Workflow and tool design
Map the task, decisions, integrations, tool permissions, and human handoffs before adding autonomy.
Agentic workflow engineering
An agent is not just a model response. It needs the right context, bounded tools, clear state, a way to recover from failure, and an interface that tells a person what happened. We design those parts together, including tool access through MCP (Model Context Protocol) and evals that run before each release.
LLM application development
Build the product and backend surfaces around retrieval, structured outputs, tool calls, queues, and external systems.
Evaluation and operation
Define expected behavior as evals, inspect failures, monitor quality and cost, and improve the workflow with evidence.
Where we focus
We help teams choose an appropriate level of automation, make sensitive actions reviewable, and avoid promising more autonomy than the product can safely support. The result should be understandable to its users and maintainable by its engineers. Agents can also research companies from public sources, such as markets, funding, hiring, and technology signals, and prepare account briefs and company lists for a sales team to review.