Enterprise // AI Factory implementation
Mihron AI designs, builds, and operationalizes enterprise AI Factories: strategy, data readiness, platform, use cases, and scale — for banks and regulated enterprises, working alongside your systems integrator.
Book a scoping call Banking AI use cases →The AI Factory model
An enterprise AI Factory is built in three layers. Most of the investment — and most of the risk of it never reaching production — sits above the hardware. Mihron AI owns the layer that turns GPU infrastructure into a running AI product.
GPU servers, networking, and storage — the physical foundation. Mihron AI designs for it, but doesn't sell or install it.
The orchestration and MLOps layer that turns racked GPUs into a usable platform.
Use-case design, agent development, and evaluation — where strategy becomes a working system.
Delivery methodology
The same framework, delivered as a phased engagement: a discovery survey, structured workshops, a written playbook, ongoing advisory, a proof of concept, and a path to production.
vision & roadmap
Vision, roadmap, governance model, build-vs-buy decisions, and the KPIs that define success before a line of code ships.
readiness & compliance
Data readiness assessment, feature store design, and the compliance, privacy, and regulatory review that has to happen before training starts.
selection & integration
Platform selection and integration: NVIDIA AI Enterprise, Run:AI, orchestration, and the MLOps pipeline that will run your models in production.
POC portfolio
The use-case portfolio, scoped and prioritized, built out as proofs of concept against real data and real workflows.
production hardening
Operational scaling, drift monitoring, GPU cluster scaling, and the security posture that keeps a production system production-grade.
Discovery to production
Week-by-week
Vision, roadmap, governance model, build-vs-buy decisions, and the KPIs the engagement will be measured against.
Data readiness assessment, feature store design, and compliance, privacy, and regulatory review specific to your industry.
Platform selection and orchestration design: NVIDIA AI Enterprise, Run:AI, container orchestration, and the shape of the MLOps pipeline.
Priority use cases move from spec to proof of concept, built and evaluated against real data.
Operational scaling, drift monitoring, GPU cluster scaling, and security hardening as the system moves from POC to production.
Financial-services AI use cases
Six use cases we bring to every banking-sector engagement as the starting menu — typically three are chosen as priority proofs of concept, scoped to your data and your regulatory environment.
Real-time transaction scoring and anomaly detection built on your existing fraud signals — designed to reduce false positives without weakening detection, with every flagged decision explainable for audit.
Document extraction and verification for know-your-customer intake, turning unstructured identity and compliance documents into structured, reviewable data, with a human-in-the-loop step by default.
Model-assisted credit scoring and underwriting support that augments existing risk models rather than replacing them, with explainability and bias review built into the evaluation framework.
Conversational AI for account and service inquiries, escalating to a human at defined trust boundaries — the same agent-architecture discipline behind Maya, applied at banking scale.
Automated generation and validation of recurring risk and regulatory reports, cutting the manual assembly work while keeping a human sign-off step in the loop.
Retrieval-grounded assistants over internal policy, procedure, and product documentation, so staff get accurate answers with citations back to the source document.
Agentic AI & AI co-workers
Most enterprises have a chatbot. Very few have agentic AI — systems that plan, use tools, and complete multi-step work across business functions with the right guardrails in place. That transition is a consulting problem before it's an engineering one; Mihron AI advises on both.
Where the transition applies
Governance & compliance
Regulated-industry delivery discipline isn't new to Mihron AI's team — it's the operating mode. Our team's enterprise data and AI delivery background includes work in the global pharmaceutical sector, and Maya, our own AI voice platform, runs in production today under PHIPA and PIPEDA — the same compliance posture we bring to model lifecycle governance on AI Factory engagements.
Systems integrator partnerships
Mihron AI is built to sit alongside a systems integrator, not compete with one. The integrator owns the hardware and software infrastructure; Mihron AI owns AI strategy, use-case development, and implementation — with a joint validated design and one unified line of project communication.
Hardware infrastructure, software platform, cluster operations, and day-two infrastructure support.
AI strategy, use-case development, agent architecture, implementation, and evaluation.
Shared architecture review, one project communication channel, aligned delivery milestones.
Need a single agent, not a full AI Factory?
See AI Agent Deployment →Proof, not a pitch
We operate our own production AI system inside a regulated Canadian industry. Delivery discipline isn't a claim on this page — it's how we already run.
What we can show you
Maya, our production AI voice platform, live walkthroughs of our delivery methodology, workshop curriculum, and playbook samples — available on request during a scoping call.
Global delivery
Workshops, advisory, and delivery run remote-first by default, with on-site available where an engagement calls for it. Mihron AI is based in Toronto and works across North America, Europe, and Asia-Pacific.
FAQ
An AI Factory is the full stack an enterprise needs to turn GPU infrastructure into production AI: hardware, a software platform (orchestration and MLOps), and the AI applications and consulting layer that builds and operates use cases. Mihron AI works in that third layer.
Yes. Mihron AI is built to partner with a systems integrator rather than replace one — the integrator owns hardware and software infrastructure, Mihron AI owns AI strategy and use-case implementation, with a joint validated design and shared project communication.
We work fluently with NVIDIA AI Enterprise, Run:AI, Kubernetes, MLOps pipelines, feature stores, and NVMe/object storage integration — the platform layer most enterprise AI Factories are built on.
A typical engagement runs a phased sequence — a discovery survey and workshops, a written playbook, ongoing advisory, a proof of concept, then a path to production. Week 1 covers vision and governance, Week 2 data readiness, Week 3 platform selection, and Week 4 onward the use-case build. Exact timelines are scoped per engagement.
Yes. Mihron AI is Toronto-based and delivers remote-first workshops and advisory worldwide, with on-site delivery available, across North America, Europe, and Asia-Pacific.
One scoping call. We'll walk through your infrastructure layer, your priority use cases, and where Mihron AI fits alongside your systems integrator.
Custom scope, priced after discovery — no published enterprise pricing on this page.