The latest product iterations, standard integrations, and engineering milestones for OSCAR.
To support international teams, we rolled out an edge-storage multilingual translation system, allowing users to seamlessly toggle the interface between English, German, and Simplified Chinese without losing deep technical context. Additionally, we overhauled our intake architecture, deploying a seamless, background-fetch request system. Teams exploring Automotive Pilots or Railway/Defence Sandboxes can now submit technical inquiries friction-free, triggering immediate internal routing.
Compliance overhead is a massive hidden cost in engineering. To help management visualize this, we developed and integrated an interactive Engineering Budget Recovery Calculator directly into our platform. Based on Algomotive’s internal OEM cost-modelling, users can now accurately project reclaimed engineering hours and the reduction in external assessor fees. Alongside this, we deployed a significant UI overhaul to make navigating OSCAR's deployment options and sample reports more intuitive.
As pilot users began uploading massive system architecture documents, we realized standard context windows were insufficient. We upgraded our domain-trained encoder stack to support a 32,000 token context window. Furthermore, we finalized our Stage 06 output layer. OSCAR now generates heavily structured, audit-ready PDF reports and machine-readable JSON exports, drastically reducing the time needed to compile external assessor evidence packs.
A massive update to our standards ontology. OSCAR’s knowledge base now fully incorporates all 12 parts of ISO 26262:2018, as well as ISO 21448 (SOTIF). To support this, we upgraded our ingestion pipeline to handle unstructured data, integrating Apache Tika and advanced OCR to parse complex PDF and DOCX exports. The engine can now trace ASIL rationale and flag missing functional safety requirements directly to specific standard clauses.
Listening to strict IT security requirements from our Tier-1 and OEM pilot partners, we completely overhauled OSCAR’s deployment infrastructure. As of this month, OSCAR can be deployed entirely on-premise behind corporate firewalls. We ensured that the inference engine and vector database operate completely locally, meaning zero outbound network calls are made during artifact assessment. Proprietary safety goals and architectures never leave the client's perimeter.
We officially locked in the core RAG (Retrieval-Augmented Generation) architecture for the OSCAR engine. Our primary focus was eliminating the manual mapping of systems engineering artifacts. In this release, we successfully deployed our custom parsers for SysML and ReqIF formats. By mapping these directly against our newly verified ASPICE PAM 3.1 ontology, the engine can now automatically detect gaps in base practices and output capability level scores deterministically, with zero generic LLM hallucination.