.
Competitive Landscape 2025–2026
Seeq has raised significant venture funding and serves clients in oil and gas, chemicals, pharmaceuticals, food and beverage, and utilities sectors where process historian data volumes are large and the engineer-to-data ratio makes it impractical for data science teams to service every analytical request. The platform integrates with AWS, Azure, and GCP for cloud deployment alongside on-premise historian connections, and has expanded its capabilities through machine learning model deployment that allows data science teams to operationalize predictive models accessible to engineers through Seeq's interface. Seeq competes with AspenTech IP.21 analytics, AVEVA PI Vision, and Cognite Data Fusion in the industrial analytics market.
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