Build Custom Generative AI Applications
Turn large language models into real products. We build RAG systems, content generation engines, and AI copilots grounded in your data, using GPT-4, Claude, Llama, and Stable Diffusion.
Generative AI Capabilities We Deliver
From idea to production, we handle model selection, data pipelines, deployment, and monitoring.
Text & Content Generation
Automated copywriting, summarization, translation, email drafting, and report generation tuned to your brand voice.
RAG & Knowledge Systems
Retrieval-augmented generation over your private documents and databases for accurate, grounded answers.
Image & Media Generation
Product visuals, marketing creatives, and design variations with Stable Diffusion, FLUX, and DALL·E.
Code Generation & Copilots
Custom developer copilots, code review assistants, and internal tooling that understands your codebase.
AI Agents & Automation
Multi-step autonomous agents that plan, call tools, and complete tasks, built with LangChain and function calling.
Fine-Tuning & Customization
Domain-adapted models via fine-tuning and instruction tuning for your specialized vocabulary and tasks.
Generative AI Use Cases
Our GenAI Development Process
Discovery
Define the use case, success metrics, and data sources available
Model & Architecture
Select models, design the RAG/agent pipeline, and plan guardrails
Build & Evaluate
Develop the app, run evaluation sets, and tune prompts for quality
Deploy & Monitor
Ship to production with logging, cost tracking, and continuous evaluation
Generative AI Tech Stack
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Frequently Asked Questions
Generative AI development is the process of building applications powered by large language models (LLMs) and diffusion models that can create new content such as text, images, code, audio, and structured data. Unlike traditional software that follows fixed rules, generative AI produces original, context-aware output. We build production systems on top of models like GPT-4, Claude, Llama, Mistral, and Stable Diffusion.
We work with both commercial and open-source models: OpenAI (GPT-4o, GPT-4), Anthropic Claude, Google Gemini, Meta Llama 3, Mistral, and Stable Diffusion / FLUX for images. We help you choose between hosted APIs (faster to ship) and self-hosted open models (better for data privacy and cost at scale), then build the surrounding application.
Yes. Retrieval-Augmented Generation (RAG) is one of our core offerings. We use a vector database (Pinecone, Weaviate, pgvector) to connect an LLM to your private knowledge base: documents, databases, support tickets, product catalogues. The model then answers questions grounded in your actual data, dramatically reducing hallucinations and keeping responses accurate and current.
We use a combination of RAG grounding, prompt engineering, structured output validation, guardrails (content filters and schema enforcement), and human-in-the-loop review for sensitive use cases. For high-stakes applications we add evaluation pipelines that score outputs against a test set before every deployment.
A focused proof-of-concept (e.g. a RAG chatbot over your docs) can be delivered in 2–4 weeks. A production-grade application with authentication, monitoring, evaluation, and scaling typically takes 8–16 weeks depending on complexity and integration requirements.
Yes. For privacy-sensitive workloads we deploy open-source models in your own cloud (AWS/GCP/Azure) or via enterprise API tiers that contractually exclude your data from model training. We sign NDAs, implement data-access controls, and can build fully on-premise solutions where regulation requires it.
Ready to Build with Generative AI?
Book a free consultation. We will map your use case to the right models and architecture, and show you a realistic roadmap to production.