Capabilities
We build AI applications that work with your business data, tools, and people. Model selection and evaluation are part of the job; so are software, integrations, cloud infrastructure, and the review steps that make the result useful in everyday work.
We build LLM applications for tasks such as searching internal knowledge, extracting information from documents, and classifying content. We connect them to existing systems and evaluate output quality, failure cases, and usage cost using representative work, with human review where the task needs it.
We combine AI tasks such as document extraction, classification, and drafting with repeatable workflow steps such as intake, reporting, approvals, and notifications. We build in exception handling, failure reporting, and human review so your team knows when to intervene and how to recover.
We build web applications, internal tools, and APIs that bring AI into your business processes. Work can include adding an AI capability to an existing application or turning a tested prototype into a usable tool. We plan for testing, deployment, documentation, and the people who will maintain the code.
We design and review the cloud infrastructure behind AI applications and other business systems. We account for model usage costs, capacity, access, deployment, monitoring, and recovery. Recommendations explain the cost, effort, and reliability tradeoffs so you can judge which changes are worth making.
We connect business applications and data sources through APIs, webhooks, and data pipelines. We account for authentication, data mapping, retries, and failure reporting so your team can see when a connection needs attention and understand how information moves between systems.
