AI Engineering
Discover insights and updates from Vector Institute’s AI Engineering team as they translate cutting-edge research into practical applications, develop open source tools, and tackle real-world AI implementation challenges. Our engineering blog showcases the technical work that bridges academic discovery with industry-ready solutions.
From research to real-world application
Vector’s AI Engineering team transforms breakthrough research into practical tools, frameworks, and solutions that organizations can implement. These insights provide behind-the-scenes looks at technical challenges, innovative solutions, and the engineering approaches that make AI research actionable across industries.
Latest AI Engineering updates
AI Systems: evaluation, modeling & benchmarking
Vector’s AI Engineering team has developed tools, including a framework that puts the principles into practice to guide the integration of AI into products. Check out some of our other open source solutions and resources below for both AI researchers and practitioners across sectors.
UnBias-Plus
UnBias-Plus is an open source tool that detects bias in text. Feed it any piece of text and it identifies biased phrases, explains why they’re problematic and returns a neutral rewrite, all as a clean, structured output ready to plug into any workflow. Whether you’re reviewing content before publication, sanitizing data before training or building evaluation pipelines or studying language at scale, every result is auditable and transparent.
How to leverage UnBias-Plus
Newsrooms and editors
Run pre-publication checks that flag loaded framing, sensationalism and politically charged language at the phrase level – so editorial decisions are grounded in evidence, not instinct.
Researchers and educators
Triage user-generated content with structured, machine-readable signals – bias types, segment offsets and rationales – instead of opaque scores that leave teams without clear next steps.
Trust and safety teams
Build bias free datasets, study framing effects or bring media literacy to life with concrete, reasoning-backed examples that go well beyond surface-level labels.
ML and NLP teams
Integrate a reproducible bias-analysis stage post-deployment , bias analysis RAG content systems or LLM output guardrails, delivering consistent results without disrupting existing workflows.
Stay updated on AI Engineering
Get the latest technical insights, engineering updates, implementation stories, and open source releases from Vector’s AI Engineering team.