Experience spans leading AI platforms, with models, services and engineering patterns selected for each use case. The stack can include multiple large language models (LLMs), retrieval-augmented generation (RAG), machine learning and neural networks.
MICROSOFT
Microsoft ecosystem
AI & LLMs: Azure AI Foundry, Azure OpenAI Service and model selection across available GPT and other supported model families.
RAG & data: Azure AI Search, embeddings, vector and hybrid search, document retrieval and grounded generation.
Engineering & ML: Azure Machine Learning, Semantic Kernel, Python, PyTorch and TensorFlow for model development, orchestration and integration.
GOOGLE
Google ecosystem
AI & LLMs: Gemini models through Vertex AI and the Google AI ecosystem, choosing models to match capability, latency and cost needs.
RAG & data: Vertex AI Search, vector search, embeddings, grounding and retrieval pipelines over enterprise knowledge.
Engineering & ML: Vertex AI, Google Cloud, TensorFlow, JAX and Python for machine learning workflows, training, evaluation and deployment.
ANTHROPIC
Anthropic ecosystem
AI & LLMs: Claude models through the Anthropic API and supported cloud platforms, applied to reasoning, coding and enterprise workflows.
RAG & integration: Claude-powered retrieval workflows using embeddings, vector databases, contextual grounding, tool use and connected knowledge sources.
Engineering & ML: Model evaluation, prompt and context engineering, Python integrations and neural-network-based ML systems alongside LLM applications.
MISTRAL AI
Mistral ecosystem
AI & LLMs: Mistral model families through the Mistral API, with model choice based on reasoning quality, coding needs, throughput, latency and cost.
RAG & data: Mistral embeddings, document understanding and retrieval pipelines that connect enterprise knowledge to grounded LLM responses.
Engineering & deployment: Mistral Studio/API, agent and tool workflows, evaluation, and open-weight model options for cloud or self-hosted deployments where licensing and infrastructure fit.
Mistral documentation
EUROPEAN MODELS
European AI ecosystem
Explore European models such as Switzerland’s open Apertus, EuroLLM, Teuken, and Aleph Alpha’s Pharia family. Compare language coverage, access, deployment options, and governance to identify the best fit.
Apertus · EuroLLM · Teuken 7B · PhariaAI
CHINESE MODELS
Chinese AI ecosystem
Evaluate models such as Qwen, DeepSeek, GLM, Kimi, and MiniMax through provider APIs and cloud platforms. Support covers model selection, retrieval-augmented generation (RAG), tool use, and evaluation for multilingual and regional requirements.
Architecture / Ownership / Autonomy
Build AI with clear ownership.
Choose AI systems your organization can understand, manage and improve. Balance business value with privacy, security, transparency, human oversight, regulatory obligations, cost and operational capacity.
Model families
GPTClaudeGeminiMistralLlamaQwenDeepSeekGLMKimiMiniMaxApertusEuroLLMTeukenPharia
Relational and document databases
PostgreSQLMySQLSQL ServerOracle DatabaseMongoDBElasticsearchOpenSearchRedis
Vector search
pgvectorQdrantWeaviateMilvusPineconeChroma
AI engineering
RAGEmbeddingsHybrid searchRerankingPrompt engineeringTool callingMCPModel evaluationGuardrailsMonitoringData governance
Establish ownership
Map data, workflows, risks and business outcomes. Define governance, access controls, evaluation criteria, human review and clear ownership of prompts, retrieval sources and AI-generated outputs.
Keep the architecture portable
Use interfaces and standard APIs that make it easier to change providers. Keep application logic, prompts, retrieval and model settings separate, so a provider change does not mean rebuilding the product.
Build on your knowledge
Develop secure RAG pipelines, curated data sources, embeddings and reusable evaluation sets. Keep enterprise knowledge and retrieval infrastructure under organizational governance.
Choose deployment freedom
Compare managed APIs with open-weight models deployed in your own cloud or on-premises environment. Consider data residency, licensing, hardware, performance, maintenance and total cost of ownership.
Validate and improve
Benchmark multiple LLMs against real tasks for quality, safety, latency and cost. Add monitoring, human review, security testing, fallback models and continuous improvement before scaling.
The goal is to make an informed choice and stay in control. There is no need to avoid every cloud provider. A hybrid setup can work well and leave room to change models or where they run as needs evolve.
These are areas of experience, not claims of vendor certification or partnership. Services and models are chosen to fit each client’s needs, availability and governance.
