The technologies behind every AI system we build.
From frontier foundation models to the evaluation frameworks that keep them honest, this is the AI stack our engineers use in active client engagements.
From frontier foundation models to the evaluation frameworks that keep them honest, this is the AI stack our engineers use in active client engagements.
Our AI practice is built on a combination of leading foundation model providers, open-weight models for privacy-sensitive deployments, and the orchestration and evaluation frameworks that make AI systems reliable in production. We are model-agnostic by design, selecting the right model for each use case rather than defaulting to a single provider.
We work across every major foundation model provider, from Anthropic Claude and OpenAI's GPT and o-series to Google Gemini, so model selection is driven by the use case rather than a single vendor relationship. For deployments where data residency or cost rules out a frontier API, we run open-weight models like Meta Llama and Mistral, accessed directly or through managed gateways such as AWS Bedrock and Azure OpenAI Service.
LangChain and LangGraph, LlamaIndex, and Microsoft's AutoGen form the backbone of most multi-step agent systems we build, with CrewAI and Semantic Kernel used where a lighter orchestration layer fits better. We adopted the Model Context Protocol early because it standardises how agents connect to tools and data sources, and we use Instructor and DSPy to keep model outputs structured and reliable.
Pinecone, Weaviate, and Qdrant handle retrieval for our larger RAG deployments, while pgvector is our default when a client already runs PostgreSQL and doesn't need a dedicated vector database. Chroma and FAISS cover local and prototyping workloads, and Milvus and Redis Vector come into play for retrieval at higher scale or lower latency.
PyTorch is our default for custom model development, with TensorFlow and Hugging Face Transformers used where a client's existing pipeline calls for it. Scikit-learn and XGBoost cover classical ML and tabular data problems, and ONNX Runtime and TensorRT get models running efficiently at inference time, on servers or at the edge.
MLflow and Weights & Biases track experiments and model versions across every project, and Kubeflow Pipelines or cloud-native services like SageMaker and Vertex AI Pipelines handle orchestration once a model is ready to scale. DVC keeps training data versioned alongside code, and BentoML or Seldon Core package models for reliable, repeatable deployment.
Langfuse gives us visibility into LLM application behaviour in production, and RAGAS and DeepEval quantify whether a RAG system's answers are actually grounded and correct. Guardrails AI and NVIDIA NeMo Guardrails enforce output boundaries, and Evidently AI and Arize AI catch drift in classical ML models before it affects business outcomes.
Orchestration, evaluation, and observability infrastructure are just as consequential as which model you pick.
LLMs, RAG, and agents sit alongside custom predictive models, computer vision, and NLP under one team.
Self-hosted models for data that can't leave your environment, frontier APIs when capability matters more.
With model routing and fallback logic for reliability and cost control
From document ingestion through vector retrieval to grounded, cited responses
Using LangGraph, AutoGen, and MCP for multi-step, multi-tool autonomous systems
With PyTorch and Scikit-learn, full MLOps pipelines in MLflow or Kubeflow
Deployed on GPU infrastructure or edge devices using YOLO, EfficientDet, and Vision Transformers
Using Langfuse, RAGAS, and Evidently to keep AI systems accurate and cost-efficient after deployment