Generative AI Development Company

A real generative AI development company ships products that work in production, not demos. The markers: named client references with quantifiable outcomes, production deployments (not pilots), architecture decisions that go beyond chaining OpenAI API calls, RAG pipelines with accuracy evaluation, fine-tuning experience on domain-specific data, and engineers who have debugged latency, hallucination, and reliability at scale. An API wrapper shop connects your prompt to an LLM, wraps it in a UI, and calls it done. The difference shows up the first time something breaks in production. We've shipped 25+ GenAI products end to end. We can name the clients. We can show you what they measure.

We work with GPT-4o (OpenAI), Claude 3.5 Sonnet and Claude 3 Opus (Anthropic), Gemini 1.5 Pro (Google), Llama 3 (Meta), and Mistral. Model selection depends on your task, cost, latency, context window requirements, and data privacy constraints. For data that cannot leave your infrastructure, we deploy Llama 3 or Mistral on your own servers. For regulated data with compliance requirements, we use Azure OpenAI or AWS Bedrock, both of which offer HIPAA BAAs and data residency controls. We're model-agnostic. We pick what fits your problem, not what's easiest for us.

RAG is the right choice when your knowledge base changes frequently, when accuracy and citations matter, when you need the model to answer from specific documents rather than general training data, or when your knowledge base is too large to include in context. Fine-tuning is the right choice when you need to change the model's tone or domain vocabulary, when a narrow task needs higher accuracy than prompting alone achieves, or when you need a smaller, faster, cheaper model that matches a larger model's performance on a specific task. Most enterprise GenAI products we build use RAG for knowledge-grounded answers and optionally fine-tune a smaller model for speed and cost at production volume. We assess which approach fits your use case during discovery and explain the trade-offs before you commit.

A focused single-capability build (a domain-specific chatbot, a document Q&A tool, a single-agent automation) typically runs $30,000 to $80,000 in 8 to 12 weeks. A multi-capability AI product (RAG plus agents plus a user-facing interface) typically runs $80,000 to $150,000 in 12 to 16 weeks. Projects requiring custom fine-tuning and enterprise deployment infrastructure run $150,000 to $200,000 and beyond. We give fixed-cost quotes for well-scoped projects. You know the number before we start.

A working prototype takes 2 to 3 weeks. A production-ready single-capability product takes 8 to 12 weeks. A multi-capability product typically takes 12 to 16 weeks. Timeline is driven by data complexity, number of integrations, whether fine-tuning is in scope, and compliance requirements. We scope the project before quoting so the timeline you see in the proposal is the timeline you hold us to, not an estimate that grows.

Production-grade means the system performs reliably for real users at real volume, not just in a controlled demo. Specifically: retrieval accuracy measured against a held-out evaluation set before launch, not eyeballed in development. Latency tested at your expected request volume, not just single-query response time. Hallucination guardrails with fallback responses when retrieval confidence is below threshold. Authentication, audit logging, and error handling built in. Monitoring so you know when quality degrades. A codebase your team can maintain, or that we can maintain for you. Infrastructure you own and can operate without us. Demo-grade skips most of this. We don't.

You do. All application code, fine-tuned model weights, training data pipelines, vector indices, and deployment infrastructure transfer to you at project end. We don't retain IP, use proprietary frameworks that create lock-in, or require you to use our infrastructure. The handover includes documentation, a walk-through session for your team, and optional ongoing support if you want it. The project ends with you fully in control.