Key Ward offers a no‑code DataOps platform that automates ingestion, cleaning, and feature extraction from CAD, finite‑element, CFD, and test data for engineering teams. The system includes template‑driven machine‑learning and deep‑learning model builders, advanced analytics, and versioned data lineage, with results accessible via RESTful APIs and cloud‑hosted compute. It enables rapid predictive modeling without requiring data‑science expertise.
Funding
$1.1M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
WFounders
Product
Problem
Engineering teams often face fragmented, multi‑source CAD, finite‑element, CFD, and test data that require manual extraction, cleaning, and formatting before any analysis or AI modeling can occur. The process is time‑consuming, error‑prone, and typically demands data‑science expertise that most engineers do not possess.
Solution
Key Ward delivers a no‑code DataOps platform built specifically for engineers to automate the end‑to‑end workflow of extracting, transforming, and processing CAx and experimental data. Users can ingest raw design and simulation files, let the system automatically clean, normalize, and version the data, and then apply built‑in advanced analytics to uncover correlations and patterns. The platform provides template‑driven machine‑learning and deep‑learning model builders that generate predictive models in days rather than months, without requiring any coding or data‑science background. All pipelines and AI models are managed through a unified web interface, with results stored securely in the cloud and accessible via standard APIs for downstream integration. This accelerates design validation, reduces lead times, and lowers the total cost of AI adoption in engineering projects.
Target Audience
The primary customers are engineering teams in automotive, aerospace, heavy‑industry, and consumer‑product manufacturers who need to manage large volumes of simulation and test data and apply AI‑driven predictions without hiring data scientists.
Features
- No‑code pipeline editor that connects to CAD, FE, CFD, and test data sources and automates ingestion, cleaning, and feature extraction.
- Built‑in advanced analytics suite for statistical correlation, dependency mapping, and pattern detection on multi‑dimensional engineering data.
- Template library for rapid creation of machine‑learning, reduced‑order, and 3D deep‑learning models with auto‑tuned hyperparameters.
- Pre‑trained AI models for 1D, 2D, and 3D data that deliver near‑real‑time predictions on new simulation or test inputs.
- Cloud‑hosted processing engine with scalable compute resources and secure, role‑based access control (HIPAA‑grade encryption).
- RESTful and FHIR‑compatible APIs for exporting processed datasets and model outputs to downstream CAD tools, PLM systems, or custom applications.
- Versioned data lineage and audit trail to ensure reproducibility and compliance across the engineering workflow.