Six services, from strategy to production.
Each service page says who it fits, who it does not, and what we need from you to start.
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LLM application development
Custom applications on GPT, Claude, Gemini, and open weights. Internal tools through customer-facing products.
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RAG development services
Retrieval pipelines with vector stores, re-ranking, and the evaluation harness that proves they work.
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AI agent development
Multi-agent systems that execute real workflows, not chatbots that answer questions.
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LLM fine-tuning services
Domain adaptation, LoRA and QLoRA, and custom training where off-the-shelf falls short.
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Generative AI consulting
For leadership deciding where to invest, what to build against buy, and how to sequence it.
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MLOps and production deployment
CI/CD for models, monitoring, evaluation, cost control, and compliance. The work that separates a demo from a product.
Not sure where to start?
Most projects begin with one of these situations. Each service page also says when that service is the wrong choice.
- You have a workflow in mind and need the application built.LLM application development
- People need answers drawn from your own documents.RAG development services
- A repeatable process should run without someone clicking through it.AI agent development
- Prompting has hit a ceiling on one narrow task.LLM fine-tuning services
- You are still deciding what to build, buy, or skip.Generative AI consulting
- A prototype works and now has to survive real use.MLOps and production deployment
How the services fit together
Most projects use more than one service, in a rough order. The order is typical, not required, and discovery on any project tests it first.
- Consulting settles what to build, or whether to build at all, and ends with a roadmap.
- An application, a RAG system, or an agent comes next, depending on whether the job is a workflow, answers from your documents, or a process that runs on its own.
- Fine-tuning comes later, and only when testing shows that prompting and retrieval have hit a ceiling on a narrow task. Our post on RAG vs fine-tuning explains how to tell.
- Deployment and operations cover everything above once real users arrive. Monitoring, evaluation, and cost tracking start in the first build and do not wait for launch.
See how an engagement runs or where these services apply by industry.
Ready to move from AI experiments to AI in production?
A thirty-minute call. We assess the use case, name the risks, and tell you honestly whether we are the right fit.