Cresta Product Designer Interview: Questions, Experience & Prep (2026)

Cresta builds AI products that help contact centre teams sell and serve customers better in real time. The Product Designer role sits at the intersection of complex AI workflows, agent-facing dashboards, and manager analytics. With 111 open roles at Cresta as of July 2026, the team is growing quickly, and Product Designer is among the most active hiring areas.

Candidates report a structured interview process that typically spans three to four rounds: an initial recruiter screen, a hiring manager conversation, a design exercise or portfolio review, and a final cross-functional panel. Every stage tests your ability to design for AI-assisted workflows, not just visual polish.

The broader market has 393 Product Designer openings across India right now, with Bangalore leading at 62 roles, followed by Delhi (33), Mumbai (13), Pune (4), Chennai (4), and Hyderabad (3).

Salary bands for Product Designers in India (from knok job market data, as of July 2026):

Cresta's exact compensation for India-based roles is not publicly reported in detail. Compare any offer against these bands and check Glassdoor for current data points.

Questions candidates commonly report encountering at Cresta Product Designer interviews:

1. Walk us through a product you designed end-to-end. How did you decide what to build and what to cut?
2. Cresta's AI surfaces suggestions to agents in real time. How would you design a UI that builds agent trust in AI recommendations without slowing them down?
3. How do you handle situations where an AI model's output is uncertain or wrong? What design patterns reduce user frustration?
4. Describe a time you had to simplify a very complex workflow for a non-technical user. What was your process?
5. How do you approach designing for two very different user types on the same platform, for example a frontline agent and their supervisor?
6. Tell us about a portfolio piece where data or research changed your design direction.
7. How do you collaborate with engineers when technical constraints conflict with your design vision?
8. Cresta serves enterprise customers with strict compliance needs. How does that affect your design decisions?
9. What does 'good' look like for an AI coaching product? How would you measure it?
10. Describe a design that failed or underperformed. What did you learn, and what would you do differently?
11. How do you prioritise between quick UX wins and deeper systemic redesigns when the roadmap is packed?
12. What is your process for designing onboarding experiences for complex tools with a steep learning curve?

Q: How would you design a UI that builds agent trust in AI recommendations without slowing them down?

*Situation:* At a previous company, our team shipped an in-app suggestion panel for support agents showing AI-generated reply drafts. Adoption was lower than expected because agents felt they had to verify every suggestion manually before acting on it.

*Task:* My goal was to redesign the panel so agents could act on suggestions quickly while still feeling in control.

*Action:* I ran five contextual interviews with agents during live calls to see where they hesitated. I found that agents trusted suggestions more when they could see a brief 'why' label, for example 'matches your last three successful calls on this topic'. I introduced confidence indicators using familiar colour signals, added a one-click 'edit before send' mode, and moved the full suggestion preview to a hover state so it stayed off the critical path by default.

*Result:* In a follow-up usability study, agents completed typical tasks faster and reported feeling more in control. The team shipped the redesign to the full agent base the following quarter.

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Q: Describe a time you had to simplify a complex workflow for a non-technical user.

*Situation:* I was designing a reporting dashboard for contact centre managers tracking agent performance. The original data model exposed dozens of configurable filters, which engineers valued but managers found overwhelming.

*Task:* I needed to reduce cognitive load without hiding data that managers genuinely needed for their weekly reviews.

*Action:* I interviewed eight managers across two enterprise customers to map which filters they used daily versus occasionally. I then introduced a 'quick view' layer showing the five most-used metrics by default, with a clearly labelled 'advanced filters' drawer for everything else. I also replaced freeform date pickers with plain-language presets such as 'this week' and 'last thirty days'.

*Result:* Usability testing showed managers completed their top weekly tasks in meaningfully fewer steps. Post-launch feedback showed the reporting module was the most positively rated feature in that release cycle.

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Q: Tell us about a portfolio piece where research changed your design direction.

*Situation:* I was redesigning the onboarding flow for a B2B SaaS tool. My initial concept centred on a feature tour because that was the pattern most competitors used.

*Task:* I needed to validate this approach before committing the engineering team to a full build.

*Action:* I ran moderated usability tests with six new users using a low-fidelity prototype. I found that users were not interested in features during onboarding. They wanted to complete one real task immediately to build confidence. I scrapped the tour and redesigned around a guided 'first win' flow where users completed a meaningful action within their first session.

*Result:* The revised prototype scored higher on perceived ease-of-use in follow-up tests. The shipped version showed improved early retention compared to the previous flow, based on the team's internal tracking.

For AI and trust questions, lead with the user's mental model, not the technology. Explain how you surface confidence signals, handle errors gracefully, and keep humans in control. Cresta interviewers want to see that you treat AI as a collaborator, not a decision-maker.

For portfolio walkthroughs, use this structure: problem, constraints, research method, key insight, design decision, and outcome. Do not just show screens. Cresta cares about your thinking process at least as much as the final visual.

For cross-functional collaboration questions, be specific about how you communicate design rationale to engineers and PMs. Mention the tools you use (Figma annotations, design specs, async video walkthroughs) and how you handle disagreement without stalling the team.

For metrics and impact questions, always connect design decisions to a measurable goal. If you do not have exact numbers, be honest: describe what you measured, what direction it moved, and what sample size you had. Fabricating numbers is a fast way to lose credibility with a data-driven team.

For 'how would you design X' prompts, do not jump straight to solutions. State the assumptions you are making, the users you would research first, and the constraints you would clarify before touching Figma. This signals you think like a product designer, not just a visual designer.

Cresta interviewers, based on candidates' publicly shared experiences, consistently look for four qualities.

Deep systems thinking. Cresta's product is genuinely complex. Interviewers want to see you can hold a large, interconnected system in mind and make design decisions that account for edge cases, error states, and long-term scalability, not just the happy path.

Comfort with AI as a design material. You do not need to be an ML engineer, but you should understand how probabilistic outputs behave, why AI can be wrong, and how to design interfaces that keep users informed and in control when the AI is uncertain.

Empathy for frontline workers. Contact centre agents work under pressure, often on low-end hardware, with very little time to think between calls. Designs that look elegant in Figma but create friction in a real call are a red flag. Interviewers probe whether you have done genuine contextual research with users like this.

Strong communication. Much of Cresta's work is cross-functional and asynchronous. Candidates who explain their design rationale clearly, handle pushback constructively, and adapt their style for engineers versus executives tend to move forward in the process.

Week one: Know the product. Watch any available Cresta product demos or walkthroughs. Map the core user journeys for an agent, a supervisor, and an admin. Note where AI surfaces in each journey and think about the design trade-offs involved.

Week two: Sharpen your portfolio. Pick your two or three strongest pieces and practise explaining each in under eight minutes using the problem-constraints-insight-decision-outcome structure. Make sure at least one piece involves AI or data-heavy product design work.

Week three: Practise design exercises. Candidates report that Cresta typically includes a take-home or whiteboard design exercise. Practise redesigning a B2B dashboard or an AI assistant interface under time pressure. Focus on showing your process, not visual perfection.

Week four: Research and questions. Read recent product announcements and publicly available content from Cresta. Prepare five thoughtful questions for each interview stage. Questions that show you understand the product and the user deeply will stand out more than generic ones.

On the interview day, bring a clear case study that shows your research process in full. If you are searching across many companies at the same time, knok checks 150+ job sites nightly, applies to jobs that match your resume, and messages HR for you, so you can focus on preparation rather than chasing applications.

Treating the design exercise as an art test. Cresta is a B2B AI company. Interviewers are not evaluating visual polish first. They are evaluating how you frame the problem, what assumptions you make explicit, and how you justify your decisions.

Skipping the 'why' in portfolio walkthroughs. Showing screens without explaining the research or trade-offs behind them is the most common reason candidates do not advance past the portfolio review, based on publicly shared interview feedback.

Claiming AI expertise you do not have. If you have not designed AI features before, say so and talk about how you would approach the learning curve. Interviewers at AI-first companies respect honesty more than overconfidence.

Not asking clarifying questions during the design exercise. Jumping straight into solutions without scoping the problem signals that you might do the same on the job. Ask about users, constraints, and success metrics before you start sketching.

Underestimating the B2B context. Consumer product examples are fine in a portfolio, but also show that you understand enterprise constraints such as compliance requirements, admin controls, multi-user permissions, and integration with existing tools.

Forgetting to talk about outcomes. Even if your impact was indirect or your data is limited, always close the loop on what happened after your design shipped. If you genuinely have no data, say what you would have measured and why.