Calvin AI Models

Overview

Calvin is built on a multi-model, multi-agent architecture, combining several types of AI technologies to deliver intelligent, context-aware e-commerce experiences. Rather than relying on a single model, Calvin orchestrates a diverse set of AI components — including LLMs, embedding models, reranking models, vector databases, and graph databases — each selected for the specific task it performs best. Within each workspace, multiple specialized agents collaborate using different models: some build, some review, and some research, working together much like a real development team.

Model Categories

Large Language Models (LLMs)

Calvin employs multiple LLMs across its platform, organized around a workspace-based architecture. Each workspace contains a team of specialized agents, and each agent is paired with the LLM best suited for its role. Some agents are dedicated builders, responsible for generating UI components, writing production-ready code, and implementing platform integrations. Others serve as reviewers, running QA checks, validating outputs against best practices, and ensuring code quality before anything reaches production. Additional agents focus on research and analysis — synthesizing user behavior data, extracting insights from product catalogs, or evaluating A/B test results. This division of labor mirrors how a real development team operates, with different specialists collaborating within the same workspace to deliver high-quality results.

This multi-model approach means Calvin is not locked into a single provider or architecture. Calvin combines in-house models developed specifically for e-commerce workflows with leading proprietary models from top AI providers. In-house models handle tasks where domain-specific optimization and data control are critical, while proprietary models are used where general-purpose reasoning and generation capabilities are needed. Each agent's model assignment is continuously evaluated and updated to ensure the best balance of performance, accuracy, and cost efficiency.

Because different agents serve different purposes, they often run on entirely different models. A developer agent tasked with generating complex React components might use a large, highly capable reasoning model, while a reviewer agent performing code linting might use a faster, more cost-efficient model optimized for pattern detection. Meanwhile, a research agent scanning competitor pricing strategies could leverage a model fine-tuned for data extraction and summarization. This flexibility allows each workspace to maintain an optimal balance of speed, intelligence, and cost — tailored to the exact mix of tasks at hand.

Embedding Models

Calvin uses embedding models to transform product catalogs, user queries, and behavioral signals into rich vector representations. These embeddings power semantic search, product discovery, and recommendation systems — enabling Calvin to understand intent rather than just match keywords.

Reranking Models

After initial retrieval, reranking models refine and reorder results based on deeper contextual understanding. This ensures that the most relevant products, content, or actions surface first, improving conversion and user satisfaction.

Vector Databases

Calvin stores and queries vector embeddings through high-performance vector databases, enabling real-time similarity search and retrieval at scale. This infrastructure supports fast, accurate results even across large and complex product catalogs.

Graph Databases for Memory Management

In addition to vector databases, Calvin leverages graph databases to manage long-term memory and contextual relationships across workspaces. Graph databases store structured knowledge as interconnected nodes and edges, allowing Calvin's agents to maintain rich, persistent memory of past interactions, project history, user preferences, and domain-specific relationships. This graph-based memory layer enables agents to recall prior decisions, understand how different components relate to each other, and carry context across sessions — making each workspace smarter over time. By combining vector search for semantic retrieval with graph structures for relational reasoning, Calvin achieves a deeper level of contextual intelligence that goes beyond simple pattern matching.

Security and Data Privacy

All AI models used by Calvin operate within a secure, isolated network inside AWS. Specifically:

• All model inference runs within the same Virtual Private Cloud (VPC), ensuring that data never leaves the secure network boundary.

• No customer data is sent to external model providers for training purposes.

• Data sovereignty is maintained at every layer — from inference to storage.

This architecture is designed to meet enterprise-grade security requirements while still leveraging the latest advances in AI.

E-Commerce Native Intelligence

Calvin's models are not general-purpose AI adapted for e-commerce — they are built from the ground up with deep e-commerce domain knowledge. This means every model in the stack understands the language, patterns, and nuances of online retail: from product taxonomies and catalog structures to conversion optimization and merchandising strategies.

Beyond domain knowledge, Calvin's models are equipped with the right tools, up-to-date documentation, and context needed to not just analyze but actually build. Agents understand how to construct UI components, implement platform integrations, generate production-ready code, and work within real e-commerce frameworks. This is achieved through extensive fine-tuning, curated knowledge bases, and purpose-built toolchains that keep models aligned with current best practices, platform APIs, and design patterns.

The result is an AI that doesn't just suggest — it executes with the precision of a team that has shipped hundreds of e-commerce projects.

Why a Multi-Model Approach?

No single AI model excels at every task. By combining specialized models across different domains — language understanding, semantic search, ranking, and generation — Calvin achieves a level of performance and flexibility that a monolithic approach simply cannot match. This also allows Calvin to evolve rapidly, adopting new models and techniques as the field advances without disrupting existing workflows. Combined with per-workspace agent teams — where developers, reviewers, and analysts each operate on the model best suited to their task — Calvin delivers a truly collaborative AI experience that scales with your business.