Possehl Digital Services builds modern data architectures for mid‑size manufacturers and industrial service firms, consolidating siloed sources into ELT pipelines and semantic data models that support digital‑twin and predictive‑maintenance analytics. It embeds machine‑learning, NLP and generative‑AI into workflow engines and delivers custom cloud‑native full‑stack applications while modernizing legacy systems, all backed by data‑governance and cloud‑operations support.
Funding
Funding not disclosed
Founders
Product
Problem
Mid-sized industrial companies often operate with fragmented data silos, legacy IT systems, and limited analytics capabilities, which prevents them from leveraging machine learning and digital services for competitive advantage.
Solution
Possehl Digital Services helps these firms become data‑ready by designing and implementing modern data architectures that break down silos and enable reliable AI use. The company builds ELT pipelines, semantic data models, and digital‑twin representations to turn raw machine and business data into actionable assets. It integrates machine‑learning, natural‑language processing, and generative‑AI components directly into existing processes, automating routine tasks and creating new service‑based revenue streams. In parallel, Possehl delivers custom full‑stack software—cloud‑native, web and mobile applications—tailored to specific business models while modernizing legacy applications through refactoring or hybrid approaches. All solutions are packaged with blueprints, industry‑specific best practices, and ongoing cloud‑operations support to ensure rapid rollout, scalability, and long‑term data sovereignty for the industrial mid‑market.
Target Audience
The primary customers are mid‑size manufacturers, machine‑tool builders, and industrial service providers that need to unlock data value, automate processes, and launch digital products.
Features
- ELT pipelines and AI‑assisted data mapping that consolidate disparate sources into a unified, queryable data lake.
- Data‑governance framework defining ownership, quality rules, and compliance controls for trustworthy AI deployments.
- Semantic data models and digital‑twin constructs that expose machine‑level context for predictive maintenance and service analytics.
- Integrated AI modules (ML, NLP, generative AI) embedded in workflow engines to automate decision points and generate insights.
- Real‑time IIoT ingestion layer using industry‑standard protocols for condition monitoring and anomaly detection.
- Custom full‑stack applications (cloud‑agnostic, web, iOS/Android) built with modern frameworks, UI/UX design, and API‑first integration.
- Legacy system modernization via code refactoring, rewriting, or hybrid wrappers to preserve functionality while reducing technical debt.
- Cloud‑native infrastructure management with Infrastructure‑as‑Code, high‑availability orchestration, and continuous security monitoring.