Close the gap between knowing AI and building with it.
Inference Lab exists to close the gap between people who know AI concepts and engineers who can actually build, deploy, and maintain AI systems — while producing research that is reproducible, publicly verifiable, and genuinely useful.
One organization. Three reinforcing tracks.
Research
Original work in low-resource NLP and speech intelligence — conducted independently and through international academic collaboration. Every release ships reproducible pipelines and a permanent DOI.
Engineering Services
AI/ML systems, LLM and RAG pipelines, computer vision, speech AI, and full-stack development for organisations that need working systems, not slide decks.
Engineering Education
A structured, deployment-focused curriculum — 6 phases, 12.5 months — from Python fundamentals to deployed AI systems. Graduates leave with real projects on GitHub, not a certificate.
Engineering discipline over hype
Real, deployed output — not demos that break the moment they leave a notebook. Every project we release has to run, be testable, and be deployable by someone else.
Evidence over branding
Reproducible pipelines, proper evaluation documentation, and a permanent DOI on every research release. We do not publish for a leaderboard number.
Output over certificates
People who train with the lab leave with systems on GitHub and models on HuggingFace — things that can be verified by any technical interviewer or collaborator.
Three reinforcing tracks
Research credibility validates education quality. Education rigor validates engineering services. Engineering services fund and inform real-world research. Each track makes the others stronger.