A Canadian applied-AI studio focused on applied research, intelligent systems, and scalable systems engineering, specializing in computer vision and medical imaging.
Deployable intelligent systems hinge on pipelines, integrations, latency, governance, and how teams operate them—not checkpoints alone. The work is systems engineering: reliability under load, clear human touchpoints where risk demands it, and measurable behavior in production.
Serving, scaling, observability, and repeatable releases matter as much as model quality. Systems need sane deployment topology, CI-driven artifacts, GPU-aware throughput where it counts, and runbooks operators can rely on—not ad hoc notebooks.
High-stakes or ambiguous outputs need review gates, escalation paths, and attribution—not fully automated black boxes. We wire models into processes where specialists stay accountable, with auditable decisions and configurable thresholds.
Field systems have latency and throughput envelopes. We align ingest, preprocessing, inference, and post-processing with operational SLOs—streaming versus batch tradeoffs, efficient batching, and hardware-conscious paths—so responsiveness is intentional, not accidental.
Imaging, clinical contexts, and regulated settings constrain storage, access patterns, and where compute may run. Architecture respects consent, least privilege, segmentation, and deployment options that match your risk profile—not one-size-fits-all cloud defaults.
Most initiatives stall between experiment and repeatable release. We connect versioning, reproducibility, validation suites, and packaging so promoted models behave like tested software—traceable deltas, gated promotion, not hand-copied weights between environments.
Operators need overlays, comparative views, and failure forensics grounded in lineage—not static exports. Thoughtful tooling ties what people see back to underlying tensors, geometries, metadata, or runs so discrepancies narrow quickly.
We focus on what ships, how it behaves under real constraints, and how it earns its place alongside existing tooling and workflows.
Four scoped ways to engage—architecture and feasibility reviews, embedded product engineering, and release-grade pipelines—with emphasis on what runs in production, not slideware.
Senior engineering embedded across inference, APIs, UX, mobile, cloud, and evaluation—shipping models inside products operators can rely on.
Close the gap from experiment to reproducible artifact: versioning, builds, rollout, telemetry, drift handling, and post-release evidence.
Ground the problem in feasibility, latency, data, infra, validation, and human workflow before funding a build. Narrow scope toward deployable increments.
Independent technical review for founders, operators, and investors—paired with senior advisory on architecture, infrastructure, validation, reproducibility, and delivery risk.
Products built, systems deployed, and production engagements delivered.
Generative AI product shipped end-to-end — solo, production-grade.
Model training to App Store, with full infrastructure and cost ownership across every layer.
US clinical AI companyFDA submission engagement
Principal ML engineering · vascular CTA · FDA submission pipeline
TensorHarmony owns the full ML pipeline—research through packaged release—inside a live FDA submission program.
Prior clinical imaging · regulated CT/MRI product line.
Representative infrastructure, tooling, and runtimes used in shipped systems—not a sponsorship wall.
Technical discovery, production engineering, and deployment-focused collaboration for intelligent systems, computer vision, and medical imaging workflows.
No obligation — just a focused technical conversation
