Revolutionizing Customer Support with AI: How CRED Uses OpenAI to Elevate the Member Experience

Customer expectations are rising fast. Members want support that is immediate, accurate, and empathetic—no matter the time of day or the complexity of their request. In this context, CRED, a members-only platform in India known for rewarding financial responsibility, has embraced an AI-first approach to reinvent how it serves its community. By building on advanced OpenAI technologies and carefully orchestrating AI-human collaboration, CRED is not only resolving issues faster but also making support feel more human.

This article explores how CRED’s AI companion, Cleo, and internal tools like Thea and Stark are improving resolution accuracy, member satisfaction, and operational efficiency—while preserving trust and safety. We also look at the design choices behind this transformation, lessons that apply to any enterprise, and how CRED’s strategy aligns with broader trends in responsible AI adoption.

Why customer support matters in fintech today

In fintech, support is more than problem-solving—it’s a brand touchpoint where trust is earned daily. Payments, credit card bills, and rewards are deeply personal. A single poor interaction can erode confidence, while a thoughtful resolution can turn a member into an advocate.

Global bodies increasingly recognise that inclusive, responsible innovation is critical for digital services to benefit everyone. The United Nations emphasizes the importance of ethical technology as part of broader goals for equitable development, and the World Health Organization highlights human oversight and transparency as essential principles for AI in sensitive contexts. While these frameworks often focus on health or public services, the principles—privacy, safety, and explainability—apply equally to financial customer support.

Inside CRED’s AI-first transformation

CRED’s shift to an AI-first operating model began with a simple premise: automate the routine, elevate the human. Under the guidance of leadership in engineering, the company designed a hybrid support stack where AI handles high-frequency queries and assists agents with context-rich suggestions—allowing humans to focus on complex cases and escalations that require judgment.

Meet Cleo: CRED’s AI conversational companion

Cleo is the centrepiece of CRED’s new support experience. Built on advanced OpenAI language models, Cleo engages members in natural conversation, provides precise answers, and executes account-level tasks when appropriate.

What Cleo does well today:

• Understands context across turns: Cleo keeps track of a member’s intent and history within the session to reduce repetition.
• Handles routine to moderately complex flows: balance queries, bill due dates, reward redemption steps, and troubleshooting for common friction points.
• Initiates secure, guided actions: when policy allows, Cleo can walk members through actions like downloading a statement or verifying a payment status.
• Escalates intelligently: if confidence dips or policy boundaries are reached, Cleo routes the conversation to a human, with a brief handoff summary.

Early outcomes have been meaningful. CRED reports substantial gains since deploying Cleo at scale, including a jump in customer satisfaction (CSAT)—measured internally as 14,000 basis points (equivalent to 140 percentage points)—and a 98% resolution accuracy for the categories Cleo is authorised to handle. While every implementation differs, this illustrates what’s possible when AI is introduced with the right guardrails and measurement.

Thea and Stark: agent co-pilots behind the scenes

AI shouldn’t just help customers—it should help agents, too. CRED’s internal tools Thea and Stark work as co-pilots for support and operations teams:

• Thea surfaces relevant knowledge and policy snippets in real time, reducing the time agents spend searching internal wikis or past tickets.
• Stark assists with operational workflows, from drafting follow-up messages to highlighting anomalies that might require supervisor review.

The result: faster responses, fewer manual steps, and more consistent policy adherence across shifts and channels.

How the technology works (at a high level)

CRED’s support stack uses large language models (LLMs) to understand intent, fetch accurate context, and generate responses that match brand tone. But the difference between a pilot and a production system lies in the scaffolding around the model.

Language models and guardrails

Under the hood, Cleo combines intent detection and policy-aware decisioning with response generation. Guardrails govern what the assistant can say and do, with explicit disallow lists, escalation triggers, and compliance rules. These choices reflect best practices seen across the OpenAI ecosystem as more enterprises adopt generative AI for operations. For context on how OpenAI’s platform is scaling across industries, see our overview of OpenAI’s evolving capabilities in 2025.

Security starts at the conversation layer. Prompt-hardening, request validation, and context isolation protect the assistant from manipulation. For a deeper dive into this topic, explore how organisations are guarding against prompt injections to keep AI-user interactions safe and trustworthy.

Data privacy, safety, and security

Trust depends on privacy. CRED’s implementation takes a conservative approach: minimal data exposure to the model, strict role-based access for tools, and encryption in transit and at rest. Sensitive operations are gated behind explicit member consent and policy-compliant verification steps.

These safeguards align with global expectations for responsible AI. Bodies like the United Nations advocate for inclusive and rights-respecting digital ecosystems, and the World Health Organization stresses transparency and human oversight—principles that are increasingly applied to fintech support as well.

Measuring impact: what changed for members and agents

AI investments only matter if they reduce wait times, improve outcomes, and make customers feel heard. CRED tracks these outcomes across clear metrics.

• Resolution accuracy: Reported at 98% for Cleo’s authorised scenarios, reflecting strong intent recognition and policy adherence.
• Customer satisfaction: CSAT scores rose significantly following Cleo’s rollout (internal measure: +14,000 bps), supported by faster answers and more consistent tone.
• Speed-to-resolution: Routine requests are resolved in seconds rather than minutes, with agent time focused on high-value escalations.
• Operational efficiency: Thea and Stark reduce handle time and training overhead by centralising guidance and automation.

Customer stories and use cases

Common scenarios where Cleo shines include:

• Payment status checks: A member asks, “Did my credit card bill go through?” Cleo confirms the transaction, explains settlement timelines, and provides a reference ID.
• Statement downloads: When a member needs a monthly statement for reimbursement, Cleo guides them to a one-tap download—no agent wait required.
• Reward redemption: Cleo explains eligible rewards and helps initiate a redemption without the member navigating multiple screens.
• Gentle escalations: For edge cases—like suspected fraud or a complex chargeback—Cleo hands off to an agent with a summary, reducing repetition and frustration.

Each of these flows turns a potentially tedious task into a brief, clear interaction. Over thousands of daily conversations, that consistency adds up.

Implementation playbook: how CRED rolled it out

Enterprises can borrow from CRED’s approach to de-risk deployment and accelerate time to value.

Phased deployment and human-in-the-loop

• Start with bounded domains: Launch in high-volume, low-risk intents first (e.g., FAQs, billing timelines) to build confidence and training data.
• Shadow mode: Let AI observe and draft responses while humans review, then progressively grant autonomy as accuracy stabilises.
• Escalation-first mindset: Make it easy for AI to defer to a human. Safety and trust outrank short-term automation rates.
• Continuous evaluation: Track intent coverage, confusion triggers, compliance exceptions, and CSAT regularly; tune prompts and policies weekly.

Training, tone, and UX design choices

• Brand voice matters: CRED tuned Cleo to be concise, respectful, and helpful—never pushy. Guardrails prevent off-brand replies.
• Explainability beats guesswork: Where possible, Cleo explains the “why” behind an answer (e.g., settlement windows) to enhance clarity.
• Transparency in capabilities: Cleo discloses limits and offers an easy path to a human for complex or sensitive issues.
• Agent co-pilots reduce training time: With tools like Thea surfacing policies in the moment, new agents become productive faster.

These patterns echo other successful deployments. For example, enterprise case studies such as BBVA’s ChatGPT Enterprise strategy demonstrate how well-structured rollouts can boost productivity while preserving trust. Similarly, smaller organisations have seen measurable gains; see how an SME streamlined operations in this ChatGPT-powered case study.

How CRED’s approach compares with other AI leaders

Across industries, leaders are converging on similar principles: human-in-the-loop oversight, robust security, and gradual expansion from low-risk to high-value domains. The banking sector’s AI playbooks underscore the need for measurable outcomes, transparent controls, and cultural alignment with risk management. As OpenAI’s enterprise capabilities evolve, more firms are adopting comparable architectures to balance innovation with governance—an industry trajectory outlined in our analysis of OpenAI’s generative AI platform in 2025.

CRED also reflects India’s accelerating AI adoption. Broader initiatives are making advanced tools more accessible to entrepreneurs and consumers alike, accelerating upskilling and digital inclusion. For context on this momentum, see how major providers are fueling free access to intelligent tools across India.

Risks, limitations, and how to avoid common pitfalls

No AI system is perfect. CRED’s experience highlights risks every enterprise should plan for—and how to mitigate them.

• Model drift and hallucinations: Keep prompts and retrieval sources fresh; require citations for policy-heavy answers. Escalate if confidence drops.
• Security threats: Harden prompts, sanitise inputs, and restrict tools. Review the latest patterns for defending against prompt injections.
• Privacy and compliance: Use minimal data by default; mask or tokenise sensitive fields. Maintain clear audit trails for escalations and actions.
• Bias and fairness: Monitor outcomes across demographics where applicable. Align with global best practices—principles echoed by the United Nations—to ensure equitable access and treatment.
• Over-automation risk: Don’t chase containment at the expense of experience. Build in fast, human handoffs.

Looking ahead: what’s next for AI-enhanced support at CRED

As models improve and tooling matures, CRED plans to expand Cleo’s capabilities across more product lines and deepen its integration with internal systems. That growth will be guided by the same principles that shaped the first phase: clarity about what the assistant can do, transparent limits, and alignment with company values around trust, security, and exceptional design.

Two vectors seem especially promising:

• Proactive support: Notifying members about potential issues (e.g., payment failures, upcoming due dates) with actionable next steps.
• Personalised guidance: Context-aware suggestions that help members get more value from CRED’s ecosystem—without compromising privacy.

If the first chapter of AI assistance was about response efficiency, the next chapter is about anticipating needs and reducing effort for the member—while keeping humans in the loop.

Conclusion

CRED’s AI-first support strategy illustrates how generative AI can revolutionize customer support when it’s deployed with care. By pairing an empathetic assistant (Cleo) with strong guardrails and human co-pilots (Thea and Stark), CRED has improved accuracy, speed, and satisfaction—without sacrificing trust. The approach is instructive: start small, measure relentlessly, prioritise safety, and expand where the member experience clearly benefits. As responsible AI practice becomes the norm across industries, this blend of technology and thoughtful design is likely to define the gold standard for support at scale.

Frequently asked questions

How does AI actually change customer support outcomes?

AI reduces wait times by resolving routine questions instantly, improves accuracy by referencing live policies, and preserves consistency across shifts. In CRED’s case, the assistant handles common intents end-to-end and escalates gracefully when necessary, resulting in higher CSAT and fewer repeated contacts.

What is Cleo, and what kinds of queries can it handle?

Cleo is CRED’s AI conversational companion built on OpenAI technologies. It can manage high-frequency, low-risk tasks such as balance checks, payment status updates, statement downloads, and reward redemption guidance. For sensitive or ambiguous cases, Cleo hands off to a human agent with context to avoid member frustration.

How should companies measure success for AI in support?

Track both quantitative and qualitative metrics: resolution accuracy, time-to-resolution, containment with satisfaction, CSAT/NPS, and policy compliance. Monitor failure modes (e.g., escalations due to low confidence), and use weekly reviews to refine prompts and guardrails. CRED reports notable gains, including a 98% resolution accuracy for authorised flows.

How do privacy and security factor into AI-driven support?

Minimise data exposure, encrypt everything in transit and at rest, and restrict tool access to only what’s needed. Implement prompt hardening and input sanitisation to prevent adversarial inputs. For an overview of emerging risks and defenses, see our guide on prompt injection safeguards. Principles promoted by the World Health Organization and the United Nations underscore transparency and human oversight—best practices that translate well to fintech support.

Will AI replace human support agents?

AI is most effective as a co-pilot. It handles routine tasks and equips agents with better context and drafts, while humans resolve complex, sensitive, and exception scenarios. CRED’s approach emphasises human-in-the-loop oversight and easy escalations, improving both member experience and agent productivity.