Technology Stack
Overview
At DigitalBoutique.ai, we leverage a modern, modular technology stack engineered for speed, adaptability, and long-term scalability. Our platform combines proven cloud infrastructure, developer-friendly tools, and trusted AI frameworks to deliver performance and reliability at scale.
Every technology we choose is guided by four principles: measurable performance, seamless integration, strong open-source support, and direct alignment with client use cases.
Whether you’re a seasoned technical leader or just beginning your AI journey, our stack is designed to make advanced innovation both accessible and dependable.
Infrastructure
At DigitalBoutique.ai, our infrastructure is built on a foundation of reliability, security, and scalability. We operate primarily within the Amazon Web Services (AWS) ecosystem, ensuring that our systems are deployed, managed, and scaled on one of the world’s most trusted cloud platforms. This provides our clients with enterprise-grade security, high availability, and performance at scale.
Beyond AWS, our in-house cloud engineers are fluent in multi-cloud environments, enabling deployments across Azure, Google Cloud Platform (GCP), and DigitalOcean as project needs dictate. This flexibility is especially valuable for clients in regulated industries or those with unique compliance and infrastructure requirements.
Core Components of Our Stack
- Supabase – An open-source backend platform that powers secure authentication, real-time updates, and accelerated feature deployment for user-facing applications.
- PostgreSQL – Our database of choice for structured data, trusted for its robustness, reliability, and ability to support complex workflows.
- Vector Databases – Specialized solutions such as Pinecone, Weaviate, and Qdrant drive semantic search and contextual understanding—essential for AI systems that rely on similarity matching and retrieval.
- API Key Provisioning – Every client engagement includes dedicated keys with scoped permissions, ensuring secure and well-controlled data access.
- Internal Developer Tooling – Custom scripts, secure command-line utilities, and integrations with ClickUp and GitHub allow our teams to ship faster, debug more effectively, and uphold operational excellence.
Programming & Integrations
Our development process is designed for rapid iteration, intelligent automation, and seamless connectivity with external systems. By combining proven programming languages with modern developer tools and a robust integration layer, we deliver solutions that move from prototype to production with speed and precision.
Core Development Practices:
- Languages – We rely on Python for AI, data pipelines, and backend systems, while JavaScript/TypeScript power front-end interfaces and lightweight application logic. This combination ensures flexibility for experimentation and reliability in production.
- Developer Tools – Platforms like Replit, Cursor, Lovable, and Bolt support real-time collaboration, code generation, and rapid prototyping. These tools allow our engineers to accelerate workflows while maintaining code quality and long-term maintainability.
- Integration Layer – Through our partnership with n8n, we enable 850+ ready-to-use integrations, connecting to SaaS platforms, databases, APIs, and webhook-based services. This drastically reduces time-to-value for custom automations.
- Voice AI – For conversational interfaces, we use ElevenLabs as our primary provider for lifelike speech synthesis and supplement with specialized tools for retail and domain-specific use cases. This ensures our voice agents are natural, context-aware, and highly responsive.
Artificial Intelligence
At DigitalBoutique.ai, we take a model-agnostic approach to artificial intelligence. Our systems are designed to integrate with all major LLM providers, allowing us to benchmark, test, and select the right model for each client’s unique goals. Whether the priority is speed, precision, cost-efficiency, multilingual capabilities, or safety, we tailor model selection to maximize value and reliability.
Key AI Capabilities:
- Retrieval-Augmented Generation (RAG) – We ground AI responses in your internal data, ensuring outputs are accurate, contextually relevant, and resistant to hallucinations.
- Embedding Model Selection & Testing – Through controlled experiments, we evaluate different embedding strategies to optimize how AI systems “understand” and compare information.
- Agent Frameworks & Builders – Our AI agents extend beyond conversation. They can search, summarize, trigger workflows, and take real-time actions, all built with modular components for ongoing reuse and improvement.
- LLM Evaluation & Model Routing – Using internal scoring systems (LLM-as-a-judge) and dynamic routing, we continuously evaluate model performance and direct each request to the best option for the task.
This resource is continuously updated as our technology evolves. If you don’t see your question here, please reach out to our support team — we’ll help directly and expand this guide for future users.