AI & MLOps Consulting Services
We bridge the gap from AI prototype to production. Our elite pods deliver production-grade MLOps and deep performance optimization, building reliable, mission-critical AI systems.
Why Engineering Leaders Choose Janea for AI & MLOps
Engineering leaders trust Janea Systems to solve the hardest problems in the AI lifecycle: deployment, scaling, and optimization. While many can build a model, we specialize in the complex MLOps and systems engineering required to move AI from a promising PoC to a production-grade system that performs under pressure. We act as a long-term, embedded partner for your team, tackling the deep, "grey-area" technical challenges that stall AI initiatives. Our work enabling the entire PyTorch stack on Windows for Microsoft and delivering 50x faster AI for Bing Maps demonstrates our ability to engineer AI solutions that operate at a global scale.
Critical AI & MLOps Challenges
CTOs and VPs of Engineering face immense hurdles scaling AI from prototype to production. These challenges drain resources, delay roadmaps, and prevent AI investments from delivering business value.
Stalled PoC, Blocked Production
Your AI model shows promise but is stuck in the lab. The path to a scalable, resilient, and maintainable production system is unclear, blocking feature delivery and ROI.
Immature Data Infrastructure
Fragmented data pipelines and inadequate MLOps tooling create bottlenecks. This "dirty work" of data engineering consumes over 80% of your team's time, stalling model deployment and retraining cycles.
Inefficient Edge Performance
Deploying AI on edge devices is critical, but meeting strict power and latency constraints is a major challenge. Without deep optimization, on-device inference is slow, unreliable, and drains battery life.
Large Language Model (LLM) Bloat
The massive storage and memory footprint of LLM embeddings makes on-premise or edge deployments prohibitively expensive. This limits your ability to leverage powerful generative AI features without compromising on cost or accuracy.
AI Framework Integration Gaps
Your teams need to leverage modern frameworks like PyTorch and TensorFlow, but ensuring they perform optimally on your target platforms (e.g., Windows, ARM64) requires specialized, low-level engineering expertise you lack in-house.
Scaling and Reliability Bottlenecks
Your AI system struggles under heavy load, lacks low-latency performance, and is difficult to monitor. These issues prevent you from meeting enterprise-grade SLAs and delivering a reliable user experience.
Our End-to-End AI & MLOps Engineering Services
Go beyond AI talent. Our embedded engineering pods take strategic ownership of your most complex AI/ML challenges. We de-risk your roadmap, delivering production-grade AI systems.
AI Maturity Workshops
We partner with your leadership to build a pragmatic AI roadmap. Our technical discovery sessions identify high-impact use cases and leverage rapid prototyping to de-risk your investment.
Production-Grade AI & MLOps
We engineer robust MLOps infrastructure for automated deployment, monitoring, and scaling. Our solutions deliver your AI features with the velocity and reliability that mission-critical systems demand.
Applied AI & Performance
Our elite engineers provide the deep, low-level systems tuning to maximize AI performance. We optimize your entire stack, from custom hardware enablement to framework performance tuning.
Edge & Embedded AI
We deliver highly optimized on-device inference for resource-constrained environments. Our expertise in quantization and custom runtimes ensures maximum AI performance within tight power and latency budgets.
Generative AI & LLM Solutions
We engineer and optimize enterprise-ready GenAI systems. From RAG pipelines to advanced vector compression, we make large language models cost-effective, accurate, and ready for production.
Production-Grade Agentic AI Systems
We are building the next generation of autonomous AI. Our teams design and implement agents that can reason, plan, and execute complex tasks using your existing APIs.
Proven Impact
Bing Maps QAS
50x Faster AI and 7x Faster Training: Optimizing Bing Maps’ Deep Learning
PyTorch
50% Drop in Windows Issues Moves PyTorch Toward Cross-Platform Stability
NDA
Platform Integration & Infrastructure Optimization for Pharmaceutical R&D
BigFilter
From Idea to Prototype in 3 Months: A Case Study in AI Fact-Checking for BigFilter
Microsoft PowerToys
From Legacy to Windows Feature Sandbox: Reviving PowerToys as Open-Source Software
Related Blogs
Discuss Your AI Engineering Challenge
Get past the bottlenecks. Let's talk engineer-to-engineer about your AI framework challenges, data pipelines, or low-level optimization needs. We build the high-performance, production-grade AI systems that others can't. Get in touch via the contact form or at sales@janeasystems.com.
Discuss Your AI Engineering Challenge
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