Daqstra offers an AI‑native orchestration platform that unifies hardware‑intensive test equipment into a version‑controlled system graph, allowing engineers to model devices, sensors, and configurations as reusable software assets. The platform automatically propagates calibrations, captures self‑describing measurements with full provenance, and uses built‑in observability and AI tools to surface anomalies, rank root‑cause hypotheses, and recommend safe next steps, accelerating reproducible testing for aerospace and other hardware‑focused R&D teams.
Funding
Funding not disclosed
Founders
Product
Problem
Hardware‑intensive R&D teams must manually integrate diverse sensors, DAQ instruments, and control systems for each test, then stitch together configurations and data after runs. This fragmented workflow leads to repeated setup effort, limited traceability, and difficulty pinpointing the root cause of anomalies across test campaigns.
Solution
Daqstra provides an AI‑native orchestration platform that unifies physical test hardware into a single, version‑controlled system graph. Engineers model devices, signals, and configurations as reusable software assets, enabling automatic propagation of calibration and channel settings between runs. During execution, Daqstra agents configure equipment, monitor operations, and capture every measurement with embedded units, calibration state, and provenance, creating a traceable record for each test. The platform’s observability layer streams self‑describing data to a central hub, where AI/ML tools rank root‑cause hypotheses, surface drift, and generate actionable recommendations while keeping an engineer in the loop. This end‑to‑end approach reduces manual setup, ensures reproducible test conditions, and accelerates decision‑making across multiple stands and facilities.
Target Audience
Primary customers are aerospace and other hardware‑intensive engineering teams that run complex physical test programs, including test engineers, program managers, and operations directors managing multiple stands or facilities.
Features
- System Graph: infrastructure‑as‑code model for devices, sensors, and configurations with versioning and reuse across campaigns
- Built‑in Observability: every data point includes units, calibration metadata, and full provenance for reliable cross‑run analysis
- Orchestration Runtime: deterministic edge agents execute sequences locally, enforce safety interlocks, and report status to a central hub
- AI‑driven Run Intelligence: automated anomaly ranking, root‑cause hypothesis generation, and policy‑safe command suggestions
- Fleet‑wide Hub: unified dashboard showing live status, active runs, health alerts, and drill‑down into any node across sites
- Institutional Memory: indexed, queryable archive of all runs, conditions, and outcomes to onboard new engineers quickly
- Vendor‑agnostic Integration: supports heterogeneous hardware stacks without custom re‑architecture