LLM Integration (GPT-4o, Claude, Gemini)
Connect your product to the world's most powerful language models. We handle prompt engineering, context management, streaming responses, rate limiting, and cost optimization.
Specialized service
Generative AI Development
We turn cutting-edge AI models into production-ready business software. From LLM integration to fully autonomous AI agents, Code Huddle builds intelligent systems that create measurable ROI.
LLM IntegrationRAG PipelinesAI AgentsCustom AI Products
Capability map
What we design, build, and improve
Code Huddle is a specialized Generative AI development company. We build production-grade AI systems for startups and enterprises — from rapid AI prototypes to fully scaled AI-native SaaS platforms. Our engineers have hands-on experience integrating OpenAI GPT-4o, Anthropic Claude 3.5, Google Gemini 1.5, and open-source models like Llama 3 and Mistral into real-world applications.
RAG Pipeline Development
Build Retrieval-Augmented Generation systems that ground AI responses in your own data. We implement document ingestion, vector databases (Pinecone, pgvector, Weaviate), semantic search, and re-ranking.
AI Agents & Multi-Agent Systems
Design and deploy autonomous AI agents using LangGraph, CrewAI, and AutoGen. From simple tool-calling agents to complex multi-agent workflows that handle end-to-end business processes.
Custom AI Chatbots
Build intelligent chatbots trained on your data and tuned to your brand voice. We deliver web, mobile, and API-accessible chatbots with memory, persona, and escalation flows.
Fine-tuning & Custom Model Training
Adapt foundation models to your domain using LoRA, QLoRA, and full fine-tuning. We handle dataset preparation, training, evaluation, and deployment to production inference endpoints.
AI-Native SaaS Product Development
Build complete AI-powered SaaS products from the ground up — combining AI capabilities with React/Next.js frontends, Node.js backends, and scalable cloud infrastructure.
Delivery model
A visible path from uncertainty to production
- 01
Frame the decision
Clarify the user outcome, commercial goal, constraints, inherited systems, and unknowns worth testing first.
- 02
Design the system
Shape the experience, architecture, integrations, release boundary, acceptance criteria, and operating model.
- 03
Ship with evidence
Deliver reviewable increments with testing, demonstrations, production telemetry, documentation, and handover.
Commercial model
Scope and cost follow uncertainty
Defined outcomes can use milestones. Evolving products are usually better served by transparent team capacity. Estimates follow discovery of workflows, integrations, constraints, and acceptance criteria.
Engineering judgment
Tradeoffs stay explicit
Build versus buy, delivery speed, operating cost, security, maintainability, migration, and technical ambition are discussed as product decisions—not hidden implementation details.
Where this fits
Common product situations
- 01AI-powered customer support chatbot
- 02Internal knowledge base search with LLMs
- 03AI sales assistant for lead qualification
- 04Document analysis and summarization tool
- 05AI code review and generation tool
- 06Personalized content recommendation engine
- 07Automated data extraction from unstructured documents
- 08Conversational BI / natural language data queries
Technology choices
Selected for the system—not the trend
The final stack follows product constraints, team capability, integration boundaries, security, scale, and long-term ownership.
OpenAI GPT-4oAnthropic Claude 3.5Google Gemini 1.5Llama 3MistralLangChainLangGraphLlamaIndexCrewAIPineconepgvectorWeaviatePythonFastAPINode.jsNext.jsAWS SageMakerHugging Face
Evidence related to Generative AI Development
Explore product stories with related architecture, workflows, and delivery decisions.
Questions before starting