Technologies
Built With the Right Technology for the System
Divine AI Systems isn't built around one vendor. We work across the AI model platforms, cloud systems, data technologies, and analytics tools that fit the problem in front of us — and we're direct with clients about why.
How It Fits Together
The same architecture, whichever technologies fill it
A production AI system is layered — from how people interact with it down to the enterprise systems it ultimately has to work with. Security, monitoring, and governance run through every layer, not bolted on afterward.
AI Platform
OpenAI
GPT-family models for reasoning, drafting, extraction, and multimodal tasks — used where broad capability and a mature tool-calling ecosystem matter most.
Explore OpenAIAnthropic
Claude models for long-context reasoning, careful instruction-following, and agentic tool use — where reliability and controllability matter as much as raw capability.
Explore AnthropicCloud Platform
Microsoft Azure
Cloud infrastructure and AI services for organizations already running on Microsoft 365, Dynamics, or Active Directory — deep native fit with existing enterprise identity and data.
Explore Microsoft AzureAWS
The broadest cloud infrastructure and AI service catalog — a strong fit when scale, flexibility, and infrastructure control matter most.
Explore AWSGoogle Cloud
Cloud infrastructure with tight data-and-AI convergence through BigQuery and Vertex AI — a strong fit when analytics and AI need to sit close together.
Explore Google CloudData Platform
Databricks
A unified lakehouse for data engineering and machine learning — a strong fit when data science, ML, and AI need to run on the same governed data.
Explore DatabricksMicrosoft Fabric
A unified analytics platform tying data engineering, data warehousing, and Power BI reporting together on one Microsoft-native foundation (OneLake).
Explore Microsoft FabricSnowflake
A cloud data warehouse built for governed, cross-cloud data sharing — a strong fit when many teams and tools need reliable access to the same data.
Explore SnowflakeFAQ
Common questions about our technology choices
No. These are technologies we design and build with on client engagements. We describe technical fit, not a formal partnership, certification, or reseller relationship — we'll always tell you plainly if we haven't worked with something before.
These are the platforms we have the deepest, most current experience with. Most enterprise AI work fits within them, but where a project calls for something else, we'll say so during scoping rather than force a fit.
By the constraints of the problem — your existing systems, your data, your team's skills, your budget, and your risk tolerance. We'll recommend a specific technology and explain the tradeoff, not default to whichever platform we prefer.
Yes — most engagements build on top of a client's existing cloud, data, and enterprise systems rather than replacing them. Technology selection for new components still follows the same fit-first approach.
Services describe the capability we build (a custom application, an integration, an automated workflow). Technologies describes the underlying platforms those capabilities are often built on.
Tell us what you're working with today
Existing cloud provider, data platform, half-finished AI pilot — whatever your starting point, we'll give you a direct read on what fits and what doesn't.