We help organizations move from AI interest to production-ready solutions — building ML models, LLM applications, RAG systems, and intelligent automation connected to your enterprise data.
Most AI projects fail not because of bad technology, but because of poor problem framing, data quality issues, or the gap between prototype and production. Zhoton bridges that gap with production engineering discipline.
Our team combines data science expertise with enterprise engineering rigor. We build AI solutions that work at scale, integrate with your existing systems, and deliver measurable value — not just interesting demos.
Start a Conversation →Practical, outcome-focused engagements designed around your business — not generic toolkits.
Custom applications built on Azure OpenAI (GPT-4o) and AWS Bedrock (Claude, Llama) — intelligent chatbots, document analysis tools, semantic search, and enterprise copilots connected to your knowledge base.
Retrieval-Augmented Generation systems that connect large language models to your internal documents, databases, and APIs — enabling accurate, source-cited answers without hallucination.
Custom machine learning models for demand forecasting, anomaly detection, churn prediction, and recommendation — trained on your data and deployed to production via MLflow and Azure ML.
Automated extraction and classification of data from invoices, contracts, and forms using Azure Document Intelligence and AWS Textract — eliminating manual data entry and reducing processing time significantly.
The infrastructure for sustainable AI — model registries, automated retraining pipelines, drift monitoring, A/B testing, and governance frameworks that keep your AI reliable as data changes over time.
A structured evaluation of your data maturity, technical infrastructure, and business processes — identifying the highest-ROI AI opportunities and building a prioritized implementation roadmap.
Certified hands-on expertise across the tools that power modern enterprise IT.
Deep dive into your environment, goals, and constraints.
Architecture review and precise scoping with cost estimates.
Tailored solution with defined milestones and deliverables.
Agile delivery with weekly updates and transparent reporting.
Post-launch support, knowledge transfer, and optimization.
Yes, this is our most common starting point. We assess your data quality, identify the right use cases, and can either build and hand off solutions or operate them ongoing as a managed service.
We implement evaluation frameworks, human-in-the-loop validation, RAG architectures to ground LLMs in your data, and monitoring that detects output drift over time.
We design AI architectures with privacy as a first-class requirement — using private Azure/AWS deployments and ensuring your data never enters public model training pipelines.
A focused use case — such as an internal chatbot or document processing pipeline — typically takes 6–12 weeks from kickoff to production. More complex platforms take longer and are scoped individually.
Talk to one of our certified experts — no obligation, just a genuine conversation about what's possible for your organization.