AI development services built around your business

Our AI development work spans the full stack of modern applied AI: large language model (LLM) integrations, retrieval-augmented generation (RAG) pipelines, AI agents, natural language processing, computer vision, predictive analytics, and intelligent process automation. We treat each of these as engineering disciplines with their own failure modes — prompt injection, hallucination, data drift, latency, and cost — and design accordingly rather than shipping a thin wrapper around a single API call.

Every engagement begins with an assessment of your existing data, systems, and team, because the right AI architecture for a healthcare records system looks nothing like the right architecture for an e-commerce recommendation engine. We scope the model, the data pipeline, the evaluation strategy, and the human-in-the-loop checkpoints before writing production code.

We build custom LLM integrations on top of OpenAI, Anthropic Claude, and open models, including fine-tuning where it genuinely improves outcomes and retrieval-augmented generation where grounding responses in your own documents and data matters more than fine-tuning. RAG pipelines connect your knowledge base, product catalog, or internal documentation to an LLM so answers stay accurate and current without retraining a model every time your content changes.

For teams that need software to take multi-step action rather than just answer questions, we design AI agents that call tools, query APIs, and complete workflows end-to-end, with guardrails, logging, and human approval steps where the stakes call for it. See our dedicated page on AI agent development for more on agent architecture and evaluation.

Beyond generative AI, we build computer vision systems for image classification, object detection, and quality inspection; NLP pipelines for classification, extraction, and summarization of unstructured text; and predictive analytics models that turn historical data into forward-looking decisions — demand forecasting, churn prediction, and anomaly detection among them.

Our AI/ML stack includes OpenAI and Anthropic Claude for language models, AWS, Azure, and Google Cloud for infrastructure, PyTorch, TensorFlow, and Hugging Face for model development, and PostgreSQL and vector databases for retrieval and storage. On the application layer we build with React, Next.js, TypeScript, Node.js, and Python/FastAPI, and we deploy with Docker, Kubernetes, and Terraform so AI systems are reproducible and observable in production, not one-off notebooks.

Every engagement starts with a free discovery call, where we learn your goals, systems, and constraints before proposing a scope. From there we move in agile two-week sprints — discovery and strategy, architecture and design, build and iterate, then deploy and scale — so you see working software early and can redirect scope before it compounds into rework. We work in agile two-week sprints with visible progress every cycle. Many MVPs and first production releases ship in 6 to 12 weeks depending on scope; larger platforms are broken into sprint-sized milestones so you see working software early and often.