We studyhow work actually moves.
SignalAct's research process begins with real operational situations rather than generic AI use cases. Before we write a line of product copy, we try to understand where work actually breaks—and why.
We examine
We start with evidence that already exists in public, rather than assuming we know how a given team works.
- Public customer feedback
- Support and operational patterns
- Industry-specific exceptions
- Escalation paths
- Decision conditions
- Cross-team handoffs
- Existing software environments
- Common resolution delays
- Repeated management involvement
We translate findings into
Raw research doesn't help anyone by itself, so every research pass gets turned into something concrete enough to react to.
- Pain hypotheses
- Workflow maps
- Decision rules
- Action plans
- Prototype screens
- Discovery questions
- Validation priorities
We then ask industry practitioners
A hypothesis is only useful once someone who lives the problem has picked it apart. Every prototype gets tested against questions like:
- Is the situation realistic?
- What context is missing?
- Which team should own it?
- Which actions are unnecessary?
- Which decisions need manager approval?
- Which systems should be updated?
- What would make the workflow valuable?
How this comes together
A single research cycle usually runs a few weeks: examine patterns, draft a workflow map and prototype screens, then sit down with practitioners and ask what's wrong with it. What survives that conversation becomes a concept workflow; what doesn't gets reworked or dropped. Nothing skips the practitioner check—no matter how solid the public research looks on paper.
Research findings are treated as hypotheses until validated directly.