Technology Stack

We build production AI systems on enterprise-grade platforms that deliver reliability, scale, and maintainability.

Platforms and Tools

Vendor-neutral selections composed into layered, observable runtime systems.

Microsoft Azure

Cloud Infrastructure

We build on Azure's enterprise-grade cloud infrastructure for secure, scalable AI deployments with built-in compliance.

  • Global infrastructure at any scale
  • Integrated security at every layer
  • Native AI and ML services
  • Enterprise compliance built-in

AWS

Cloud Services

AWS provides the foundation for scalable, cost-effective AI deployments with the broadest set of ML and AI services.

  • Scalable compute resources
  • Comprehensive ML platform
  • Serverless deployment options
  • Pay-as-you-go pricing

LangChain

LLM Framework

We use LangChain to build robust LLM applications with structured prompts, agents, and memory management.

  • Structured prompt templates
  • Agentic workflows
  • Tool integration
  • Chain composition

OpenAI

LLM Provider

GPT-4o and GPT-4 for high-quality language understanding and generation with enterprise API monitoring.

  • GPT-4o and GPT-4
  • Function calling
  • Vision capabilities
  • Enterprise API security

Anthropic Claude

LLM Provider

Current-generation Claude models for long-context reasoning, code generation, and safety-critical workflows.

  • Latest-generation models
  • 200K+ context window
  • Superior instruction following
  • Constitutional AI safety

Pinecone

Vector Database

Pinecone provides the foundation for production RAG systems with fast, scalable vector search.

  • Millisecond search latency
  • Managed infrastructure
  • Hybrid search support
  • Metadata filtering

Weaviate

Vector Database

Open-source vector database for building scalable semantic search and RAG applications.

  • Open-source flexibility
  • GraphQL API
  • Multi-modal support
  • Self-hosted option

LlamaIndex

Data Framework

LlamaIndex helps us build production RAG systems with sophisticated data ingestion and retrieval.

  • Advanced data connectors
  • Node parsers
  • Query engines
  • Agent tools

MLflow

MLOps Platform

MLflow provides experiment tracking, model registry, and deployment for production ML systems.

  • Experiment tracking
  • Model registry
  • Model serving
  • Feature store

Docker & Kubernetes

Container Orchestration

Containerization for reproducible deployments with Kubernetes orchestration at scale.

  • Containerized deployments
  • Auto-scaling
  • Service mesh
  • CI/CD integration

Our Approach

We combine proven platforms to deliver production-ready AI systems.

Cloud

Reliable infrastructure from Azure and AWS

LLM

LangChain, LlamaIndex, OpenAI, Anthropic

Vector

Pinecone, Weaviate for semantic search

MLOps

MLflow, Docker, Kubernetes for production

Ready to Build?

Let's discuss how we can help you build production AI systems.