Neural Forge AI — Applied AI Studio
Neural Forge AI is a Canadian applied-AI studio on Bay Street that designs, trains and deploys custom machine learning models, generative-AI systems and MLOps pipelines for organizations who need production-grade output — not another proof-of-concept that rusts in a notebook. We are a professional build studio and AI consultancy. We are not a course, not an income scheme, and not a data broker harvesting your corpus for resale.
What we forge
Neural Forge AI serves Canadian businesses, SMEs, scale-ups and enterprise teams who need models that survive contact with real data. We work across AI strategy, predictive ML, retrieval-augmented generation (RAG), fine-tuning, evaluation harnesses and production deployment — always with human-in-the-loop review where stakes are high. "Forge" is how we describe the discipline: frame the problem in alloy-grade clarity, prototype fast, temper outputs under load, then ship with MLOps that does not crumble the first time upstream data shifts.
Most teams we meet carry half-finished experiments — a fine-tuned checkpoint nobody trusts, a RAG pipeline that cites the wrong paragraph, a dashboard that reports accuracy on a validation set from last quarter. Our job is to take that raw ingot of intent and turn it into something your operators can rely on. We quote project fees in CAD, scope honestly, and tell you when the crucible is too thin for the heat you are asking for.
We sit at 199 Bay Street in Toronto's Financial District and work with clients across the Canadian market. Every engagement keeps senior ML engineers on the bench from discovery sprint through launch — the people who scope your build are the people who quench it under production traffic.
Figures reflect past project scope — not a promise of timeline, cost or outcome for your engagement.
How we work
We favour working prototypes over architecture diagrams that never compile. A discovery sprint produces a written roadmap, CAD project fees and a measurable definition of done — never guaranteed ROI, always explicit about data gaps and model limits.
Fixed-scope builds suit defined outcomes: a churn model, a document Q&A assistant, a forecasting pipeline with stated error bounds. Retainers suit teams who need standing MLOps, model evaluation and prompt refinement as upstream systems change. We integrate via API with the stack you already run; tool choice follows your security and residency requirements.
Senior engineers pair with your data team. We document every transform, hand over runbooks, and leave you with evaluation harnesses your staff can rerun without calling us for every drift event — though many clients keep us on retainer because production models are living things.
About the studio
Build session at the forge console — baselines reviewed before tempering begins.
Capabilities
Selected work
Past client engagements — not promises of future performance. Metrics are illustrative.
A precision parts manufacturer had a computer vision prototype that cried wolf on every glare shift. We reframed the labelling strategy, retrained with domain-augmented data and shipped an evaluation harness operators could override without guilt. Past metrics showed fewer false stops on two lines; your floor conditions will differ.
Read the case →Claims adjusters searched twelve PDF versions for the same clause. We built a RAG assistant with source citations, access control by role and human review on low-confidence retrievals. Fine-tuning was deferred until retrieval quality justified the cost — a decision we document, not hide.
Read the case →
Tell us what you trained, what broke in production, or what never left the notebook. We will scope a discovery sprint, quote CAD fees honestly, and forge a proof of concept you can pressure-test before committing to full deployment.