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.
What You Get
- AI feature integration into existing product
- End-to-end RAG system with custom knowledge base
- Deployed AI agent with tool integrations
- Custom fine-tuned model with evaluation metrics
- AI-native SaaS MVP (4–8 weeks)
- API endpoint for AI service with docs
What We Deliver
Core capabilities
End-to-end Generative AI product development: LLM integration, RAG pipelines, AI agents, fine-tuning, and AI-native SaaS platforms.
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.
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.
Technology Stack
Tools & technologies
Production-proven stack for every project we ship.
Use Cases
Common projects we deliver
- 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
Deliverables
Concrete outputs from every engagement
- AI feature integration into existing product
- End-to-end RAG system with custom knowledge base
- Deployed AI agent with tool integrations
- Custom fine-tuned model with evaluation metrics
- AI-native SaaS MVP (4–8 weeks)
- API endpoint for AI service with docs
- Monitoring dashboard for AI cost and quality
FAQ
Common questions about Generative AI Development
What AI models does Code Huddle have experience with?
We have production experience with OpenAI GPT-4o, GPT-4-turbo, Anthropic Claude 3.5 Sonnet and Opus, Google Gemini 1.5 Pro and Flash, Meta Llama 3 (8B and 70B), Mistral Large, and custom fine-tuned models. We select the best model for your specific use case and budget.
How long does it take to build a Generative AI feature?
A focused AI feature integration (e.g., adding a chatbot or RAG-powered search to an existing product) typically takes 2–6 weeks. A full AI-native SaaS product from scratch takes 2–4 months. We deliver iteratively so you see working AI in the first 2 weeks.
Do you build RAG systems for private data?
Yes — RAG (Retrieval-Augmented Generation) is one of our specialties. We build secure pipelines that ingest your private documents (PDFs, databases, web content), create vector embeddings, and serve grounded AI responses without leaking data to external model providers.
Can you integrate AI into our existing software product?
Absolutely. We regularly add AI capabilities to existing React/Next.js web apps, mobile apps, and APIs. We handle the AI backend, prompt engineering, UI components, and cost management as a self-contained feature addition.
How do you manage AI API costs in production?
We implement caching strategies (semantic caching with Redis), model routing (using cheaper models for simpler tasks), streaming to reduce time-to-first-token, and usage monitoring with budget alerts. We typically reduce AI costs by 40–70% vs. naive implementations.
Get Started
Ready to start your
next project?
Get a free 30-minute consultation. We'll scope your project, answer every question, and give you a realistic estimate — no strings attached.