Data, AI and automation
AI and agentic workflows
Large language models can now search, read, extract, calculate and draft. Chained together as agentic workflows, they can take on substantial analytical work. The hard part is not getting an agent to produce something; it is making sure what it produces is complete, correct and traceable. We design and build AI workflows for research and reporting where that matters.
Questions this answers
- Which parts of our research, review or reporting work can an AI workflow take on reliably?
- How do we keep a human in control of what goes out under our name?
- How do we check that an agent’s output is complete and correct, every time?
- Can we turn a slow manual report into a repeatable, auditable pipeline?
Methods
Workflow design that starts from the decision and its evidence; retrieval over your own documents and data; structured extraction; multi-step agent pipelines with explicit checks; deterministic calculation kept separate from model-written text; automated tests and evaluation sets; and human review at defined points.