Make one payment workflow easier to follow.
A proposed AI agent prototype for a focused Razorpay-related task, designed to make the next step clearer without hiding the underlying workflow.
Read the case study
A visual study of movement from uncertainty to a resolved next step.
01 / The case study
Start narrow. Make the handoff clear.
The exact job, user, data access, integrations, and success criteria are still being defined. This first build treats that uncertainty as part of the design problem.
Rather than position a broad assistant, the case study will select one concrete user and one triggering situation, then test whether a focused layer can reduce manual effort and provide clearer guidance.
02 / The opportunity
Clarity is the outcome.
The likely alternatives are dashboards, documentation, support channels, manual operations, or conventional software paths. The prototype earns its place by addressing one evidenced friction point, not by promising everything.
A focused conversational or automated layer can help a target user complete one clearly defined task with less ambiguity.
03 / The workflow
From trigger to next step.
A deliberately small sequence keeps the agent useful, measurable, and honest about its boundaries.
Choose one trigger
Start with one payment, support, reconciliation, or account task where the friction is visible.
Ground the response
Keep the agent close to the relevant workflow, payment state, and available evidence.
Escalate with care
When the prototype cannot safely help, make the next human step clear instead of guessing.
04 / Human-centered by design
Assist first. Escalate when needed.
The agent should support the person inside the workflow, not obscure it. If a request moves beyond the evidence or the prototype's boundary, a human handoff is part of a good outcome.
05 / What we test
Evidence over assumption.
Task completion
Can the target user finish the selected workflow with fewer unclear handoffs?
Response accuracy
Does the prototype stay grounded in the workflow instead of making broad AI claims?
Appropriate escalation
Does it recognize the boundary of what it knows and route the moment to a person?
Next step
Review the workflow, not a promise.
See the proposed path, the assumptions behind it, and the next validation step for a practical Razorpay-related case study.
See how it works