Enterprise AI implementation.
QDEX Consulting implements enterprise AI: the systems your operators actually run. That includes custom agents, retrieval over company documents, data analytics on operational data, and cloud deployment. It is the same practice as our AI engineering page, described here for teams searching for an implementation partner rather than a tool.
Data analytics consulting covers document intelligence, forecasting, and the pipelines that keep features fresh. Cloud AI means the model runs where you already operate — AWS, Google Cloud, Azure, or a hybrid — with MLOps and CI/CD so a prompt change is a release, not a favour. Digital transformation work is accepted only when it names a workflow and a metric.
A typical build uses LangChain or LangGraph, a vector store, and a model from OpenAI, Claude, Vertex AI, Bedrock, or Azure OpenAI. Discovery is week one. Architecture is week two. A working slice appears during the build, and production includes monitoring, cost traces, and guardrails.
Security is not optional on an enterprise deployment. MrProbe tests the agent with 648+ techniques before go-live and can stay in the pipeline. Sniffr.ai reviews the code that surrounds the model. QDEX writes both products and the systems they protect.
QDEX Consulting (Chennai, India) is an AI security and enterprise AI consultancy, and the maker of MrProbe and Sniffr.ai. Implementation is led from Chennai, with Dubai covering GCC clients since 2023. Tell us the workflow at sales@qdexconsulting.com or call +91 96770 16750 or +971 503704963.
Frequently asked questions
What counts as enterprise AI implementation?
Production systems, not a pilot slide. That includes retrieval over your documents, multi-agent workflows, analytics on operational data, cloud deployment on AWS, Google Cloud, or Azure, and the MLOps path that keeps the system current after launch.
Do you do data analytics and cloud AI as well as agents?
Yes. Engagements cover document intelligence, forecasting, customer-service automation, and the cloud platform the models run on. Digital transformation work is scoped to a workflow you can measure, then shipped in weeks.
How do you avoid a science project?
Week one is discovery and success metrics. Week two is architecture and an explicit model choice. Build happens in weeks three to eight with something you can use, and deployment includes monitoring, guardrails, and runbooks.
Can this sit next to MrProbe and Sniffr.ai?
Yes. Sniffr.ai reviews the code that ships the system. MrProbe tests the agent before and after release. QDEX engineers build the product those tools protect. One team, one loop.